Original Research

Diabetes mellitus (DM) is a national health crisis affecting > 38 million people (11.6%) in the United States.¹ American Indian and Alaska Native (AI/AN) adults are disproportionately affected, with a prevalence of 14.5%—the highest among all racial and ethnic groups.¹ Type 2 DM (T2DM) accounts for 90% to 95% of all DM cases and is a leading cause of morbidity and mortality due to its association with cardiovascular disease, kidney failure, and other complications.²

Maintaining glycemic control is important for managing T2DM and preventing microvascular and macrovascular complications.³ The cornerstone of diabetes self-management has been patient self-monitored blood glucose (SMBG) using finger-stick glucometers.⁴ However, SMBG provides measurements from a single point in time and requires frequent, painful, and inconvenient finger pricks, leading to decreased adherence.^5,6 These limitations negatively affect patient engagement and overall glycemic control.⁷

Continuous glucose monitors (CGMs) offer real-time, continuous glucose readings and trends.⁸ CGMs improve glycemic control and reduce hypoglycemic episodes in patients who are insulin-dependent.^9,10 Flash glucose monitors, a type of CGM that requires scanning to obtain glucose readings, provide similar benefits.¹¹ Despite these demonstrated advantages, research has primarily focused on insulin-dependent populations, leaving a significant gap in understanding the effect of CGMs on patients with T2DM who are not insulin-dependent.¹²

Given the high prevalence of T2DM among AI/AN populations and the potential benefits of CGMs, this study sought to evaluate the effect of CGM use on glycemic control and other health metrics in patients with non–insulin-dependent T2DM in an AI/AN population. This focus addresses a critical knowledge gap and may inform clinical practices and policies to improve diabetes management in this high-risk group.

Methods

A retrospective observational study was conducted using deidentified electronic health records (EHRs) from 2019 to 2024 at a federally operated outpatient Indian Health Service (IHS) clinic serving an AI/AN population in the IHS Portland Area (Oregon, Washington, Idaho). The study protocol was reviewed and deemed exempt by institutional review boards at Washington State University and the Portland Area IHS.

Study Population

This study included patients diagnosed with non–insulin-dependent T2DM, had used a CGM for ≥ 1 year, and had hemoglobin A_1c (HbA_1c) measurements within 4 months prior to CGM initiation (baseline) and within ± 4 months after 1 year of CGM use. For other health metrics, including blood pressure (BP), weight, low-density lipoprotein cholesterol (LDL-C), and estimated glomerular filtration rate (eGFR), this study required measurements within 6 months before CGM initiation and within 6 months after 1 year of CGM use. The baseline HbA_1c in the dataset ranged from 5.3% to > 14%.

Patients were excluded if they used insulin during the study period, had incomplete laboratory or clinical data for the required time frame, or had < 1 year of CGM use. The dataset did not include detailed information on oral DM medications; thus, we could not report or account for the type or number of oral hypoglycemic agents used by the patients. The IHS clinical applications coordinator compiled the dataset from the EHR, identifying patients who were prescribed and received a CGM at the clinic. All patients used the Abbott Freestyle Libre CGM, the only formulary CGM available at the clinic during the study period.

A 1-year follow-up endpoint was selected for several reasons: (1) to capture potential seasonal variations in diet and activity; (2) to align with the clinic’s standard practice of annual comprehensive diabetes evaluations; and (3) to allow sufficient time for patients to adapt to CGM use and reflect any meaningful changes in glycemic control.

All patients received standard DM care according to clinic protocols, which included DM self-management education and training. Patients met with the diabetes educator at least once, during which the educator emphasized making informed decisions using CGM data, such as adjusting dietary choices and physical activity levels to manage blood glucose concentrations effectively.

A total of 302 patients were initially identified. After applying exclusion criteria, 132 were excluded due to insulin use, and 77 were excluded due to incomplete HbA_1c data within the specified time frames (Figure 1). The final sample included 93 patients.

Measures

The primary outcome was the change in HbA_1c levels from baseline to 1 year after CGM initiation. Secondary outcomes included changes in weight, systolic and diastolic BP, LDL-C concentrations, and eGFR. For the primary outcome, HbA_1c values were collected within a grace period of ± 4 months from the baseline and 1-year time points. The laboratory’s upper reporting limit for HbA_1c was 14%; values reported as “> 14%” were recorded as 14.1% for data analysis, although the actual values could have been higher.

For secondary outcomes, data were included if measurements were obtained within ± 6 months of the baseline and 1-year time points. Patients who did not have measurements within these time frames for specific metrics were excluded from secondary outcome analysis but remained in the overall study if they met the criteria for HbA_1c and CGM use.

Statistical Analysis

Statistical analysis was performed using R statistical software version 4.4.2. Paired t tests were conducted to compare baseline and 1-year follow- up measurements for variables with parametric distributions. Wilcoxon signed-rank test was used for nonparametric data. A linear regression analysis was conducted to examine the relationship between baseline HbA_1c levels and the change in HbA_1c after 1 year of CGM use. Differences were considered significant at P < .05 set a priori. To guide future research, a posthoc power analysis was performed using Cohen’s d to estimate the required sample sizes for detecting significant effects, assuming a similar population.

Results

The study included 93 patients, with a mean (SD) age of 55 (13) years (range, 29-83 years). Of the participants, 56 were female (60%) and 37 were male (40%). All participants were identified as AI/AN and had non–insulin-dependent T2DM.

Primary Outcomes

A significant reduction in HbA_1c levels was observed after 1 year of CGM use. The mean (SD) baseline HbA1c was 9.5% (2.4%), which decreased to 7.6% (2.2%) at 1-year follow-up (Table 1). This difference represents a mean change of -1.86% (2.4%) (95% CI, -2.35 to -1.37; P < .001 [paired t test, -7.53]).

A linear regression model evaluated the relationship between baseline HbA_1c (predictor) and the change in HbA_1c after 1 year (outcome). The change in HbA_1c was calculated as the difference between 1-year follow-up and baseline values. The regression model revealed a significant negative association between baseline HbA_1c and the change in HbA_1c (Β = -0.576; P < .001), indicating that higher baseline HbA_1c values were associated with greater reductions in HbA_1c over the year. The regression equation was: Change in HbA_1c = 3.587 – 0.576 × Baseline HbA_1c

The regression coefficient for baseline HbA_1c was -0.576 (standard error, 0.083; t = -6.931; P < .001), indicating that for each 1% increase in baseline HbA_1c, the reduction of HbA_1c after 1 year increased by approximately 0.576% (Figure 2). The model explained 34.6% of the variance in HbA_1c change (R² = .345; adjusted R² = .338).

Secondary Outcomes

Systolic BP decreased by a mean (SD) -4.9 (17) mm Hg; 95% CI, -8.6 to -1.11; P = .01, paired t test). However, no significant change was observed for diastolic BP (P = .77, paired t test). Similarly, no significant changes were observed in weight, LDL-C concentrations, or eGFR after 1 year of CGM use. A posthoc power analysis indicated that the study was underpowered to detect smaller effect sizes in secondary outcomes. For example, sample size estimates indicated that detecting significant changes in weight and LDL-C concentrations would require sample sizes of 152 and 220 patients, respectively (Table 2).

Discussion

This study found a clinically significant reduction in HbA_1c levels after 1 year among AI/AN patients with non–insulin-dependent T2DM who used CGMs. The mean HbA_1c decreased 1.9%, from 9.5% at baseline to 7.6% after 1 year. This reduction is not only statistically significant (P < .001), it is clinically meaningful—even a 1% decrease in HbA_1c is associated with substantial reductions in the risk of microvascular complications.³ The magnitude of the HbA_1c reduction observed suggests CGM use may be associated with improved glycemic control in this high-risk population. By achieving lower HbA_1c levels, patients may experience improved long-term health outcomes and a reduced burden of DM-related complications.

Changes in oral DM medications during the study period may have contributed to the observed improvements in HbA_1c levels. While the dataset lacked detailed information on types or dosages of oral hypoglycemic agents used, adjustments in medication regimens are common in DM management and could significantly affect glycemic control. The inability to account for these changes results in an inability to attribute the improvements in HbA_1c solely to CGM use. Future studies should collect comprehensive medication data to better isolate the effects of CGM use from other treatment modifications.

Another factor that may have contributed to the improved glycemic control is the DM self-management education and training patients received as part of standard care. Patients met with diabetes educators at least once and learned how to use the CGM device and interpret the data for self-management decisions. This education may have enhanced patient engagement and empowerment, enabling them to make informed choices about diet, physical activity, and medication adherence. Studies have shown that DM self-management education can significantly improve glycemic control and patient outcomes.¹³ By combining the CGM technology with targeted education, patients may have been better equipped to manage their condition, contributing to the observed reduction in HbA_1c levels. Future studies should consider synergistic effects of CGM use and DM education when evaluating interventions for glycemic control.

The significant reduction in HbA_1c indicates CGM use is associated with improved glycemic control in non–insulin-dependent T2DM. The linear regression analysis suggests patients with poorer glycemic control at baseline experienced greater reductions in HbA_1c over the course of 1 year. This finding aligns with previous studies that have shown greater HbA_1c reductions in patients with higher initial levels when using CGMs. Yaron et al reported similar findings: higher baseline HbA_1c levels predicted more substantial improvements with CGM use in patients with T2DM on insulin therapy.¹⁴

This study contributes to existing research by examining the association between CGM use and glycemic control in patients with non– insulin-dependent T2DM within an AI/AN population, a group that has been underreported in previous studies. Most prior research has focused on insulin-dependent patients or populations with different ethnic backgrounds.12 By focusing on patients with non–insulin-dependent T2DM, this study highlights the broader applicability of CGMs beyond traditional use, showcasing their potential association with benefits in earlier stages of DM management. Targeting the AI/AN population addresses a critical knowledge gap, given the disproportionately high prevalence of T2DM and associated complications in this group. The findings of this study suggest integrating CGM technology into the standard care of AI/AN patients with non–insulin-dependent T2DM may be associated with improved glycemic control and may help reduce health disparities.

The modest decrease in systolic BP observed in this study may indicate potential cardiovascular benefits associated with CGM use, possibly due to improved glycemic control and increased patient engagement in self-management. However, given the limited sample size and exclusion criteria, the study lacked sufficient power to detect significant associations between CGM use and other secondary outcomes such as BP, weight, LDL-C, and eGFR. Therefore, the significant finding with systolic BP should be interpreted with caution.

The lack of significant changes in secondary outcomes may be attributed to the study’s limited sample size and the relatively short duration for observing changes in these parameters. Larger studies are needed to assess the full impact of CGM on these variables. The required sample sizes for achieving adequate power in future studies were calculated, highlighting the utility of our study as a pilot, providing critical data for the design of larger, adequately powered studies.

Limitations

The retrospective design of this study limits causal inferences. Moreover, potential confounding variables were not controlled, such as changes in medication regimens (other than insulin use), dietary counseling, or physical activity. Additionally, we could not account for the type or number of oral DM medications prescribed to patients. The dataset included only information on insulin use, without detailed records of other antidiabetic medications. This limitation may have influenced the observed change in glycemic control, as variations in medication regimens could affect HbA_1c levels.

Because this study lacked a comparator group, the effect of CGM use cannot be definitively isolated from other factors (eg, medication changes, dietary modifications, or physical activity). Moreover, CGM devices can be costly and are not universally covered by all insurance or IHS programs, potentially limiting widespread implementation. Policy-level restrictions and patient-specific barriers may also hinder feasibility in other settings.

The small sample size may limit the generalizability of the findings. Of the initial 302 patients, about 69% were excluded due to insulin use or incomplete laboratory data. A ± 4-month window was selected to balance data quality with real-world practices. Extending this window further (eg, ± 6 months) might have included more participants but risked diluting the 1-year endpoint consistency. The lack of statistical significance in secondary metrics may be due to insufficient power rather than the absence of an effect.

Exclusion of patients due to incomplete data may have introduced selection bias. However, patients were included in the overall analysis if they met the criteria for HbA_1c and CGM use, even if they lacked data for secondary outcomes. Additionally, the laboratory’s upper reporting limit for HbA_1c was 14%, with values above this reported as “> 14%.” For analysis, these were recorded as 14.1%, which may underestimate the true baseline HbA_1c levels and impact of the assessment of change. This occurred for 4 of the 93 patients included.

All patients used the Freestyle Libre CGM, which may limit the generalizability of the findings to other CGM brands or models. Differences in device features, accuracy, scanning frequency, and user experience may influence outcomes, and results might differ with other CGM technologies. The dataset did not include patients’ scanning frequency because this metric was not consistently included in the EHRs.

Conclusions

This study found that CGM use was significantly associated with improved glycemic control in patients with non–insulin-dependent T2DM within an AI/AN population, particularly among patients with higher baseline HbA_1c levels. The findings suggest that CGMs may be a valuable tool for managing T2DM beyond insulin-dependent populations.

Additional research with larger sample sizes, control groups, and extended follow-up periods is recommended to explore long-term benefits and impacts on other health metrics. The sample size estimates derived from this study serve as a valuable resource for researchers designing future studies aimed at addressing these gaps. Future research that expands on our findings by including larger, more diverse cohorts, accounting for medication use, and exploring different CGM technologies will enhance understanding and contribute to more effective diabetes management strategies for varied populations.

Diabetes mellitus (DM) is a national health crisis affecting > 38 million people (11.6%) in the United States.¹ American Indian and Alaska Native (AI/AN) adults are disproportionately affected, with a prevalence of 14.5%—the highest among all racial and ethnic groups.¹ Type 2 DM (T2DM) accounts for 90% to 95% of all DM cases and is a leading cause of morbidity and mortality due to its association with cardiovascular disease, kidney failure, and other complications.²

Maintaining glycemic control is important for managing T2DM and preventing microvascular and macrovascular complications.³ The cornerstone of diabetes self-management has been patient self-monitored blood glucose (SMBG) using finger-stick glucometers.⁴ However, SMBG provides measurements from a single point in time and requires frequent, painful, and inconvenient finger pricks, leading to decreased adherence.^5,6 These limitations negatively affect patient engagement and overall glycemic control.⁷

Continuous glucose monitors (CGMs) offer real-time, continuous glucose readings and trends.⁸ CGMs improve glycemic control and reduce hypoglycemic episodes in patients who are insulin-dependent.^9,10 Flash glucose monitors, a type of CGM that requires scanning to obtain glucose readings, provide similar benefits.¹¹ Despite these demonstrated advantages, research has primarily focused on insulin-dependent populations, leaving a significant gap in understanding the effect of CGMs on patients with T2DM who are not insulin-dependent.¹²

Given the high prevalence of T2DM among AI/AN populations and the potential benefits of CGMs, this study sought to evaluate the effect of CGM use on glycemic control and other health metrics in patients with non–insulin-dependent T2DM in an AI/AN population. This focus addresses a critical knowledge gap and may inform clinical practices and policies to improve diabetes management in this high-risk group.

Methods

A retrospective observational study was conducted using deidentified electronic health records (EHRs) from 2019 to 2024 at a federally operated outpatient Indian Health Service (IHS) clinic serving an AI/AN population in the IHS Portland Area (Oregon, Washington, Idaho). The study protocol was reviewed and deemed exempt by institutional review boards at Washington State University and the Portland Area IHS.

Study Population

This study included patients diagnosed with non–insulin-dependent T2DM, had used a CGM for ≥ 1 year, and had hemoglobin A_1c (HbA_1c) measurements within 4 months prior to CGM initiation (baseline) and within ± 4 months after 1 year of CGM use. For other health metrics, including blood pressure (BP), weight, low-density lipoprotein cholesterol (LDL-C), and estimated glomerular filtration rate (eGFR), this study required measurements within 6 months before CGM initiation and within 6 months after 1 year of CGM use. The baseline HbA_1c in the dataset ranged from 5.3% to > 14%.

Patients were excluded if they used insulin during the study period, had incomplete laboratory or clinical data for the required time frame, or had < 1 year of CGM use. The dataset did not include detailed information on oral DM medications; thus, we could not report or account for the type or number of oral hypoglycemic agents used by the patients. The IHS clinical applications coordinator compiled the dataset from the EHR, identifying patients who were prescribed and received a CGM at the clinic. All patients used the Abbott Freestyle Libre CGM, the only formulary CGM available at the clinic during the study period.

A 1-year follow-up endpoint was selected for several reasons: (1) to capture potential seasonal variations in diet and activity; (2) to align with the clinic’s standard practice of annual comprehensive diabetes evaluations; and (3) to allow sufficient time for patients to adapt to CGM use and reflect any meaningful changes in glycemic control.

All patients received standard DM care according to clinic protocols, which included DM self-management education and training. Patients met with the diabetes educator at least once, during which the educator emphasized making informed decisions using CGM data, such as adjusting dietary choices and physical activity levels to manage blood glucose concentrations effectively.

A total of 302 patients were initially identified. After applying exclusion criteria, 132 were excluded due to insulin use, and 77 were excluded due to incomplete HbA_1c data within the specified time frames (Figure 1). The final sample included 93 patients.

Measures

The primary outcome was the change in HbA_1c levels from baseline to 1 year after CGM initiation. Secondary outcomes included changes in weight, systolic and diastolic BP, LDL-C concentrations, and eGFR. For the primary outcome, HbA_1c values were collected within a grace period of ± 4 months from the baseline and 1-year time points. The laboratory’s upper reporting limit for HbA_1c was 14%; values reported as “> 14%” were recorded as 14.1% for data analysis, although the actual values could have been higher.

For secondary outcomes, data were included if measurements were obtained within ± 6 months of the baseline and 1-year time points. Patients who did not have measurements within these time frames for specific metrics were excluded from secondary outcome analysis but remained in the overall study if they met the criteria for HbA_1c and CGM use.

Statistical Analysis

Statistical analysis was performed using R statistical software version 4.4.2. Paired t tests were conducted to compare baseline and 1-year follow- up measurements for variables with parametric distributions. Wilcoxon signed-rank test was used for nonparametric data. A linear regression analysis was conducted to examine the relationship between baseline HbA_1c levels and the change in HbA_1c after 1 year of CGM use. Differences were considered significant at P < .05 set a priori. To guide future research, a posthoc power analysis was performed using Cohen’s d to estimate the required sample sizes for detecting significant effects, assuming a similar population.

Results

The study included 93 patients, with a mean (SD) age of 55 (13) years (range, 29-83 years). Of the participants, 56 were female (60%) and 37 were male (40%). All participants were identified as AI/AN and had non–insulin-dependent T2DM.

Primary Outcomes

A significant reduction in HbA_1c levels was observed after 1 year of CGM use. The mean (SD) baseline HbA1c was 9.5% (2.4%), which decreased to 7.6% (2.2%) at 1-year follow-up (Table 1). This difference represents a mean change of -1.86% (2.4%) (95% CI, -2.35 to -1.37; P < .001 [paired t test, -7.53]).

A linear regression model evaluated the relationship between baseline HbA_1c (predictor) and the change in HbA_1c after 1 year (outcome). The change in HbA_1c was calculated as the difference between 1-year follow-up and baseline values. The regression model revealed a significant negative association between baseline HbA_1c and the change in HbA_1c (Β = -0.576; P < .001), indicating that higher baseline HbA_1c values were associated with greater reductions in HbA_1c over the year. The regression equation was: Change in HbA_1c = 3.587 – 0.576 × Baseline HbA_1c

The regression coefficient for baseline HbA_1c was -0.576 (standard error, 0.083; t = -6.931; P < .001), indicating that for each 1% increase in baseline HbA_1c, the reduction of HbA_1c after 1 year increased by approximately 0.576% (Figure 2). The model explained 34.6% of the variance in HbA_1c change (R² = .345; adjusted R² = .338).

Secondary Outcomes

Systolic BP decreased by a mean (SD) -4.9 (17) mm Hg; 95% CI, -8.6 to -1.11; P = .01, paired t test). However, no significant change was observed for diastolic BP (P = .77, paired t test). Similarly, no significant changes were observed in weight, LDL-C concentrations, or eGFR after 1 year of CGM use. A posthoc power analysis indicated that the study was underpowered to detect smaller effect sizes in secondary outcomes. For example, sample size estimates indicated that detecting significant changes in weight and LDL-C concentrations would require sample sizes of 152 and 220 patients, respectively (Table 2).

Discussion

This study found a clinically significant reduction in HbA_1c levels after 1 year among AI/AN patients with non–insulin-dependent T2DM who used CGMs. The mean HbA_1c decreased 1.9%, from 9.5% at baseline to 7.6% after 1 year. This reduction is not only statistically significant (P < .001), it is clinically meaningful—even a 1% decrease in HbA_1c is associated with substantial reductions in the risk of microvascular complications.³ The magnitude of the HbA_1c reduction observed suggests CGM use may be associated with improved glycemic control in this high-risk population. By achieving lower HbA_1c levels, patients may experience improved long-term health outcomes and a reduced burden of DM-related complications.

Changes in oral DM medications during the study period may have contributed to the observed improvements in HbA_1c levels. While the dataset lacked detailed information on types or dosages of oral hypoglycemic agents used, adjustments in medication regimens are common in DM management and could significantly affect glycemic control. The inability to account for these changes results in an inability to attribute the improvements in HbA_1c solely to CGM use. Future studies should collect comprehensive medication data to better isolate the effects of CGM use from other treatment modifications.

Another factor that may have contributed to the improved glycemic control is the DM self-management education and training patients received as part of standard care. Patients met with diabetes educators at least once and learned how to use the CGM device and interpret the data for self-management decisions. This education may have enhanced patient engagement and empowerment, enabling them to make informed choices about diet, physical activity, and medication adherence. Studies have shown that DM self-management education can significantly improve glycemic control and patient outcomes.¹³ By combining the CGM technology with targeted education, patients may have been better equipped to manage their condition, contributing to the observed reduction in HbA_1c levels. Future studies should consider synergistic effects of CGM use and DM education when evaluating interventions for glycemic control.

The significant reduction in HbA_1c indicates CGM use is associated with improved glycemic control in non–insulin-dependent T2DM. The linear regression analysis suggests patients with poorer glycemic control at baseline experienced greater reductions in HbA_1c over the course of 1 year. This finding aligns with previous studies that have shown greater HbA_1c reductions in patients with higher initial levels when using CGMs. Yaron et al reported similar findings: higher baseline HbA_1c levels predicted more substantial improvements with CGM use in patients with T2DM on insulin therapy.¹⁴

This study contributes to existing research by examining the association between CGM use and glycemic control in patients with non– insulin-dependent T2DM within an AI/AN population, a group that has been underreported in previous studies. Most prior research has focused on insulin-dependent patients or populations with different ethnic backgrounds.12 By focusing on patients with non–insulin-dependent T2DM, this study highlights the broader applicability of CGMs beyond traditional use, showcasing their potential association with benefits in earlier stages of DM management. Targeting the AI/AN population addresses a critical knowledge gap, given the disproportionately high prevalence of T2DM and associated complications in this group. The findings of this study suggest integrating CGM technology into the standard care of AI/AN patients with non–insulin-dependent T2DM may be associated with improved glycemic control and may help reduce health disparities.

The modest decrease in systolic BP observed in this study may indicate potential cardiovascular benefits associated with CGM use, possibly due to improved glycemic control and increased patient engagement in self-management. However, given the limited sample size and exclusion criteria, the study lacked sufficient power to detect significant associations between CGM use and other secondary outcomes such as BP, weight, LDL-C, and eGFR. Therefore, the significant finding with systolic BP should be interpreted with caution.

The lack of significant changes in secondary outcomes may be attributed to the study’s limited sample size and the relatively short duration for observing changes in these parameters. Larger studies are needed to assess the full impact of CGM on these variables. The required sample sizes for achieving adequate power in future studies were calculated, highlighting the utility of our study as a pilot, providing critical data for the design of larger, adequately powered studies.

Limitations

The retrospective design of this study limits causal inferences. Moreover, potential confounding variables were not controlled, such as changes in medication regimens (other than insulin use), dietary counseling, or physical activity. Additionally, we could not account for the type or number of oral DM medications prescribed to patients. The dataset included only information on insulin use, without detailed records of other antidiabetic medications. This limitation may have influenced the observed change in glycemic control, as variations in medication regimens could affect HbA_1c levels.

Because this study lacked a comparator group, the effect of CGM use cannot be definitively isolated from other factors (eg, medication changes, dietary modifications, or physical activity). Moreover, CGM devices can be costly and are not universally covered by all insurance or IHS programs, potentially limiting widespread implementation. Policy-level restrictions and patient-specific barriers may also hinder feasibility in other settings.

The small sample size may limit the generalizability of the findings. Of the initial 302 patients, about 69% were excluded due to insulin use or incomplete laboratory data. A ± 4-month window was selected to balance data quality with real-world practices. Extending this window further (eg, ± 6 months) might have included more participants but risked diluting the 1-year endpoint consistency. The lack of statistical significance in secondary metrics may be due to insufficient power rather than the absence of an effect.

Exclusion of patients due to incomplete data may have introduced selection bias. However, patients were included in the overall analysis if they met the criteria for HbA_1c and CGM use, even if they lacked data for secondary outcomes. Additionally, the laboratory’s upper reporting limit for HbA_1c was 14%, with values above this reported as “> 14%.” For analysis, these were recorded as 14.1%, which may underestimate the true baseline HbA_1c levels and impact of the assessment of change. This occurred for 4 of the 93 patients included.

All patients used the Freestyle Libre CGM, which may limit the generalizability of the findings to other CGM brands or models. Differences in device features, accuracy, scanning frequency, and user experience may influence outcomes, and results might differ with other CGM technologies. The dataset did not include patients’ scanning frequency because this metric was not consistently included in the EHRs.

Conclusions

This study found that CGM use was significantly associated with improved glycemic control in patients with non–insulin-dependent T2DM within an AI/AN population, particularly among patients with higher baseline HbA_1c levels. The findings suggest that CGMs may be a valuable tool for managing T2DM beyond insulin-dependent populations.

Additional research with larger sample sizes, control groups, and extended follow-up periods is recommended to explore long-term benefits and impacts on other health metrics. The sample size estimates derived from this study serve as a valuable resource for researchers designing future studies aimed at addressing these gaps. Future research that expands on our findings by including larger, more diverse cohorts, accounting for medication use, and exploring different CGM technologies will enhance understanding and contribute to more effective diabetes management strategies for varied populations.

Diabetes mellitus (DM) is a national health crisis affecting > 38 million people (11.6%) in the United States.¹ American Indian and Alaska Native (AI/AN) adults are disproportionately affected, with a prevalence of 14.5%—the highest among all racial and ethnic groups.¹ Type 2 DM (T2DM) accounts for 90% to 95% of all DM cases and is a leading cause of morbidity and mortality due to its association with cardiovascular disease, kidney failure, and other complications.²

Maintaining glycemic control is important for managing T2DM and preventing microvascular and macrovascular complications.³ The cornerstone of diabetes self-management has been patient self-monitored blood glucose (SMBG) using finger-stick glucometers.⁴ However, SMBG provides measurements from a single point in time and requires frequent, painful, and inconvenient finger pricks, leading to decreased adherence.^5,6 These limitations negatively affect patient engagement and overall glycemic control.⁷

Continuous glucose monitors (CGMs) offer real-time, continuous glucose readings and trends.⁸ CGMs improve glycemic control and reduce hypoglycemic episodes in patients who are insulin-dependent.^9,10 Flash glucose monitors, a type of CGM that requires scanning to obtain glucose readings, provide similar benefits.¹¹ Despite these demonstrated advantages, research has primarily focused on insulin-dependent populations, leaving a significant gap in understanding the effect of CGMs on patients with T2DM who are not insulin-dependent.¹²

Given the high prevalence of T2DM among AI/AN populations and the potential benefits of CGMs, this study sought to evaluate the effect of CGM use on glycemic control and other health metrics in patients with non–insulin-dependent T2DM in an AI/AN population. This focus addresses a critical knowledge gap and may inform clinical practices and policies to improve diabetes management in this high-risk group.

Methods

A retrospective observational study was conducted using deidentified electronic health records (EHRs) from 2019 to 2024 at a federally operated outpatient Indian Health Service (IHS) clinic serving an AI/AN population in the IHS Portland Area (Oregon, Washington, Idaho). The study protocol was reviewed and deemed exempt by institutional review boards at Washington State University and the Portland Area IHS.

Study Population

This study included patients diagnosed with non–insulin-dependent T2DM, had used a CGM for ≥ 1 year, and had hemoglobin A_1c (HbA_1c) measurements within 4 months prior to CGM initiation (baseline) and within ± 4 months after 1 year of CGM use. For other health metrics, including blood pressure (BP), weight, low-density lipoprotein cholesterol (LDL-C), and estimated glomerular filtration rate (eGFR), this study required measurements within 6 months before CGM initiation and within 6 months after 1 year of CGM use. The baseline HbA_1c in the dataset ranged from 5.3% to > 14%.

Patients were excluded if they used insulin during the study period, had incomplete laboratory or clinical data for the required time frame, or had < 1 year of CGM use. The dataset did not include detailed information on oral DM medications; thus, we could not report or account for the type or number of oral hypoglycemic agents used by the patients. The IHS clinical applications coordinator compiled the dataset from the EHR, identifying patients who were prescribed and received a CGM at the clinic. All patients used the Abbott Freestyle Libre CGM, the only formulary CGM available at the clinic during the study period.

A 1-year follow-up endpoint was selected for several reasons: (1) to capture potential seasonal variations in diet and activity; (2) to align with the clinic’s standard practice of annual comprehensive diabetes evaluations; and (3) to allow sufficient time for patients to adapt to CGM use and reflect any meaningful changes in glycemic control.

All patients received standard DM care according to clinic protocols, which included DM self-management education and training. Patients met with the diabetes educator at least once, during which the educator emphasized making informed decisions using CGM data, such as adjusting dietary choices and physical activity levels to manage blood glucose concentrations effectively.

A total of 302 patients were initially identified. After applying exclusion criteria, 132 were excluded due to insulin use, and 77 were excluded due to incomplete HbA_1c data within the specified time frames (Figure 1). The final sample included 93 patients.

Measures

The primary outcome was the change in HbA_1c levels from baseline to 1 year after CGM initiation. Secondary outcomes included changes in weight, systolic and diastolic BP, LDL-C concentrations, and eGFR. For the primary outcome, HbA_1c values were collected within a grace period of ± 4 months from the baseline and 1-year time points. The laboratory’s upper reporting limit for HbA_1c was 14%; values reported as “> 14%” were recorded as 14.1% for data analysis, although the actual values could have been higher.

For secondary outcomes, data were included if measurements were obtained within ± 6 months of the baseline and 1-year time points. Patients who did not have measurements within these time frames for specific metrics were excluded from secondary outcome analysis but remained in the overall study if they met the criteria for HbA_1c and CGM use.

Statistical Analysis

Statistical analysis was performed using R statistical software version 4.4.2. Paired t tests were conducted to compare baseline and 1-year follow- up measurements for variables with parametric distributions. Wilcoxon signed-rank test was used for nonparametric data. A linear regression analysis was conducted to examine the relationship between baseline HbA_1c levels and the change in HbA_1c after 1 year of CGM use. Differences were considered significant at P < .05 set a priori. To guide future research, a posthoc power analysis was performed using Cohen’s d to estimate the required sample sizes for detecting significant effects, assuming a similar population.

Results

The study included 93 patients, with a mean (SD) age of 55 (13) years (range, 29-83 years). Of the participants, 56 were female (60%) and 37 were male (40%). All participants were identified as AI/AN and had non–insulin-dependent T2DM.

Primary Outcomes

A significant reduction in HbA_1c levels was observed after 1 year of CGM use. The mean (SD) baseline HbA1c was 9.5% (2.4%), which decreased to 7.6% (2.2%) at 1-year follow-up (Table 1). This difference represents a mean change of -1.86% (2.4%) (95% CI, -2.35 to -1.37; P < .001 [paired t test, -7.53]).

A linear regression model evaluated the relationship between baseline HbA_1c (predictor) and the change in HbA_1c after 1 year (outcome). The change in HbA_1c was calculated as the difference between 1-year follow-up and baseline values. The regression model revealed a significant negative association between baseline HbA_1c and the change in HbA_1c (Β = -0.576; P < .001), indicating that higher baseline HbA_1c values were associated with greater reductions in HbA_1c over the year. The regression equation was: Change in HbA_1c = 3.587 – 0.576 × Baseline HbA_1c

The regression coefficient for baseline HbA_1c was -0.576 (standard error, 0.083; t = -6.931; P < .001), indicating that for each 1% increase in baseline HbA_1c, the reduction of HbA_1c after 1 year increased by approximately 0.576% (Figure 2). The model explained 34.6% of the variance in HbA_1c change (R² = .345; adjusted R² = .338).

Secondary Outcomes

Systolic BP decreased by a mean (SD) -4.9 (17) mm Hg; 95% CI, -8.6 to -1.11; P = .01, paired t test). However, no significant change was observed for diastolic BP (P = .77, paired t test). Similarly, no significant changes were observed in weight, LDL-C concentrations, or eGFR after 1 year of CGM use. A posthoc power analysis indicated that the study was underpowered to detect smaller effect sizes in secondary outcomes. For example, sample size estimates indicated that detecting significant changes in weight and LDL-C concentrations would require sample sizes of 152 and 220 patients, respectively (Table 2).

Discussion

This study found a clinically significant reduction in HbA_1c levels after 1 year among AI/AN patients with non–insulin-dependent T2DM who used CGMs. The mean HbA_1c decreased 1.9%, from 9.5% at baseline to 7.6% after 1 year. This reduction is not only statistically significant (P < .001), it is clinically meaningful—even a 1% decrease in HbA_1c is associated with substantial reductions in the risk of microvascular complications.³ The magnitude of the HbA_1c reduction observed suggests CGM use may be associated with improved glycemic control in this high-risk population. By achieving lower HbA_1c levels, patients may experience improved long-term health outcomes and a reduced burden of DM-related complications.

Changes in oral DM medications during the study period may have contributed to the observed improvements in HbA_1c levels. While the dataset lacked detailed information on types or dosages of oral hypoglycemic agents used, adjustments in medication regimens are common in DM management and could significantly affect glycemic control. The inability to account for these changes results in an inability to attribute the improvements in HbA_1c solely to CGM use. Future studies should collect comprehensive medication data to better isolate the effects of CGM use from other treatment modifications.

Another factor that may have contributed to the improved glycemic control is the DM self-management education and training patients received as part of standard care. Patients met with diabetes educators at least once and learned how to use the CGM device and interpret the data for self-management decisions. This education may have enhanced patient engagement and empowerment, enabling them to make informed choices about diet, physical activity, and medication adherence. Studies have shown that DM self-management education can significantly improve glycemic control and patient outcomes.¹³ By combining the CGM technology with targeted education, patients may have been better equipped to manage their condition, contributing to the observed reduction in HbA_1c levels. Future studies should consider synergistic effects of CGM use and DM education when evaluating interventions for glycemic control.

The significant reduction in HbA_1c indicates CGM use is associated with improved glycemic control in non–insulin-dependent T2DM. The linear regression analysis suggests patients with poorer glycemic control at baseline experienced greater reductions in HbA_1c over the course of 1 year. This finding aligns with previous studies that have shown greater HbA_1c reductions in patients with higher initial levels when using CGMs. Yaron et al reported similar findings: higher baseline HbA_1c levels predicted more substantial improvements with CGM use in patients with T2DM on insulin therapy.¹⁴

This study contributes to existing research by examining the association between CGM use and glycemic control in patients with non– insulin-dependent T2DM within an AI/AN population, a group that has been underreported in previous studies. Most prior research has focused on insulin-dependent patients or populations with different ethnic backgrounds.12 By focusing on patients with non–insulin-dependent T2DM, this study highlights the broader applicability of CGMs beyond traditional use, showcasing their potential association with benefits in earlier stages of DM management. Targeting the AI/AN population addresses a critical knowledge gap, given the disproportionately high prevalence of T2DM and associated complications in this group. The findings of this study suggest integrating CGM technology into the standard care of AI/AN patients with non–insulin-dependent T2DM may be associated with improved glycemic control and may help reduce health disparities.

The modest decrease in systolic BP observed in this study may indicate potential cardiovascular benefits associated with CGM use, possibly due to improved glycemic control and increased patient engagement in self-management. However, given the limited sample size and exclusion criteria, the study lacked sufficient power to detect significant associations between CGM use and other secondary outcomes such as BP, weight, LDL-C, and eGFR. Therefore, the significant finding with systolic BP should be interpreted with caution.

The lack of significant changes in secondary outcomes may be attributed to the study’s limited sample size and the relatively short duration for observing changes in these parameters. Larger studies are needed to assess the full impact of CGM on these variables. The required sample sizes for achieving adequate power in future studies were calculated, highlighting the utility of our study as a pilot, providing critical data for the design of larger, adequately powered studies.

Limitations

The retrospective design of this study limits causal inferences. Moreover, potential confounding variables were not controlled, such as changes in medication regimens (other than insulin use), dietary counseling, or physical activity. Additionally, we could not account for the type or number of oral DM medications prescribed to patients. The dataset included only information on insulin use, without detailed records of other antidiabetic medications. This limitation may have influenced the observed change in glycemic control, as variations in medication regimens could affect HbA_1c levels.

Because this study lacked a comparator group, the effect of CGM use cannot be definitively isolated from other factors (eg, medication changes, dietary modifications, or physical activity). Moreover, CGM devices can be costly and are not universally covered by all insurance or IHS programs, potentially limiting widespread implementation. Policy-level restrictions and patient-specific barriers may also hinder feasibility in other settings.

The small sample size may limit the generalizability of the findings. Of the initial 302 patients, about 69% were excluded due to insulin use or incomplete laboratory data. A ± 4-month window was selected to balance data quality with real-world practices. Extending this window further (eg, ± 6 months) might have included more participants but risked diluting the 1-year endpoint consistency. The lack of statistical significance in secondary metrics may be due to insufficient power rather than the absence of an effect.

Exclusion of patients due to incomplete data may have introduced selection bias. However, patients were included in the overall analysis if they met the criteria for HbA_1c and CGM use, even if they lacked data for secondary outcomes. Additionally, the laboratory’s upper reporting limit for HbA_1c was 14%, with values above this reported as “> 14%.” For analysis, these were recorded as 14.1%, which may underestimate the true baseline HbA_1c levels and impact of the assessment of change. This occurred for 4 of the 93 patients included.

All patients used the Freestyle Libre CGM, which may limit the generalizability of the findings to other CGM brands or models. Differences in device features, accuracy, scanning frequency, and user experience may influence outcomes, and results might differ with other CGM technologies. The dataset did not include patients’ scanning frequency because this metric was not consistently included in the EHRs.

Conclusions

This study found that CGM use was significantly associated with improved glycemic control in patients with non–insulin-dependent T2DM within an AI/AN population, particularly among patients with higher baseline HbA_1c levels. The findings suggest that CGMs may be a valuable tool for managing T2DM beyond insulin-dependent populations.

Additional research with larger sample sizes, control groups, and extended follow-up periods is recommended to explore long-term benefits and impacts on other health metrics. The sample size estimates derived from this study serve as a valuable resource for researchers designing future studies aimed at addressing these gaps. Future research that expands on our findings by including larger, more diverse cohorts, accounting for medication use, and exploring different CGM technologies will enhance understanding and contribute to more effective diabetes management strategies for varied populations.

Cardiovascular disease (CVD) is the leading cause of death among women in the United States.¹ Most CVD is due to the buildup of plaque (ie, cholesterol, proteins, calcium, and inflammatory cells) in artery walls.² The plaque may lead to atherosclerotic cardiovascular disease (ASCVD), which includes coronary heart disease, cerebrovascular disease, peripheral artery disease, and aortic atherosclerotic disease.^2,3 Control and reduction of ASCVD risk factors, including high cholesterol levels, elevated blood pressure, insulin resistance, smoking, and a sedentary lifestyle, can contribute to a reduction in ASCVD morbidity and mortality.² People with type 2 diabetes mellitus (T2DM) have an increased prevalence of lipid abnormalities, contributing to their high risk of ASCVD.^4,5

The prescribing of statins (3-hydroxy-3-methyl-glutaryl-coenzmye A reductase inhibitors) is the cornerstone of lipid-lowering therapy and cardiovascular risk reduction for primary and secondary prevention of ASCVD.⁶ The American Diabetes Association (ADA) and American College of Cardiology/American Heart Association (ACC/AHA) recommend moderate- to high-intensity statins for primary prevention in patients with T2DM and high-intensity statins for secondary prevention in those with or without diabetes when not contraindicated.^4,5,7 Despite eligibility according to guideline recommendations, research predominantly shows that women are less likely to receive statin therapy; however, this trend is improving. ^[6,8-11] To explain the sex differences in statin use, Nanna et al found that there is a combination of women being offered statin therapy less frequently, declining therapy more frequently, and discontinuing treatment more frequently.¹¹ One possibility for discontinuing treatment could be statin-associated muscle symptoms (SAMS), which occur in about 10% of patients.¹² The incidence of adverse effects (AEs) may be related to the way statins are metabolized.

Pharmacogenomic testing is free for veterans through the US Department of Veterans Affairs (VA) PHASER program, which offers information and recommendations for a panel of 11 gene variants. The panel includes genes related to common medication classes such as anticoagulants, antiplatelets, proton pump inhibitors, nonsteroidal anti-inflammatory drugs, opioids, antidepressants, and statins. The VA PHASER panel includes the solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene, which is predominantly expressed in the liver and facilitates the hepatic uptake of most statins.^13,14 A reduced function of SLCO1B1 can lead to higher statin levels, resulting in increased concentrations that may potentially cause SAMS.^13,14 Some alleles associated with reduced function include SLCO1B1*5, *15, *23, *31, and *46 to *49, whereas others are associated with increased function, such as SLCO1B1 *14 and *20 (Appendix).¹⁵ Supporting evidence shows the SLCO1B1*5 nucleotide polymorphism increases plasma levels of simvastatin and atorvastatin, affecting effectiveness or toxicity. ¹³ Females tend to have a lower body weight and higher percentage of body fat compared with males, which might lead to higher concentrations of lipophilic drugs, including atorvastatin and simvastatin, which may be exacerbated by decreased function of SLCO1B1*5.¹⁵ With pharmacogenomic testing, therapeutic recommendations can be made to improve the overall safety and efficacy of statins, thus improving adherence using a patient-specific approach.^14,15

Methods

Carl Vinson VA Medical Center (CVVAMC) serves about 42,000 veterans in Central and South Georgia, of which about 15% are female. Of the female veterans enrolled in care, 63% identify as Black, 27% White, and 1.5% as Asian, American Indian/Alaska Native, or Native Hawaiian/Other Pacific Islander. The 2020 Veterans Chartbook report showed that female veterans and minority racial and ethnic groups had worse access to health care and higher mortality rates than their male and non-Hispanic White counterparts.¹⁶

The Primary Care Equity Dashboard (PCED) was developed to engage the VA health care workforce in the process of identifying and addressing inequities in local patient populations.¹⁷ Using electronic quality measure data, the PCED provides Veterans Integrated Service Network-level and facility-level performance on several metrics.¹⁸ The PCED had not been previously used at the CVVAMC, and few publications or quality improvement projects regarding its use have been reported by the VA Office of Health Equity. PCED helped identify disparities when comparing female to male patients in the prescribing of statin therapy for patients with CVD and statin therapy for patients with T2DM.

VA PHASER pharmacogenomic analyses provided an opportunity to expand this quality improvement project. Sanford Health and the VA collaborated on the PHASER program to offer free genetic testing for veterans. The program launched in 2019 and expanded to various VA sites, including CVVAMC in March 2023. This program has been extended to December 31, 2025.

The primary objective of this quality improvement project was to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk. Secondary outcomes included increased pharmacogenomic testing and the assessment of pharmacogenomic results related to statin therapy. This project was approved by the CVVAMC Pharmacy and Therapeutics Committee. The PCED was used to identify female veterans with T2DM and/or CVD without an active prescription for a statin between July and October 2023. A review of Computerized Patient Record System patient charts was completed to screen for prespecified inclusion and exclusion criteria. Veterans were included if they were assigned female at birth, were enrolled in care at CVVAMC, and had a diagnosis of T2DM or CVD (history of myocardial infarction, coronary bypass graft, percutaneous coronary intervention, or other revascularization in any setting).

Veterans were excluded if they were currently pregnant, trying to conceive, breastfeeding, had a T1DM diagnosis, had previously documented hypersensitivity to a statin, active liver failure or decompensated cirrhosis, previously documented statin-associated rhabdomyolysis or autoimmune myopathy, an active prescription for a proprotein convertase subtilisin/kexin type 9 inhibitor, or previously documented statin intolerance (defined as the inability to tolerate ≥ 3 statins, with ≥ 1 prescribed at low intensity or alternate-day dosing). The female veterans were compared to 2 comparators: the facility's male veterans and the VA national average, identified via the PCED.

Once a veteran was screened, they were telephoned between October 2023 and February 2024 and provided education on statin use and pharmacogenomic testing using a standardized note template. An order was placed for participants who provided verbal consent for pharmacogenomic testing. Those who agreed to statin initiation were referred to a clinical pharmacist practitioner (CPP) who contacted them at a later date to prescribe a statin following the recommendations of the 2019 ACC/AHA and 2023 ADA guidelines and pharmacogenomic testing, if applicable.^4,5,7 Appropriate monitoring and follow-up occurred at the discretion of each CPP. Data collection included: age, race, diagnoses (T2DM, CVD, or both), baseline lipid panel (total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein), hepatic function, name and dose of statin, reasons for declining statin therapy, and pharmacogenomic testing results related to SLCO1B1.

Results

At baseline in July 2023, 77.8% of female veterans with T2DM were prescribed a statin, which exceeded the national VA average (77.0%), but was below the rate for male veterans (78.7%) in the facility comparator group.17 Additionally, 82.2% of females with CVD were prescribed a statin, which was below the national VA average of 86.0% and the 84.9% of male veterans in the facility comparator group.17 The PCED identified 189 female veterans from July 2023 to October 2023 who may benefit from statin therapy. Thirty-three females met the exclusion criteria. Of the 156 included veterans, 129 (82.7%) were successfully contacted and 27 (17.3%) could not be reached by telephone after 3 attempts (Figure 1). The 129 female veterans contacted had a mean age of 59 years and the majority were Black (82.9%) (Table 1).

Primary Outcomes

Of the 129 contacted veterans, 31 (24.0%) had a non-VA statin prescription, 13 (10.1%) had an active VA statin prescription, and 85 (65.9%) did not have a statin prescription, despite being eligible. Statin adherence was confirmed with participants, and the medication list was updated accordingly.

Of the 85 veterans with no active statin therapy, 37 (43.5%) accepted a new statin prescription and 48 (56.5%) declined. There were various reasons provided for declining statin therapy: 17 participants (35.4%) declined due to concern for AEs (Table 2).

From July 2023 to March 2024, the percentage of female veterans with active statin therapy with T2DM increased from 77.8% to 79.0%. For those with active statin therapy with CVD, usage increased from 82.2% to 90.2%, which exceeded the national VA average and facility male comparator group (Figures 2 and 3).¹⁷

Secondary Outcomes

Seventy-one of 129 veterans (55.0%) gave verbal consent, and 47 (66.2%) completed the pharmacogenomic testing; 58 (45.0%) declined. Five veterans (10.6%) had a known SLCO1B1 allele variant present. One veteran required a change in statin therapy based on the results (eAppendix).

Discussion

This project aimed to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk and increase pharmacogenomic testing using the PCED and care managed by CPPs. The results of this quality improvement project illustrated that both metrics have improved at CVVAMC as a result of the intervention. The results in both metrics now exceed the PCED national VA average, and the CVD metric also exceeds that of the facility male comparator group. While there was only a 1.2% increase from July 2023 to March 2024 for patients with T2DM, there was an 8.0% increase for patients with CVD. Despite standardized education on statin use, more veterans declined therapy than accepted it, mostly due to concern for AEs. Recording the reasons for declining statin therapy offered valuable insight that can be used in additional discussions with veterans and clinicians.

Pharmacogenomics gives clinicians the unique opportunity to take a proactive approach to better predict drug responses, potentially allowing for less trial and error with medications, fewer AEs, greater trust in the clinician, and improved medication adherence. The CPPs incorporated pharmacogenomic testing into their practice, which led to identifying 5 SLCO1B1 gene abnormalities. The PCED served as a powerful tool for advancing equity-focused quality improvement initiatives on a local level and was crucial in prioritizing the detection of veterans potentially receiving suboptimal care.

Limitations

The nature of “cold calls” made it challenging to establish contact for inclusion in this study. An alternative to increase engagement could have been scheduled phone or face-to-face visits. While the use of the PCED was crucial, data did not account for statins listed in the non-VA medication list. All 31 patients with statins prescribed outside the VA had a start date added to provide the most accurate representation of the data moving forward.

Another limitation in this project was its small sample size and population. CVVAMC serves about 6200 female veterans, with roughly 63% identifying as Black. The preponderance of Black individuals (83%) in this project is typical for the female patient population at CVVAMC but may not reflect the demographics of other populations. Other limitations to this project consisted of scheduling conflicts. Appointments for laboratory draws at community-based outpatient clinics were subject to availability, which resulted in some delay in completion of pharmacogenomic testing.

Conclusions

CPPs can help reduce inequity in health care delivery. Increased incorporation of the PCED into regular practice within the VA is recommended to continue addressing sex disparities in statin use, diabetes control, blood pressure management, cancer screenings, and vaccination needs. CVVAMC plans to expand its use through another quality improvement project focused on reducing sex disparities in blood pressure management. Improving educational resources made available to veterans on the importance of statin therapy and potential to mitigate AEs through use of the VA PHASER program also would be helpful. This project successfully improved CVVAMC metrics for female veterans appropriately prescribed statin therapy and increased access to pharmacogenomic testing. Most importantly, it helped close the sex-based gap in CVD risk reduction care.

Cardiovascular disease (CVD) is the leading cause of death among women in the United States.¹ Most CVD is due to the buildup of plaque (ie, cholesterol, proteins, calcium, and inflammatory cells) in artery walls.² The plaque may lead to atherosclerotic cardiovascular disease (ASCVD), which includes coronary heart disease, cerebrovascular disease, peripheral artery disease, and aortic atherosclerotic disease.^2,3 Control and reduction of ASCVD risk factors, including high cholesterol levels, elevated blood pressure, insulin resistance, smoking, and a sedentary lifestyle, can contribute to a reduction in ASCVD morbidity and mortality.² People with type 2 diabetes mellitus (T2DM) have an increased prevalence of lipid abnormalities, contributing to their high risk of ASCVD.^4,5

The prescribing of statins (3-hydroxy-3-methyl-glutaryl-coenzmye A reductase inhibitors) is the cornerstone of lipid-lowering therapy and cardiovascular risk reduction for primary and secondary prevention of ASCVD.⁶ The American Diabetes Association (ADA) and American College of Cardiology/American Heart Association (ACC/AHA) recommend moderate- to high-intensity statins for primary prevention in patients with T2DM and high-intensity statins for secondary prevention in those with or without diabetes when not contraindicated.^4,5,7 Despite eligibility according to guideline recommendations, research predominantly shows that women are less likely to receive statin therapy; however, this trend is improving. ^[6,8-11] To explain the sex differences in statin use, Nanna et al found that there is a combination of women being offered statin therapy less frequently, declining therapy more frequently, and discontinuing treatment more frequently.¹¹ One possibility for discontinuing treatment could be statin-associated muscle symptoms (SAMS), which occur in about 10% of patients.¹² The incidence of adverse effects (AEs) may be related to the way statins are metabolized.

Pharmacogenomic testing is free for veterans through the US Department of Veterans Affairs (VA) PHASER program, which offers information and recommendations for a panel of 11 gene variants. The panel includes genes related to common medication classes such as anticoagulants, antiplatelets, proton pump inhibitors, nonsteroidal anti-inflammatory drugs, opioids, antidepressants, and statins. The VA PHASER panel includes the solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene, which is predominantly expressed in the liver and facilitates the hepatic uptake of most statins.^13,14 A reduced function of SLCO1B1 can lead to higher statin levels, resulting in increased concentrations that may potentially cause SAMS.^13,14 Some alleles associated with reduced function include SLCO1B1*5, *15, *23, *31, and *46 to *49, whereas others are associated with increased function, such as SLCO1B1 *14 and *20 (Appendix).¹⁵ Supporting evidence shows the SLCO1B1*5 nucleotide polymorphism increases plasma levels of simvastatin and atorvastatin, affecting effectiveness or toxicity. ¹³ Females tend to have a lower body weight and higher percentage of body fat compared with males, which might lead to higher concentrations of lipophilic drugs, including atorvastatin and simvastatin, which may be exacerbated by decreased function of SLCO1B1*5.¹⁵ With pharmacogenomic testing, therapeutic recommendations can be made to improve the overall safety and efficacy of statins, thus improving adherence using a patient-specific approach.^14,15

Methods

Carl Vinson VA Medical Center (CVVAMC) serves about 42,000 veterans in Central and South Georgia, of which about 15% are female. Of the female veterans enrolled in care, 63% identify as Black, 27% White, and 1.5% as Asian, American Indian/Alaska Native, or Native Hawaiian/Other Pacific Islander. The 2020 Veterans Chartbook report showed that female veterans and minority racial and ethnic groups had worse access to health care and higher mortality rates than their male and non-Hispanic White counterparts.¹⁶

The Primary Care Equity Dashboard (PCED) was developed to engage the VA health care workforce in the process of identifying and addressing inequities in local patient populations.¹⁷ Using electronic quality measure data, the PCED provides Veterans Integrated Service Network-level and facility-level performance on several metrics.¹⁸ The PCED had not been previously used at the CVVAMC, and few publications or quality improvement projects regarding its use have been reported by the VA Office of Health Equity. PCED helped identify disparities when comparing female to male patients in the prescribing of statin therapy for patients with CVD and statin therapy for patients with T2DM.

VA PHASER pharmacogenomic analyses provided an opportunity to expand this quality improvement project. Sanford Health and the VA collaborated on the PHASER program to offer free genetic testing for veterans. The program launched in 2019 and expanded to various VA sites, including CVVAMC in March 2023. This program has been extended to December 31, 2025.

The primary objective of this quality improvement project was to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk. Secondary outcomes included increased pharmacogenomic testing and the assessment of pharmacogenomic results related to statin therapy. This project was approved by the CVVAMC Pharmacy and Therapeutics Committee. The PCED was used to identify female veterans with T2DM and/or CVD without an active prescription for a statin between July and October 2023. A review of Computerized Patient Record System patient charts was completed to screen for prespecified inclusion and exclusion criteria. Veterans were included if they were assigned female at birth, were enrolled in care at CVVAMC, and had a diagnosis of T2DM or CVD (history of myocardial infarction, coronary bypass graft, percutaneous coronary intervention, or other revascularization in any setting).

Veterans were excluded if they were currently pregnant, trying to conceive, breastfeeding, had a T1DM diagnosis, had previously documented hypersensitivity to a statin, active liver failure or decompensated cirrhosis, previously documented statin-associated rhabdomyolysis or autoimmune myopathy, an active prescription for a proprotein convertase subtilisin/kexin type 9 inhibitor, or previously documented statin intolerance (defined as the inability to tolerate ≥ 3 statins, with ≥ 1 prescribed at low intensity or alternate-day dosing). The female veterans were compared to 2 comparators: the facility's male veterans and the VA national average, identified via the PCED.

Once a veteran was screened, they were telephoned between October 2023 and February 2024 and provided education on statin use and pharmacogenomic testing using a standardized note template. An order was placed for participants who provided verbal consent for pharmacogenomic testing. Those who agreed to statin initiation were referred to a clinical pharmacist practitioner (CPP) who contacted them at a later date to prescribe a statin following the recommendations of the 2019 ACC/AHA and 2023 ADA guidelines and pharmacogenomic testing, if applicable.^4,5,7 Appropriate monitoring and follow-up occurred at the discretion of each CPP. Data collection included: age, race, diagnoses (T2DM, CVD, or both), baseline lipid panel (total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein), hepatic function, name and dose of statin, reasons for declining statin therapy, and pharmacogenomic testing results related to SLCO1B1.

Results

At baseline in July 2023, 77.8% of female veterans with T2DM were prescribed a statin, which exceeded the national VA average (77.0%), but was below the rate for male veterans (78.7%) in the facility comparator group.17 Additionally, 82.2% of females with CVD were prescribed a statin, which was below the national VA average of 86.0% and the 84.9% of male veterans in the facility comparator group.17 The PCED identified 189 female veterans from July 2023 to October 2023 who may benefit from statin therapy. Thirty-three females met the exclusion criteria. Of the 156 included veterans, 129 (82.7%) were successfully contacted and 27 (17.3%) could not be reached by telephone after 3 attempts (Figure 1). The 129 female veterans contacted had a mean age of 59 years and the majority were Black (82.9%) (Table 1).

Primary Outcomes

Of the 129 contacted veterans, 31 (24.0%) had a non-VA statin prescription, 13 (10.1%) had an active VA statin prescription, and 85 (65.9%) did not have a statin prescription, despite being eligible. Statin adherence was confirmed with participants, and the medication list was updated accordingly.

Of the 85 veterans with no active statin therapy, 37 (43.5%) accepted a new statin prescription and 48 (56.5%) declined. There were various reasons provided for declining statin therapy: 17 participants (35.4%) declined due to concern for AEs (Table 2).

From July 2023 to March 2024, the percentage of female veterans with active statin therapy with T2DM increased from 77.8% to 79.0%. For those with active statin therapy with CVD, usage increased from 82.2% to 90.2%, which exceeded the national VA average and facility male comparator group (Figures 2 and 3).¹⁷

Secondary Outcomes

Seventy-one of 129 veterans (55.0%) gave verbal consent, and 47 (66.2%) completed the pharmacogenomic testing; 58 (45.0%) declined. Five veterans (10.6%) had a known SLCO1B1 allele variant present. One veteran required a change in statin therapy based on the results (eAppendix).

Discussion

This project aimed to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk and increase pharmacogenomic testing using the PCED and care managed by CPPs. The results of this quality improvement project illustrated that both metrics have improved at CVVAMC as a result of the intervention. The results in both metrics now exceed the PCED national VA average, and the CVD metric also exceeds that of the facility male comparator group. While there was only a 1.2% increase from July 2023 to March 2024 for patients with T2DM, there was an 8.0% increase for patients with CVD. Despite standardized education on statin use, more veterans declined therapy than accepted it, mostly due to concern for AEs. Recording the reasons for declining statin therapy offered valuable insight that can be used in additional discussions with veterans and clinicians.

Pharmacogenomics gives clinicians the unique opportunity to take a proactive approach to better predict drug responses, potentially allowing for less trial and error with medications, fewer AEs, greater trust in the clinician, and improved medication adherence. The CPPs incorporated pharmacogenomic testing into their practice, which led to identifying 5 SLCO1B1 gene abnormalities. The PCED served as a powerful tool for advancing equity-focused quality improvement initiatives on a local level and was crucial in prioritizing the detection of veterans potentially receiving suboptimal care.

Limitations

The nature of “cold calls” made it challenging to establish contact for inclusion in this study. An alternative to increase engagement could have been scheduled phone or face-to-face visits. While the use of the PCED was crucial, data did not account for statins listed in the non-VA medication list. All 31 patients with statins prescribed outside the VA had a start date added to provide the most accurate representation of the data moving forward.

Another limitation in this project was its small sample size and population. CVVAMC serves about 6200 female veterans, with roughly 63% identifying as Black. The preponderance of Black individuals (83%) in this project is typical for the female patient population at CVVAMC but may not reflect the demographics of other populations. Other limitations to this project consisted of scheduling conflicts. Appointments for laboratory draws at community-based outpatient clinics were subject to availability, which resulted in some delay in completion of pharmacogenomic testing.

Conclusions

CPPs can help reduce inequity in health care delivery. Increased incorporation of the PCED into regular practice within the VA is recommended to continue addressing sex disparities in statin use, diabetes control, blood pressure management, cancer screenings, and vaccination needs. CVVAMC plans to expand its use through another quality improvement project focused on reducing sex disparities in blood pressure management. Improving educational resources made available to veterans on the importance of statin therapy and potential to mitigate AEs through use of the VA PHASER program also would be helpful. This project successfully improved CVVAMC metrics for female veterans appropriately prescribed statin therapy and increased access to pharmacogenomic testing. Most importantly, it helped close the sex-based gap in CVD risk reduction care.

Cardiovascular disease (CVD) is the leading cause of death among women in the United States.¹ Most CVD is due to the buildup of plaque (ie, cholesterol, proteins, calcium, and inflammatory cells) in artery walls.² The plaque may lead to atherosclerotic cardiovascular disease (ASCVD), which includes coronary heart disease, cerebrovascular disease, peripheral artery disease, and aortic atherosclerotic disease.^2,3 Control and reduction of ASCVD risk factors, including high cholesterol levels, elevated blood pressure, insulin resistance, smoking, and a sedentary lifestyle, can contribute to a reduction in ASCVD morbidity and mortality.² People with type 2 diabetes mellitus (T2DM) have an increased prevalence of lipid abnormalities, contributing to their high risk of ASCVD.^4,5

The prescribing of statins (3-hydroxy-3-methyl-glutaryl-coenzmye A reductase inhibitors) is the cornerstone of lipid-lowering therapy and cardiovascular risk reduction for primary and secondary prevention of ASCVD.⁶ The American Diabetes Association (ADA) and American College of Cardiology/American Heart Association (ACC/AHA) recommend moderate- to high-intensity statins for primary prevention in patients with T2DM and high-intensity statins for secondary prevention in those with or without diabetes when not contraindicated.^4,5,7 Despite eligibility according to guideline recommendations, research predominantly shows that women are less likely to receive statin therapy; however, this trend is improving. ^[6,8-11] To explain the sex differences in statin use, Nanna et al found that there is a combination of women being offered statin therapy less frequently, declining therapy more frequently, and discontinuing treatment more frequently.¹¹ One possibility for discontinuing treatment could be statin-associated muscle symptoms (SAMS), which occur in about 10% of patients.¹² The incidence of adverse effects (AEs) may be related to the way statins are metabolized.

Pharmacogenomic testing is free for veterans through the US Department of Veterans Affairs (VA) PHASER program, which offers information and recommendations for a panel of 11 gene variants. The panel includes genes related to common medication classes such as anticoagulants, antiplatelets, proton pump inhibitors, nonsteroidal anti-inflammatory drugs, opioids, antidepressants, and statins. The VA PHASER panel includes the solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene, which is predominantly expressed in the liver and facilitates the hepatic uptake of most statins.^13,14 A reduced function of SLCO1B1 can lead to higher statin levels, resulting in increased concentrations that may potentially cause SAMS.^13,14 Some alleles associated with reduced function include SLCO1B1*5, *15, *23, *31, and *46 to *49, whereas others are associated with increased function, such as SLCO1B1 *14 and *20 (Appendix).¹⁵ Supporting evidence shows the SLCO1B1*5 nucleotide polymorphism increases plasma levels of simvastatin and atorvastatin, affecting effectiveness or toxicity. ¹³ Females tend to have a lower body weight and higher percentage of body fat compared with males, which might lead to higher concentrations of lipophilic drugs, including atorvastatin and simvastatin, which may be exacerbated by decreased function of SLCO1B1*5.¹⁵ With pharmacogenomic testing, therapeutic recommendations can be made to improve the overall safety and efficacy of statins, thus improving adherence using a patient-specific approach.^14,15

Methods

Carl Vinson VA Medical Center (CVVAMC) serves about 42,000 veterans in Central and South Georgia, of which about 15% are female. Of the female veterans enrolled in care, 63% identify as Black, 27% White, and 1.5% as Asian, American Indian/Alaska Native, or Native Hawaiian/Other Pacific Islander. The 2020 Veterans Chartbook report showed that female veterans and minority racial and ethnic groups had worse access to health care and higher mortality rates than their male and non-Hispanic White counterparts.¹⁶

The Primary Care Equity Dashboard (PCED) was developed to engage the VA health care workforce in the process of identifying and addressing inequities in local patient populations.¹⁷ Using electronic quality measure data, the PCED provides Veterans Integrated Service Network-level and facility-level performance on several metrics.¹⁸ The PCED had not been previously used at the CVVAMC, and few publications or quality improvement projects regarding its use have been reported by the VA Office of Health Equity. PCED helped identify disparities when comparing female to male patients in the prescribing of statin therapy for patients with CVD and statin therapy for patients with T2DM.

VA PHASER pharmacogenomic analyses provided an opportunity to expand this quality improvement project. Sanford Health and the VA collaborated on the PHASER program to offer free genetic testing for veterans. The program launched in 2019 and expanded to various VA sites, including CVVAMC in March 2023. This program has been extended to December 31, 2025.

The primary objective of this quality improvement project was to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk. Secondary outcomes included increased pharmacogenomic testing and the assessment of pharmacogenomic results related to statin therapy. This project was approved by the CVVAMC Pharmacy and Therapeutics Committee. The PCED was used to identify female veterans with T2DM and/or CVD without an active prescription for a statin between July and October 2023. A review of Computerized Patient Record System patient charts was completed to screen for prespecified inclusion and exclusion criteria. Veterans were included if they were assigned female at birth, were enrolled in care at CVVAMC, and had a diagnosis of T2DM or CVD (history of myocardial infarction, coronary bypass graft, percutaneous coronary intervention, or other revascularization in any setting).

Veterans were excluded if they were currently pregnant, trying to conceive, breastfeeding, had a T1DM diagnosis, had previously documented hypersensitivity to a statin, active liver failure or decompensated cirrhosis, previously documented statin-associated rhabdomyolysis or autoimmune myopathy, an active prescription for a proprotein convertase subtilisin/kexin type 9 inhibitor, or previously documented statin intolerance (defined as the inability to tolerate ≥ 3 statins, with ≥ 1 prescribed at low intensity or alternate-day dosing). The female veterans were compared to 2 comparators: the facility's male veterans and the VA national average, identified via the PCED.

Once a veteran was screened, they were telephoned between October 2023 and February 2024 and provided education on statin use and pharmacogenomic testing using a standardized note template. An order was placed for participants who provided verbal consent for pharmacogenomic testing. Those who agreed to statin initiation were referred to a clinical pharmacist practitioner (CPP) who contacted them at a later date to prescribe a statin following the recommendations of the 2019 ACC/AHA and 2023 ADA guidelines and pharmacogenomic testing, if applicable.^4,5,7 Appropriate monitoring and follow-up occurred at the discretion of each CPP. Data collection included: age, race, diagnoses (T2DM, CVD, or both), baseline lipid panel (total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein), hepatic function, name and dose of statin, reasons for declining statin therapy, and pharmacogenomic testing results related to SLCO1B1.

Results

At baseline in July 2023, 77.8% of female veterans with T2DM were prescribed a statin, which exceeded the national VA average (77.0%), but was below the rate for male veterans (78.7%) in the facility comparator group.17 Additionally, 82.2% of females with CVD were prescribed a statin, which was below the national VA average of 86.0% and the 84.9% of male veterans in the facility comparator group.17 The PCED identified 189 female veterans from July 2023 to October 2023 who may benefit from statin therapy. Thirty-three females met the exclusion criteria. Of the 156 included veterans, 129 (82.7%) were successfully contacted and 27 (17.3%) could not be reached by telephone after 3 attempts (Figure 1). The 129 female veterans contacted had a mean age of 59 years and the majority were Black (82.9%) (Table 1).

Primary Outcomes

Of the 129 contacted veterans, 31 (24.0%) had a non-VA statin prescription, 13 (10.1%) had an active VA statin prescription, and 85 (65.9%) did not have a statin prescription, despite being eligible. Statin adherence was confirmed with participants, and the medication list was updated accordingly.

Of the 85 veterans with no active statin therapy, 37 (43.5%) accepted a new statin prescription and 48 (56.5%) declined. There were various reasons provided for declining statin therapy: 17 participants (35.4%) declined due to concern for AEs (Table 2).

From July 2023 to March 2024, the percentage of female veterans with active statin therapy with T2DM increased from 77.8% to 79.0%. For those with active statin therapy with CVD, usage increased from 82.2% to 90.2%, which exceeded the national VA average and facility male comparator group (Figures 2 and 3).¹⁷

Secondary Outcomes

Seventy-one of 129 veterans (55.0%) gave verbal consent, and 47 (66.2%) completed the pharmacogenomic testing; 58 (45.0%) declined. Five veterans (10.6%) had a known SLCO1B1 allele variant present. One veteran required a change in statin therapy based on the results (eAppendix).

Discussion

This project aimed to increase statin prescribing among female veterans with T2DM and/or CVD to reduce cardiovascular risk and increase pharmacogenomic testing using the PCED and care managed by CPPs. The results of this quality improvement project illustrated that both metrics have improved at CVVAMC as a result of the intervention. The results in both metrics now exceed the PCED national VA average, and the CVD metric also exceeds that of the facility male comparator group. While there was only a 1.2% increase from July 2023 to March 2024 for patients with T2DM, there was an 8.0% increase for patients with CVD. Despite standardized education on statin use, more veterans declined therapy than accepted it, mostly due to concern for AEs. Recording the reasons for declining statin therapy offered valuable insight that can be used in additional discussions with veterans and clinicians.

Pharmacogenomics gives clinicians the unique opportunity to take a proactive approach to better predict drug responses, potentially allowing for less trial and error with medications, fewer AEs, greater trust in the clinician, and improved medication adherence. The CPPs incorporated pharmacogenomic testing into their practice, which led to identifying 5 SLCO1B1 gene abnormalities. The PCED served as a powerful tool for advancing equity-focused quality improvement initiatives on a local level and was crucial in prioritizing the detection of veterans potentially receiving suboptimal care.

Limitations

The nature of “cold calls” made it challenging to establish contact for inclusion in this study. An alternative to increase engagement could have been scheduled phone or face-to-face visits. While the use of the PCED was crucial, data did not account for statins listed in the non-VA medication list. All 31 patients with statins prescribed outside the VA had a start date added to provide the most accurate representation of the data moving forward.

Another limitation in this project was its small sample size and population. CVVAMC serves about 6200 female veterans, with roughly 63% identifying as Black. The preponderance of Black individuals (83%) in this project is typical for the female patient population at CVVAMC but may not reflect the demographics of other populations. Other limitations to this project consisted of scheduling conflicts. Appointments for laboratory draws at community-based outpatient clinics were subject to availability, which resulted in some delay in completion of pharmacogenomic testing.

Conclusions

CPPs can help reduce inequity in health care delivery. Increased incorporation of the PCED into regular practice within the VA is recommended to continue addressing sex disparities in statin use, diabetes control, blood pressure management, cancer screenings, and vaccination needs. CVVAMC plans to expand its use through another quality improvement project focused on reducing sex disparities in blood pressure management. Improving educational resources made available to veterans on the importance of statin therapy and potential to mitigate AEs through use of the VA PHASER program also would be helpful. This project successfully improved CVVAMC metrics for female veterans appropriately prescribed statin therapy and increased access to pharmacogenomic testing. Most importantly, it helped close the sex-based gap in CVD risk reduction care.

In 2020, the US Multi-Society Task Force (USMSTF) on Colorectal Cancer (CRC) increased the recommended colon polyp surveillance interval for 1 to 2 subcentimeter tubular adenomas from 5 to 10 years to 7 to 10 years.¹ This change was prompted by emerging research indicating that rates of CRC and advanced neoplasia among patients with a history of only 1 to 2 subcentimeter tubular adenomas are lower than initially estimated.^2,3 This extension provides an opportunity to increase endoscopy capacity and improve access to colonoscopies by retroactively applying the 2020 guidelines to surveillance interval recommendations made before their introduction. For example, based on the updated guidelines, patients previously recommended to undergo colon polyp surveillance colonoscopy 5 years after an index colonoscopy could extend their surveillance interval by 2 to 5 years. Increasing endoscopic capacity could address the growing demand for colonoscopies from new screening guidelines that reduced the age of initial CRC screening from 50 years to 45 years and the backlog of procedures due to COVID-19 restrictions.⁴

As part of a project to increase endoscopic capacity at the US Department of Veterans Affairs (VA) Pittsburgh Healthcare System (VAPHS), this study assessed the potential impact of retroactively applying the 2020 USMSTF polyp surveillance guidelines on endoscopic capacity. These results may be informative for other VA and private-sector health care systems seeking to identify strategies to improve endoscopy capacity.

Methods

VAPHS is an integrated health care system in the Veterans Health Administration (VHA) serving 85,000 patients across 8 health care institutions in Pennsylvania, Ohio, and West Virginia. VAPHS manages colorectal screening recommendations for patients receiving medical care in the health care system regardless of whether their prior colonoscopy was performed at VAPHS or external facilities. The VA maintains a national CRC screening and surveillance electronic medical record reminder that prompts health care practitioners to order colon polyp surveillance based on interval recommendations from the index colonoscopy. This study reviewed all patients from the VAPHS panel with a reminder to undergo colonoscopy for screening for CRC or surveillance of colon polyps within 12 months from September 1, 2022.

Among patients with a reminder, 3 investigators reviewed index colonoscopy and pathology reports to identify CRC risk category, colonoscopy indication, procedural quality, and recommended repeat colonoscopy interval. Per the USMSTF guidelines, patients with incomplete colonoscopy or pathology records, high-risk indications (ie, personal history of inflammatory bowel disease, personal history of CRC, or family history of CRC), or inadequate bowel preparation (Boston Bowel Preparation Score < 6) were excluded. Additionally, patients who had CRC screening or surveillance discontinued due to age or comorbidities, had completed a subsequent follow-up colonoscopy, or were deceased at the time of review were excluded.

Retroactive Interval Reclassification

Among eligible patients, this study compared the repeat colonoscopy interval recommended by the prior endoscopist with those from the 2020 USMSTF guidelines. In cases where the interval was documented as a range of years, the lower end was considered the recommendation. Similarly, the lower end of the range from the 2020 USMSTF guidelines was used for the reclassified surveillance interval. Years extended per patient were quantified relative to September 1, 2023 (ie, 1 year after the review date). For example, if the index colonoscopy was completed on September 1, 2016, the initial surveillance recommendation was 5 years, and the reclassified recommendation was 7 years, the interval extension beyond September 1, 2023, was 0 years.

Furthermore, because index surveillance recommendations are not always guideline concordant, the years extended per patient were calculated by harmonizing the index endoscopist’s recommendations with the guidelines at the time of the index colonoscopy.⁵ For example, if the index colonoscopy was completed on September 1, 2018, and the endoscopist recommended a 5-year follow-up for a patient with average risk for CRC, adequate bowel preparation, and no colorectal polyps, that patient is eligible to extend their colonoscopy to September 1, 2028, based on guideline recommendations at the time of index endoscopy recommending that the next colonoscopy occur in 10 years. In this analysis the 2012 USMSTF guidelines were applied to all index colonoscopies completed in 2021 or earlier to allow time for adoption of the 2020 guidelines. 

This project fulfilled a facility mandate to increase capacity to conduct endoscopic procedures. Institutional review board approval was not required by VAPHS policy relating to clinical operations projects. Approval for publication of clinical operations activity was obtained from the VAPHS facility director.

Results

Within 1 year of the September 1, 2022, review date, 637 patients receiving care at VAPHS had clinical reminders for an upcoming colonoscopy. Of these, 54 (8.4%) were already up to date or were deceased at the time of review. Of the 583 eligible patients, 96% were male, the median age was 74 years, the median index colonoscopy year was 2016, and 178 (30.5%) had an average-risk CRC screening indication at the index colonoscopy (Table).

Of the 583 patients due for colonoscopy, 331 (56.7%) had both colonoscopy and pathology reports available. The majority of those with incomplete records had the index colonoscopy completed outside VAPHS. Among these patients, 222 (67.0%) had adequate bowel preparation. Of those with adequate bowel preparation, 43 were not eligible for interval extension because of high-risk conditions and 13 were not eligible because there was no index surveillance interval recommendation from the index endoscopist. Of the patients due for colonoscopy, 166 (28.4%) were potentially eligible for surveillance interval extension (Figure).  

Sixty-five (39.2%) of the 166 patients had 1 to 2 subcentimeter tubular adenomas on their index colonoscopy. Sixty-two patients were eligible for interval extension to 7 years, but this only resulted in ≥ 1 year of extension beyond the review date for 36 (6% of all 583 patients due for colonoscopy). The 36 patients were extended 63 years. By harmonizing the index endoscopists’ surveillance interval recommendation with the guideline at the time of the index colonoscopy, 29 additional patients could have their colonoscopy extended by ≥ 1 year. Harmonization extended colonoscopy intervals by 93 years. Retroactively applying the 2020 USMSTF polyp surveillance guidelines and harmonizing recommendations to guidelines extended the time of index colonoscopy by 153 years.

Discussion

With retroactive application of the 2020 USMSTF polyp surveillance guidelines, 6% of patients due for an upcoming colonoscopy could extend their follow-up by ≥ 1 year by extending the surveillance interval for 1 to 2 subcentimeter tubular adenomas to 7 years. An additional 5% of patients could extend their interval by harmonizing the index endoscopist’s interval recommendation with polyp surveillance guidelines at the time of the index colonoscopy. These findings are consistent with the results of 2 studies that demonstrated that about 14% of patients due for colonoscopy could have their interval extended.^6,7 The current study enhances those insights by separating the contribution of 2020 USMSTF polyp surveillance guidelines from the contribution of harmonizing surveillance intervals with guidelines for other polyp histologies. This study found that there is an opportunity to improve endoscopic capacity by harmonizing recommendations with guidelines. This complements a 2023 study showing that even when knowledgeable about guidelines, clinicians do not necessarily follow recommendations.⁸ While this and previous research have identified that 11% to 14% of patients are eligible for extension, these individuals would also have to be willing to have their polyp surveillance intervals extended for there to be a real-world impact on endoscopic capacity. A 2024 study found that only 19% to 37% of patients with 1 to 2 small tubular adenomas were willing to have polyps surveillance interval extension.⁹ This suggests the actual effect on capacity may be even lower than reported.

Limitations

The overall impact of the 2020 USMSTF polyp surveillance guidelines on endoscopic capacity was blunted by the high prevalence of incomplete index colonoscopy records among the study population. Without data on bowel preparation quality or procedure indications, this study could not assess whether 43% of patients were eligible for surveillance interval extension. Most index colonoscopies with incomplete documentation were completed at community-care gastroenterology facilities. This high rate of incomplete documentation is likely generalizable to other VA health care systems—especially in the era of the Veterans Access, Choice, and Accountability Act of 2014, which increased veteran access to non-VA community care.¹⁰ Veterans due for colon polyp surveillance colonoscopies are more likely to have had their prior colonoscopy in community care compared with prior eras.¹¹ Furthermore, because the VHA is among the most established integrated health care systems offering primary and subspecialty care in the US, private sector health care systems may have even greater rates of care fragmentation for longitudinal CRC screening and colon polyp surveillance, as these systems have only begun to regionally integrate recently.^12,13

Another limitation is that nearly one-third of the individuals with documentation had inadequate bowel preparation for surveillance recommendations. This results in shorter surveillance follow-up colonoscopies and increases downstream demand for future colonoscopies. The low yield of extending colon polyp surveillance interval in this study emphasizes that improved efforts to obtain colonoscopy and pathology reports from community care, right-sizing the colon polyp surveillance intervals recommended by endoscopists, and improving quality of bowel preparation could have downstream health care system benefits in the future. These efforts could increase colonoscopy capacity at VA health care systems, thereby shortening colonoscopy wait times, decreasing fragmentation of care, and increasing the number of veterans who receive high-quality colonoscopies at VA health care systems.¹⁴

Conclusions

Eleven percent of patients in this study due for a colonoscopy could extend their follow-up by ≥ 1 year. About half of these extensions were directly due to the 2020 USMSTF polyp surveillance interval extension for 1 to 2 subcentimeter tubular adenomas. The rest resulted from harmonizing recommendations with guidelines at the time of the procedure. To determine whether retroactively applying polyp surveillance guidelines to follow-up interval recommendations will result in improved endoscopic capacity, health care system administrators should consider the degree of CRC screening care fragmentation in their patient population. Greater long-term gains in endoscopic capacity may be achieved by proactively supporting endoscopists in making guideline-concordant screening recommendations at the time of colonoscopy.

In 2020, the US Multi-Society Task Force (USMSTF) on Colorectal Cancer (CRC) increased the recommended colon polyp surveillance interval for 1 to 2 subcentimeter tubular adenomas from 5 to 10 years to 7 to 10 years.¹ This change was prompted by emerging research indicating that rates of CRC and advanced neoplasia among patients with a history of only 1 to 2 subcentimeter tubular adenomas are lower than initially estimated.^2,3 This extension provides an opportunity to increase endoscopy capacity and improve access to colonoscopies by retroactively applying the 2020 guidelines to surveillance interval recommendations made before their introduction. For example, based on the updated guidelines, patients previously recommended to undergo colon polyp surveillance colonoscopy 5 years after an index colonoscopy could extend their surveillance interval by 2 to 5 years. Increasing endoscopic capacity could address the growing demand for colonoscopies from new screening guidelines that reduced the age of initial CRC screening from 50 years to 45 years and the backlog of procedures due to COVID-19 restrictions.⁴

As part of a project to increase endoscopic capacity at the US Department of Veterans Affairs (VA) Pittsburgh Healthcare System (VAPHS), this study assessed the potential impact of retroactively applying the 2020 USMSTF polyp surveillance guidelines on endoscopic capacity. These results may be informative for other VA and private-sector health care systems seeking to identify strategies to improve endoscopy capacity.

Methods

VAPHS is an integrated health care system in the Veterans Health Administration (VHA) serving 85,000 patients across 8 health care institutions in Pennsylvania, Ohio, and West Virginia. VAPHS manages colorectal screening recommendations for patients receiving medical care in the health care system regardless of whether their prior colonoscopy was performed at VAPHS or external facilities. The VA maintains a national CRC screening and surveillance electronic medical record reminder that prompts health care practitioners to order colon polyp surveillance based on interval recommendations from the index colonoscopy. This study reviewed all patients from the VAPHS panel with a reminder to undergo colonoscopy for screening for CRC or surveillance of colon polyps within 12 months from September 1, 2022.

Among patients with a reminder, 3 investigators reviewed index colonoscopy and pathology reports to identify CRC risk category, colonoscopy indication, procedural quality, and recommended repeat colonoscopy interval. Per the USMSTF guidelines, patients with incomplete colonoscopy or pathology records, high-risk indications (ie, personal history of inflammatory bowel disease, personal history of CRC, or family history of CRC), or inadequate bowel preparation (Boston Bowel Preparation Score < 6) were excluded. Additionally, patients who had CRC screening or surveillance discontinued due to age or comorbidities, had completed a subsequent follow-up colonoscopy, or were deceased at the time of review were excluded.

Retroactive Interval Reclassification

Among eligible patients, this study compared the repeat colonoscopy interval recommended by the prior endoscopist with those from the 2020 USMSTF guidelines. In cases where the interval was documented as a range of years, the lower end was considered the recommendation. Similarly, the lower end of the range from the 2020 USMSTF guidelines was used for the reclassified surveillance interval. Years extended per patient were quantified relative to September 1, 2023 (ie, 1 year after the review date). For example, if the index colonoscopy was completed on September 1, 2016, the initial surveillance recommendation was 5 years, and the reclassified recommendation was 7 years, the interval extension beyond September 1, 2023, was 0 years.

Furthermore, because index surveillance recommendations are not always guideline concordant, the years extended per patient were calculated by harmonizing the index endoscopist’s recommendations with the guidelines at the time of the index colonoscopy.⁵ For example, if the index colonoscopy was completed on September 1, 2018, and the endoscopist recommended a 5-year follow-up for a patient with average risk for CRC, adequate bowel preparation, and no colorectal polyps, that patient is eligible to extend their colonoscopy to September 1, 2028, based on guideline recommendations at the time of index endoscopy recommending that the next colonoscopy occur in 10 years. In this analysis the 2012 USMSTF guidelines were applied to all index colonoscopies completed in 2021 or earlier to allow time for adoption of the 2020 guidelines. 

This project fulfilled a facility mandate to increase capacity to conduct endoscopic procedures. Institutional review board approval was not required by VAPHS policy relating to clinical operations projects. Approval for publication of clinical operations activity was obtained from the VAPHS facility director.

Results

Within 1 year of the September 1, 2022, review date, 637 patients receiving care at VAPHS had clinical reminders for an upcoming colonoscopy. Of these, 54 (8.4%) were already up to date or were deceased at the time of review. Of the 583 eligible patients, 96% were male, the median age was 74 years, the median index colonoscopy year was 2016, and 178 (30.5%) had an average-risk CRC screening indication at the index colonoscopy (Table).

Of the 583 patients due for colonoscopy, 331 (56.7%) had both colonoscopy and pathology reports available. The majority of those with incomplete records had the index colonoscopy completed outside VAPHS. Among these patients, 222 (67.0%) had adequate bowel preparation. Of those with adequate bowel preparation, 43 were not eligible for interval extension because of high-risk conditions and 13 were not eligible because there was no index surveillance interval recommendation from the index endoscopist. Of the patients due for colonoscopy, 166 (28.4%) were potentially eligible for surveillance interval extension (Figure).  

Sixty-five (39.2%) of the 166 patients had 1 to 2 subcentimeter tubular adenomas on their index colonoscopy. Sixty-two patients were eligible for interval extension to 7 years, but this only resulted in ≥ 1 year of extension beyond the review date for 36 (6% of all 583 patients due for colonoscopy). The 36 patients were extended 63 years. By harmonizing the index endoscopists’ surveillance interval recommendation with the guideline at the time of the index colonoscopy, 29 additional patients could have their colonoscopy extended by ≥ 1 year. Harmonization extended colonoscopy intervals by 93 years. Retroactively applying the 2020 USMSTF polyp surveillance guidelines and harmonizing recommendations to guidelines extended the time of index colonoscopy by 153 years.

Discussion

With retroactive application of the 2020 USMSTF polyp surveillance guidelines, 6% of patients due for an upcoming colonoscopy could extend their follow-up by ≥ 1 year by extending the surveillance interval for 1 to 2 subcentimeter tubular adenomas to 7 years. An additional 5% of patients could extend their interval by harmonizing the index endoscopist’s interval recommendation with polyp surveillance guidelines at the time of the index colonoscopy. These findings are consistent with the results of 2 studies that demonstrated that about 14% of patients due for colonoscopy could have their interval extended.^6,7 The current study enhances those insights by separating the contribution of 2020 USMSTF polyp surveillance guidelines from the contribution of harmonizing surveillance intervals with guidelines for other polyp histologies. This study found that there is an opportunity to improve endoscopic capacity by harmonizing recommendations with guidelines. This complements a 2023 study showing that even when knowledgeable about guidelines, clinicians do not necessarily follow recommendations.⁸ While this and previous research have identified that 11% to 14% of patients are eligible for extension, these individuals would also have to be willing to have their polyp surveillance intervals extended for there to be a real-world impact on endoscopic capacity. A 2024 study found that only 19% to 37% of patients with 1 to 2 small tubular adenomas were willing to have polyps surveillance interval extension.⁹ This suggests the actual effect on capacity may be even lower than reported.

Limitations

The overall impact of the 2020 USMSTF polyp surveillance guidelines on endoscopic capacity was blunted by the high prevalence of incomplete index colonoscopy records among the study population. Without data on bowel preparation quality or procedure indications, this study could not assess whether 43% of patients were eligible for surveillance interval extension. Most index colonoscopies with incomplete documentation were completed at community-care gastroenterology facilities. This high rate of incomplete documentation is likely generalizable to other VA health care systems—especially in the era of the Veterans Access, Choice, and Accountability Act of 2014, which increased veteran access to non-VA community care.¹⁰ Veterans due for colon polyp surveillance colonoscopies are more likely to have had their prior colonoscopy in community care compared with prior eras.¹¹ Furthermore, because the VHA is among the most established integrated health care systems offering primary and subspecialty care in the US, private sector health care systems may have even greater rates of care fragmentation for longitudinal CRC screening and colon polyp surveillance, as these systems have only begun to regionally integrate recently.^12,13

Another limitation is that nearly one-third of the individuals with documentation had inadequate bowel preparation for surveillance recommendations. This results in shorter surveillance follow-up colonoscopies and increases downstream demand for future colonoscopies. The low yield of extending colon polyp surveillance interval in this study emphasizes that improved efforts to obtain colonoscopy and pathology reports from community care, right-sizing the colon polyp surveillance intervals recommended by endoscopists, and improving quality of bowel preparation could have downstream health care system benefits in the future. These efforts could increase colonoscopy capacity at VA health care systems, thereby shortening colonoscopy wait times, decreasing fragmentation of care, and increasing the number of veterans who receive high-quality colonoscopies at VA health care systems.¹⁴

Conclusions

Eleven percent of patients in this study due for a colonoscopy could extend their follow-up by ≥ 1 year. About half of these extensions were directly due to the 2020 USMSTF polyp surveillance interval extension for 1 to 2 subcentimeter tubular adenomas. The rest resulted from harmonizing recommendations with guidelines at the time of the procedure. To determine whether retroactively applying polyp surveillance guidelines to follow-up interval recommendations will result in improved endoscopic capacity, health care system administrators should consider the degree of CRC screening care fragmentation in their patient population. Greater long-term gains in endoscopic capacity may be achieved by proactively supporting endoscopists in making guideline-concordant screening recommendations at the time of colonoscopy.

In 2020, the US Multi-Society Task Force (USMSTF) on Colorectal Cancer (CRC) increased the recommended colon polyp surveillance interval for 1 to 2 subcentimeter tubular adenomas from 5 to 10 years to 7 to 10 years.¹ This change was prompted by emerging research indicating that rates of CRC and advanced neoplasia among patients with a history of only 1 to 2 subcentimeter tubular adenomas are lower than initially estimated.^2,3 This extension provides an opportunity to increase endoscopy capacity and improve access to colonoscopies by retroactively applying the 2020 guidelines to surveillance interval recommendations made before their introduction. For example, based on the updated guidelines, patients previously recommended to undergo colon polyp surveillance colonoscopy 5 years after an index colonoscopy could extend their surveillance interval by 2 to 5 years. Increasing endoscopic capacity could address the growing demand for colonoscopies from new screening guidelines that reduced the age of initial CRC screening from 50 years to 45 years and the backlog of procedures due to COVID-19 restrictions.⁴

As part of a project to increase endoscopic capacity at the US Department of Veterans Affairs (VA) Pittsburgh Healthcare System (VAPHS), this study assessed the potential impact of retroactively applying the 2020 USMSTF polyp surveillance guidelines on endoscopic capacity. These results may be informative for other VA and private-sector health care systems seeking to identify strategies to improve endoscopy capacity.

Methods

VAPHS is an integrated health care system in the Veterans Health Administration (VHA) serving 85,000 patients across 8 health care institutions in Pennsylvania, Ohio, and West Virginia. VAPHS manages colorectal screening recommendations for patients receiving medical care in the health care system regardless of whether their prior colonoscopy was performed at VAPHS or external facilities. The VA maintains a national CRC screening and surveillance electronic medical record reminder that prompts health care practitioners to order colon polyp surveillance based on interval recommendations from the index colonoscopy. This study reviewed all patients from the VAPHS panel with a reminder to undergo colonoscopy for screening for CRC or surveillance of colon polyps within 12 months from September 1, 2022.

Among patients with a reminder, 3 investigators reviewed index colonoscopy and pathology reports to identify CRC risk category, colonoscopy indication, procedural quality, and recommended repeat colonoscopy interval. Per the USMSTF guidelines, patients with incomplete colonoscopy or pathology records, high-risk indications (ie, personal history of inflammatory bowel disease, personal history of CRC, or family history of CRC), or inadequate bowel preparation (Boston Bowel Preparation Score < 6) were excluded. Additionally, patients who had CRC screening or surveillance discontinued due to age or comorbidities, had completed a subsequent follow-up colonoscopy, or were deceased at the time of review were excluded.

Retroactive Interval Reclassification

Among eligible patients, this study compared the repeat colonoscopy interval recommended by the prior endoscopist with those from the 2020 USMSTF guidelines. In cases where the interval was documented as a range of years, the lower end was considered the recommendation. Similarly, the lower end of the range from the 2020 USMSTF guidelines was used for the reclassified surveillance interval. Years extended per patient were quantified relative to September 1, 2023 (ie, 1 year after the review date). For example, if the index colonoscopy was completed on September 1, 2016, the initial surveillance recommendation was 5 years, and the reclassified recommendation was 7 years, the interval extension beyond September 1, 2023, was 0 years.

Furthermore, because index surveillance recommendations are not always guideline concordant, the years extended per patient were calculated by harmonizing the index endoscopist’s recommendations with the guidelines at the time of the index colonoscopy.⁵ For example, if the index colonoscopy was completed on September 1, 2018, and the endoscopist recommended a 5-year follow-up for a patient with average risk for CRC, adequate bowel preparation, and no colorectal polyps, that patient is eligible to extend their colonoscopy to September 1, 2028, based on guideline recommendations at the time of index endoscopy recommending that the next colonoscopy occur in 10 years. In this analysis the 2012 USMSTF guidelines were applied to all index colonoscopies completed in 2021 or earlier to allow time for adoption of the 2020 guidelines. 

This project fulfilled a facility mandate to increase capacity to conduct endoscopic procedures. Institutional review board approval was not required by VAPHS policy relating to clinical operations projects. Approval for publication of clinical operations activity was obtained from the VAPHS facility director.

Results

Within 1 year of the September 1, 2022, review date, 637 patients receiving care at VAPHS had clinical reminders for an upcoming colonoscopy. Of these, 54 (8.4%) were already up to date or were deceased at the time of review. Of the 583 eligible patients, 96% were male, the median age was 74 years, the median index colonoscopy year was 2016, and 178 (30.5%) had an average-risk CRC screening indication at the index colonoscopy (Table).

Of the 583 patients due for colonoscopy, 331 (56.7%) had both colonoscopy and pathology reports available. The majority of those with incomplete records had the index colonoscopy completed outside VAPHS. Among these patients, 222 (67.0%) had adequate bowel preparation. Of those with adequate bowel preparation, 43 were not eligible for interval extension because of high-risk conditions and 13 were not eligible because there was no index surveillance interval recommendation from the index endoscopist. Of the patients due for colonoscopy, 166 (28.4%) were potentially eligible for surveillance interval extension (Figure).  

Sixty-five (39.2%) of the 166 patients had 1 to 2 subcentimeter tubular adenomas on their index colonoscopy. Sixty-two patients were eligible for interval extension to 7 years, but this only resulted in ≥ 1 year of extension beyond the review date for 36 (6% of all 583 patients due for colonoscopy). The 36 patients were extended 63 years. By harmonizing the index endoscopists’ surveillance interval recommendation with the guideline at the time of the index colonoscopy, 29 additional patients could have their colonoscopy extended by ≥ 1 year. Harmonization extended colonoscopy intervals by 93 years. Retroactively applying the 2020 USMSTF polyp surveillance guidelines and harmonizing recommendations to guidelines extended the time of index colonoscopy by 153 years.

Discussion

With retroactive application of the 2020 USMSTF polyp surveillance guidelines, 6% of patients due for an upcoming colonoscopy could extend their follow-up by ≥ 1 year by extending the surveillance interval for 1 to 2 subcentimeter tubular adenomas to 7 years. An additional 5% of patients could extend their interval by harmonizing the index endoscopist’s interval recommendation with polyp surveillance guidelines at the time of the index colonoscopy. These findings are consistent with the results of 2 studies that demonstrated that about 14% of patients due for colonoscopy could have their interval extended.^6,7 The current study enhances those insights by separating the contribution of 2020 USMSTF polyp surveillance guidelines from the contribution of harmonizing surveillance intervals with guidelines for other polyp histologies. This study found that there is an opportunity to improve endoscopic capacity by harmonizing recommendations with guidelines. This complements a 2023 study showing that even when knowledgeable about guidelines, clinicians do not necessarily follow recommendations.⁸ While this and previous research have identified that 11% to 14% of patients are eligible for extension, these individuals would also have to be willing to have their polyp surveillance intervals extended for there to be a real-world impact on endoscopic capacity. A 2024 study found that only 19% to 37% of patients with 1 to 2 small tubular adenomas were willing to have polyps surveillance interval extension.⁹ This suggests the actual effect on capacity may be even lower than reported.

Limitations

The overall impact of the 2020 USMSTF polyp surveillance guidelines on endoscopic capacity was blunted by the high prevalence of incomplete index colonoscopy records among the study population. Without data on bowel preparation quality or procedure indications, this study could not assess whether 43% of patients were eligible for surveillance interval extension. Most index colonoscopies with incomplete documentation were completed at community-care gastroenterology facilities. This high rate of incomplete documentation is likely generalizable to other VA health care systems—especially in the era of the Veterans Access, Choice, and Accountability Act of 2014, which increased veteran access to non-VA community care.¹⁰ Veterans due for colon polyp surveillance colonoscopies are more likely to have had their prior colonoscopy in community care compared with prior eras.¹¹ Furthermore, because the VHA is among the most established integrated health care systems offering primary and subspecialty care in the US, private sector health care systems may have even greater rates of care fragmentation for longitudinal CRC screening and colon polyp surveillance, as these systems have only begun to regionally integrate recently.^12,13

Another limitation is that nearly one-third of the individuals with documentation had inadequate bowel preparation for surveillance recommendations. This results in shorter surveillance follow-up colonoscopies and increases downstream demand for future colonoscopies. The low yield of extending colon polyp surveillance interval in this study emphasizes that improved efforts to obtain colonoscopy and pathology reports from community care, right-sizing the colon polyp surveillance intervals recommended by endoscopists, and improving quality of bowel preparation could have downstream health care system benefits in the future. These efforts could increase colonoscopy capacity at VA health care systems, thereby shortening colonoscopy wait times, decreasing fragmentation of care, and increasing the number of veterans who receive high-quality colonoscopies at VA health care systems.¹⁴

Conclusions

Eleven percent of patients in this study due for a colonoscopy could extend their follow-up by ≥ 1 year. About half of these extensions were directly due to the 2020 USMSTF polyp surveillance interval extension for 1 to 2 subcentimeter tubular adenomas. The rest resulted from harmonizing recommendations with guidelines at the time of the procedure. To determine whether retroactively applying polyp surveillance guidelines to follow-up interval recommendations will result in improved endoscopic capacity, health care system administrators should consider the degree of CRC screening care fragmentation in their patient population. Greater long-term gains in endoscopic capacity may be achieved by proactively supporting endoscopists in making guideline-concordant screening recommendations at the time of colonoscopy.

Intravascular large B-cell lymphoma (IVBCL) is an exceedingly rare aggressive form of non-Hodgkin lymphoma with tumor cells growing selectively in vascular lumina.¹ The annual incidence of IVBCL is fewer than 0.5 cases per 1,000,000 individuals worldwide.² Only about 500 known cases of IVBCL have been recorded in the literature,³ and it accounts for less than 1% of all lymphomas. It generally affects middle-aged to elderly individuals, with an average age at diagnosis of 70 years.² It has a predilection for men and commonly develops in individuals who are immunosuppressed.^3,4

Multiple variants of IVBCL have been described in the literature, with central nervous system and cutaneous involvement being the most classic findings.⁵ Bone marrow involvement with hepatosplenomegaly also has been noted in the literature.^6,7 Diagnosis of IVBCL and its variants requires a high index of suspicion, as the clinical manifestations and tissues involved typically are nonspecific and highly variable. Even in the classic variant of IVBCL, skin involvement is only reported in approximately half of cases.³ When present, cutaneous manifestations can range from nodules and violaceous plaques to induration and telangiectasias.³ Lymphadenopathy and lymphoma (leukemic) cells are not seen on a peripheral blood smear.^2,8,9

The lack of lymphadenopathy or identifiable leukemic cells in the peripheral blood presents a diagnostic dilemma, as sufficient information for accurate diagnosis must be obtained while minimizing invasive procedures and resource expenditure. Because IVBCL cells can reside in the vascular lumina of various organs, numerous biopsy sites have been proposed for diagnosis of lymphoma, including the bone marrow, skin, prostate, adrenal gland, brain, liver, and kidneys.¹⁰ While some studies have reported that the optimal diagnostic site is the bone marrow, skin biopsies are more routinely carried out, as they represent a convenient and cost-effective alternative to other more invasive techniques.^6,7,10 Studies have shown biopsy sensitivity values ranging from 77.8% to 83.3% for detection of IVBCL in normal-appearing skin, which is comparable to the sensitivities of a bone marrow biopsy.^7,8 Although skin biopsy of random sites has shown diagnostic efficacy, some studies have proposed that biopsies taken from hemangiomas and other hypervascular lesions can further improve diagnostic yield, as lymphoma cells often are present in capillaries of subcutaneous adipose tissue.^6,10,11 However, no obvious clinicopathologic differences were observed between IVBCL with and without involvement of a cutaneous hemangioma.¹¹

The purpose of this study was to determine the diagnostic efficacy of skin biopsies for detecting IVBCL at various body sites and to establish whether biopsies from hemangiomas yield higher diagnostic value.

Methods

A 66-year-old man recently died at our institution secondary to IVBCL. His disease course was characterized by multiple hospital admissions in a 6-month period for fever of unknown origin and tachycardia unresponsive to broad-spectrum antibiotics and systemic steroids. The patient declined over the course of 3 to 4 weeks with findings suggestive of lymphoma and tumor lysis syndrome, and he eventually developed shock, hypoxic respiratory failure, and acute renal failure. As imaging studies and examinations had not shown lymphadenopathy, bone marrow biopsy was performed, and dermatology was consulted to perform skin biopsies to evaluate for IVBCL. Both bone marrow biopsies and random skin biopsies from the abdomen showed large and atypical CD20+ B cells within select vascular lumina (Figure). No extravascular lymphoma cells were seen. Based on the bone marrow and skin biopsies, a diagnosis of IVBCL was made. Unfortunately, no progress was made clinically, and the patient was transitioned to comfort measures. Upon the patient’s death, his family expressed interest in participating in IVBCL research and agreed to a limited autopsy consisting of numerous skin biopsies to evaluate different body sites and biopsy types (normal skin vs hemangiomas) to ascertain whether diagnostic yield could be increased by performing selective biopsies of hemangiomas if IVBCL was suspected.

Twenty-four postmortem 4-mm punch biopsies containing subcutaneous adipose tissue were taken within 24 hours of the death of the patient before embalming. The biopsies were taken from all regions of the body except the head and neck for cosmetic preservation of the decedent. Eighteen of the biopsies were taken from random sites of normal-appearing skin; the remaining 6 were taken from clinically identifiable cherry hemangiomas (5 on the trunk and 1 on the thigh). There was a variable degree of livor mortis in the dependent areas of the body, which was included in the random biopsies from the back to ensure any pooling of dependent blood would not alter the findings.

A histopathologic examination by a board-certified dermatopathologist (M.P.) on a single hematoxylin-eosin–stained level was performed to evaluate each biopsy for superficial involvement and deep involvement by IVBCL. Superficial involvement was defined as dermal involvement at or above the level of the eccrine sweat glands; deep involvement was defined as dermal involvement beneath the eccrine sweat glands and all subcutaneous fat present. Skin and bone marrow biopsies used to make the original diagnosis prior to the patient’s death were reviewed, including CD20 immunohistochemistry for morphologic comparison to the study slides. Involvement was graded as 0 to 3+ (eTable).

Results

Results from all 24 biopsies are shown in the eTable. Twenty-two (91.7%) biopsies showed at least focal involvement by IVBCL. Nine (37.5%) biopsies showed more deep vs superficial involvement of the same site. On average, the 6 biopsies from clinically detected hemangiomas showed more involvement by IVBCL than the random biopsies (eFigures 1 and 2A). The superficial involvement of skin with a hemangioma showed an average score of 2.33 v 0.78 when compared with the superficial aspect of the random biopsies; the deep involvement of skin with a hemangioma showed an average score of 2.67 vs 1.16 when compared with the deep aspect of the random biopsies (eFigure 2B).

Comment

Intravascular large B-cell lymphoma is an aggressive malignancy that traditionally is difficult to diagnose. Many efforts have been made to improve detection and early diagnosis. As cutaneous involvement is common and sometimes the only sign of disease, dermatologists may be called upon to evaluate and biopsy patients with this suspected diagnosis. The purpose of our study was to improve diagnostic efficacy by methodically performing numerous biopsies and assessing the level of involvement of the superficial and deep skin as well as involvement of hemangiomas. The goal of this meticulous approach was to identify the highest-yield areas for biopsy with minimal impact on the patient. Our results showed that random skin biopsies are an effective way to identify IVBCL. Twenty-two (91.7%) biopsies contained at least focal lymphoma cells. Although the 2 biopsies that showed no tumor cells at all happened to both be from the left arm, this is believed to be coincidental. No discernable pattern was identified regarding involvement and anatomic region. Even though 20 (83.3%) biopsies showed superficial involvement, deep biopsy is essential, as 9 (37.5%) biopsies showed increased deep involvement compared to superficial involvement. Therefore, a deep punch biopsy is essential for maximum sensitivity.

Hemangiomas provide a potential target that could increase the sensitivity of a biopsy in the absence of clinical findings, when the disease in question is exclusively intravascular. The data gathered in this study support this idea, as biopsies from hemangiomas showed increased involvement compared to random biopsies, both superficially and deep (2.33 vs 0.78 and 2.67 vs 1.16, respectively). Interestingly, the hemangioma biopsy sites showed increased deep and superficial involvement, despite these typical cherry hemangiomas only involving the superficial dermis. One possible explanation for this is that the hemangiomas have larger-caliber feeder vessels with increased blood flow beneath them. It would then follow that this increased vasculature would increase the chances of identifying intravascular lymphoma cells. This finding further accentuates the need for a deep punch biopsy containing subcutaneous fat. 

Completing the study in the setting of an autopsy provided the advantage of being able to take numerous biopsies without increased harm to the patient. This extensive set of biopsies would not be reasonable to complete on a living patient. This study also has limitations. Although this patient did fall within the typical demographics for patients with IVBCL, the data were still limited to 1 patient. This autopsy format (on a patient whose cause of death was indeed IVBCL) also implies terminal disease, which may mean the patient had a larger disease burden than a living patient who would typically be biopsied. Although this increased disease burden may have increased the sensitivity of finding IVBCL in the biopsies of this study, this further emphasizes the importance of trying to determine any factors that could increase sensitivity in a living patient with a lower disease burden.

Conclusion

Skin biopsies can provide a sensitive, low-cost, and low-morbidity method to assess a patient for IVBCL. Though random skin biopsies can yield valuable information, deep, 4-mm punch biopsies of clinically identifiable hemangiomas may provide the highest sensitivity for IVBCL.

Intravascular large B-cell lymphoma (IVBCL) is an exceedingly rare aggressive form of non-Hodgkin lymphoma with tumor cells growing selectively in vascular lumina.¹ The annual incidence of IVBCL is fewer than 0.5 cases per 1,000,000 individuals worldwide.² Only about 500 known cases of IVBCL have been recorded in the literature,³ and it accounts for less than 1% of all lymphomas. It generally affects middle-aged to elderly individuals, with an average age at diagnosis of 70 years.² It has a predilection for men and commonly develops in individuals who are immunosuppressed.^3,4

Multiple variants of IVBCL have been described in the literature, with central nervous system and cutaneous involvement being the most classic findings.⁵ Bone marrow involvement with hepatosplenomegaly also has been noted in the literature.^6,7 Diagnosis of IVBCL and its variants requires a high index of suspicion, as the clinical manifestations and tissues involved typically are nonspecific and highly variable. Even in the classic variant of IVBCL, skin involvement is only reported in approximately half of cases.³ When present, cutaneous manifestations can range from nodules and violaceous plaques to induration and telangiectasias.³ Lymphadenopathy and lymphoma (leukemic) cells are not seen on a peripheral blood smear.^2,8,9

The lack of lymphadenopathy or identifiable leukemic cells in the peripheral blood presents a diagnostic dilemma, as sufficient information for accurate diagnosis must be obtained while minimizing invasive procedures and resource expenditure. Because IVBCL cells can reside in the vascular lumina of various organs, numerous biopsy sites have been proposed for diagnosis of lymphoma, including the bone marrow, skin, prostate, adrenal gland, brain, liver, and kidneys.¹⁰ While some studies have reported that the optimal diagnostic site is the bone marrow, skin biopsies are more routinely carried out, as they represent a convenient and cost-effective alternative to other more invasive techniques.^6,7,10 Studies have shown biopsy sensitivity values ranging from 77.8% to 83.3% for detection of IVBCL in normal-appearing skin, which is comparable to the sensitivities of a bone marrow biopsy.^7,8 Although skin biopsy of random sites has shown diagnostic efficacy, some studies have proposed that biopsies taken from hemangiomas and other hypervascular lesions can further improve diagnostic yield, as lymphoma cells often are present in capillaries of subcutaneous adipose tissue.^6,10,11 However, no obvious clinicopathologic differences were observed between IVBCL with and without involvement of a cutaneous hemangioma.¹¹

The purpose of this study was to determine the diagnostic efficacy of skin biopsies for detecting IVBCL at various body sites and to establish whether biopsies from hemangiomas yield higher diagnostic value.

Methods

A 66-year-old man recently died at our institution secondary to IVBCL. His disease course was characterized by multiple hospital admissions in a 6-month period for fever of unknown origin and tachycardia unresponsive to broad-spectrum antibiotics and systemic steroids. The patient declined over the course of 3 to 4 weeks with findings suggestive of lymphoma and tumor lysis syndrome, and he eventually developed shock, hypoxic respiratory failure, and acute renal failure. As imaging studies and examinations had not shown lymphadenopathy, bone marrow biopsy was performed, and dermatology was consulted to perform skin biopsies to evaluate for IVBCL. Both bone marrow biopsies and random skin biopsies from the abdomen showed large and atypical CD20+ B cells within select vascular lumina (Figure). No extravascular lymphoma cells were seen. Based on the bone marrow and skin biopsies, a diagnosis of IVBCL was made. Unfortunately, no progress was made clinically, and the patient was transitioned to comfort measures. Upon the patient’s death, his family expressed interest in participating in IVBCL research and agreed to a limited autopsy consisting of numerous skin biopsies to evaluate different body sites and biopsy types (normal skin vs hemangiomas) to ascertain whether diagnostic yield could be increased by performing selective biopsies of hemangiomas if IVBCL was suspected.

Twenty-four postmortem 4-mm punch biopsies containing subcutaneous adipose tissue were taken within 24 hours of the death of the patient before embalming. The biopsies were taken from all regions of the body except the head and neck for cosmetic preservation of the decedent. Eighteen of the biopsies were taken from random sites of normal-appearing skin; the remaining 6 were taken from clinically identifiable cherry hemangiomas (5 on the trunk and 1 on the thigh). There was a variable degree of livor mortis in the dependent areas of the body, which was included in the random biopsies from the back to ensure any pooling of dependent blood would not alter the findings.

A histopathologic examination by a board-certified dermatopathologist (M.P.) on a single hematoxylin-eosin–stained level was performed to evaluate each biopsy for superficial involvement and deep involvement by IVBCL. Superficial involvement was defined as dermal involvement at or above the level of the eccrine sweat glands; deep involvement was defined as dermal involvement beneath the eccrine sweat glands and all subcutaneous fat present. Skin and bone marrow biopsies used to make the original diagnosis prior to the patient’s death were reviewed, including CD20 immunohistochemistry for morphologic comparison to the study slides. Involvement was graded as 0 to 3+ (eTable).

Results

Results from all 24 biopsies are shown in the eTable. Twenty-two (91.7%) biopsies showed at least focal involvement by IVBCL. Nine (37.5%) biopsies showed more deep vs superficial involvement of the same site. On average, the 6 biopsies from clinically detected hemangiomas showed more involvement by IVBCL than the random biopsies (eFigures 1 and 2A). The superficial involvement of skin with a hemangioma showed an average score of 2.33 v 0.78 when compared with the superficial aspect of the random biopsies; the deep involvement of skin with a hemangioma showed an average score of 2.67 vs 1.16 when compared with the deep aspect of the random biopsies (eFigure 2B).

Comment

Intravascular large B-cell lymphoma is an aggressive malignancy that traditionally is difficult to diagnose. Many efforts have been made to improve detection and early diagnosis. As cutaneous involvement is common and sometimes the only sign of disease, dermatologists may be called upon to evaluate and biopsy patients with this suspected diagnosis. The purpose of our study was to improve diagnostic efficacy by methodically performing numerous biopsies and assessing the level of involvement of the superficial and deep skin as well as involvement of hemangiomas. The goal of this meticulous approach was to identify the highest-yield areas for biopsy with minimal impact on the patient. Our results showed that random skin biopsies are an effective way to identify IVBCL. Twenty-two (91.7%) biopsies contained at least focal lymphoma cells. Although the 2 biopsies that showed no tumor cells at all happened to both be from the left arm, this is believed to be coincidental. No discernable pattern was identified regarding involvement and anatomic region. Even though 20 (83.3%) biopsies showed superficial involvement, deep biopsy is essential, as 9 (37.5%) biopsies showed increased deep involvement compared to superficial involvement. Therefore, a deep punch biopsy is essential for maximum sensitivity.

Hemangiomas provide a potential target that could increase the sensitivity of a biopsy in the absence of clinical findings, when the disease in question is exclusively intravascular. The data gathered in this study support this idea, as biopsies from hemangiomas showed increased involvement compared to random biopsies, both superficially and deep (2.33 vs 0.78 and 2.67 vs 1.16, respectively). Interestingly, the hemangioma biopsy sites showed increased deep and superficial involvement, despite these typical cherry hemangiomas only involving the superficial dermis. One possible explanation for this is that the hemangiomas have larger-caliber feeder vessels with increased blood flow beneath them. It would then follow that this increased vasculature would increase the chances of identifying intravascular lymphoma cells. This finding further accentuates the need for a deep punch biopsy containing subcutaneous fat. 

Completing the study in the setting of an autopsy provided the advantage of being able to take numerous biopsies without increased harm to the patient. This extensive set of biopsies would not be reasonable to complete on a living patient. This study also has limitations. Although this patient did fall within the typical demographics for patients with IVBCL, the data were still limited to 1 patient. This autopsy format (on a patient whose cause of death was indeed IVBCL) also implies terminal disease, which may mean the patient had a larger disease burden than a living patient who would typically be biopsied. Although this increased disease burden may have increased the sensitivity of finding IVBCL in the biopsies of this study, this further emphasizes the importance of trying to determine any factors that could increase sensitivity in a living patient with a lower disease burden.

Conclusion

Skin biopsies can provide a sensitive, low-cost, and low-morbidity method to assess a patient for IVBCL. Though random skin biopsies can yield valuable information, deep, 4-mm punch biopsies of clinically identifiable hemangiomas may provide the highest sensitivity for IVBCL.

Intravascular large B-cell lymphoma (IVBCL) is an exceedingly rare aggressive form of non-Hodgkin lymphoma with tumor cells growing selectively in vascular lumina.¹ The annual incidence of IVBCL is fewer than 0.5 cases per 1,000,000 individuals worldwide.² Only about 500 known cases of IVBCL have been recorded in the literature,³ and it accounts for less than 1% of all lymphomas. It generally affects middle-aged to elderly individuals, with an average age at diagnosis of 70 years.² It has a predilection for men and commonly develops in individuals who are immunosuppressed.^3,4

Multiple variants of IVBCL have been described in the literature, with central nervous system and cutaneous involvement being the most classic findings.⁵ Bone marrow involvement with hepatosplenomegaly also has been noted in the literature.^6,7 Diagnosis of IVBCL and its variants requires a high index of suspicion, as the clinical manifestations and tissues involved typically are nonspecific and highly variable. Even in the classic variant of IVBCL, skin involvement is only reported in approximately half of cases.³ When present, cutaneous manifestations can range from nodules and violaceous plaques to induration and telangiectasias.³ Lymphadenopathy and lymphoma (leukemic) cells are not seen on a peripheral blood smear.^2,8,9

The lack of lymphadenopathy or identifiable leukemic cells in the peripheral blood presents a diagnostic dilemma, as sufficient information for accurate diagnosis must be obtained while minimizing invasive procedures and resource expenditure. Because IVBCL cells can reside in the vascular lumina of various organs, numerous biopsy sites have been proposed for diagnosis of lymphoma, including the bone marrow, skin, prostate, adrenal gland, brain, liver, and kidneys.¹⁰ While some studies have reported that the optimal diagnostic site is the bone marrow, skin biopsies are more routinely carried out, as they represent a convenient and cost-effective alternative to other more invasive techniques.^6,7,10 Studies have shown biopsy sensitivity values ranging from 77.8% to 83.3% for detection of IVBCL in normal-appearing skin, which is comparable to the sensitivities of a bone marrow biopsy.^7,8 Although skin biopsy of random sites has shown diagnostic efficacy, some studies have proposed that biopsies taken from hemangiomas and other hypervascular lesions can further improve diagnostic yield, as lymphoma cells often are present in capillaries of subcutaneous adipose tissue.^6,10,11 However, no obvious clinicopathologic differences were observed between IVBCL with and without involvement of a cutaneous hemangioma.¹¹

The purpose of this study was to determine the diagnostic efficacy of skin biopsies for detecting IVBCL at various body sites and to establish whether biopsies from hemangiomas yield higher diagnostic value.

Methods

A 66-year-old man recently died at our institution secondary to IVBCL. His disease course was characterized by multiple hospital admissions in a 6-month period for fever of unknown origin and tachycardia unresponsive to broad-spectrum antibiotics and systemic steroids. The patient declined over the course of 3 to 4 weeks with findings suggestive of lymphoma and tumor lysis syndrome, and he eventually developed shock, hypoxic respiratory failure, and acute renal failure. As imaging studies and examinations had not shown lymphadenopathy, bone marrow biopsy was performed, and dermatology was consulted to perform skin biopsies to evaluate for IVBCL. Both bone marrow biopsies and random skin biopsies from the abdomen showed large and atypical CD20+ B cells within select vascular lumina (Figure). No extravascular lymphoma cells were seen. Based on the bone marrow and skin biopsies, a diagnosis of IVBCL was made. Unfortunately, no progress was made clinically, and the patient was transitioned to comfort measures. Upon the patient’s death, his family expressed interest in participating in IVBCL research and agreed to a limited autopsy consisting of numerous skin biopsies to evaluate different body sites and biopsy types (normal skin vs hemangiomas) to ascertain whether diagnostic yield could be increased by performing selective biopsies of hemangiomas if IVBCL was suspected.

Twenty-four postmortem 4-mm punch biopsies containing subcutaneous adipose tissue were taken within 24 hours of the death of the patient before embalming. The biopsies were taken from all regions of the body except the head and neck for cosmetic preservation of the decedent. Eighteen of the biopsies were taken from random sites of normal-appearing skin; the remaining 6 were taken from clinically identifiable cherry hemangiomas (5 on the trunk and 1 on the thigh). There was a variable degree of livor mortis in the dependent areas of the body, which was included in the random biopsies from the back to ensure any pooling of dependent blood would not alter the findings.

A histopathologic examination by a board-certified dermatopathologist (M.P.) on a single hematoxylin-eosin–stained level was performed to evaluate each biopsy for superficial involvement and deep involvement by IVBCL. Superficial involvement was defined as dermal involvement at or above the level of the eccrine sweat glands; deep involvement was defined as dermal involvement beneath the eccrine sweat glands and all subcutaneous fat present. Skin and bone marrow biopsies used to make the original diagnosis prior to the patient’s death were reviewed, including CD20 immunohistochemistry for morphologic comparison to the study slides. Involvement was graded as 0 to 3+ (eTable).

Results

Results from all 24 biopsies are shown in the eTable. Twenty-two (91.7%) biopsies showed at least focal involvement by IVBCL. Nine (37.5%) biopsies showed more deep vs superficial involvement of the same site. On average, the 6 biopsies from clinically detected hemangiomas showed more involvement by IVBCL than the random biopsies (eFigures 1 and 2A). The superficial involvement of skin with a hemangioma showed an average score of 2.33 v 0.78 when compared with the superficial aspect of the random biopsies; the deep involvement of skin with a hemangioma showed an average score of 2.67 vs 1.16 when compared with the deep aspect of the random biopsies (eFigure 2B).

Comment

Intravascular large B-cell lymphoma is an aggressive malignancy that traditionally is difficult to diagnose. Many efforts have been made to improve detection and early diagnosis. As cutaneous involvement is common and sometimes the only sign of disease, dermatologists may be called upon to evaluate and biopsy patients with this suspected diagnosis. The purpose of our study was to improve diagnostic efficacy by methodically performing numerous biopsies and assessing the level of involvement of the superficial and deep skin as well as involvement of hemangiomas. The goal of this meticulous approach was to identify the highest-yield areas for biopsy with minimal impact on the patient. Our results showed that random skin biopsies are an effective way to identify IVBCL. Twenty-two (91.7%) biopsies contained at least focal lymphoma cells. Although the 2 biopsies that showed no tumor cells at all happened to both be from the left arm, this is believed to be coincidental. No discernable pattern was identified regarding involvement and anatomic region. Even though 20 (83.3%) biopsies showed superficial involvement, deep biopsy is essential, as 9 (37.5%) biopsies showed increased deep involvement compared to superficial involvement. Therefore, a deep punch biopsy is essential for maximum sensitivity.

Hemangiomas provide a potential target that could increase the sensitivity of a biopsy in the absence of clinical findings, when the disease in question is exclusively intravascular. The data gathered in this study support this idea, as biopsies from hemangiomas showed increased involvement compared to random biopsies, both superficially and deep (2.33 vs 0.78 and 2.67 vs 1.16, respectively). Interestingly, the hemangioma biopsy sites showed increased deep and superficial involvement, despite these typical cherry hemangiomas only involving the superficial dermis. One possible explanation for this is that the hemangiomas have larger-caliber feeder vessels with increased blood flow beneath them. It would then follow that this increased vasculature would increase the chances of identifying intravascular lymphoma cells. This finding further accentuates the need for a deep punch biopsy containing subcutaneous fat. 

Completing the study in the setting of an autopsy provided the advantage of being able to take numerous biopsies without increased harm to the patient. This extensive set of biopsies would not be reasonable to complete on a living patient. This study also has limitations. Although this patient did fall within the typical demographics for patients with IVBCL, the data were still limited to 1 patient. This autopsy format (on a patient whose cause of death was indeed IVBCL) also implies terminal disease, which may mean the patient had a larger disease burden than a living patient who would typically be biopsied. Although this increased disease burden may have increased the sensitivity of finding IVBCL in the biopsies of this study, this further emphasizes the importance of trying to determine any factors that could increase sensitivity in a living patient with a lower disease burden.

Conclusion

Skin biopsies can provide a sensitive, low-cost, and low-morbidity method to assess a patient for IVBCL. Though random skin biopsies can yield valuable information, deep, 4-mm punch biopsies of clinically identifiable hemangiomas may provide the highest sensitivity for IVBCL.

Abdominal aortic aneurysm (AAA) is a public health threat, with a global prevalence of 4.8% and a prevalence in males that increases with age, from 1.3% between ages 45 and 54 years to 12.5% between ages 75 and 84 years.¹ AAA is often asymptomatic until it ruptures and can become life-threatening, with mortality rates near 90% in the event of rupture with survival rates of about 50% to 70% for individuals with rupture who require urgent surgical intervention.^2,3 Males experience AAA at 4 times the rate of females.⁴

Previous research has found that the awareness of having an AAA causes anxiety that some have described as “living with a ticking time bomb.”⁵ Others reported worries and concerns about life’s fragility and mortality due to an AAA diagnosis.⁶ However, the psychological impact on the individuals’ quality of life (QoL) remains unclear, especially for individuals with a small AAA (< 5.5 cm).⁷ Factors such as age, male sex, smoking, family history, hypertension, carotid artery disease, and hypercholesterolemia have been strongly associated with increased growth rate and the risk of small AAA ruptures.^8,9

Most patients with a small AAA enter surveillance awaiting future repair and not only have the anxiety of living with an AAA despite the low risk of rupture, but also a worse QoL than those who have undergone repair.^10,11 However, data are sparse regarding the effects on QoL of knowing they have an AAA, whether repaired or not. This study sought to examine the impact an AAA diagnosis had on male QoL at the initial investigation and after 12 months.

Methods

This prospective study was examined and approved by the Veterans Affairs Northern California Health Care System (NCHCS) Institutional Review Board. It was conducted at the Sacramento US Department of Veterans Affairs (VA) Medical Center from January 1, 2019, to February 28, 2022. Patients were identified through the vascular clinic. One hundred sixteen patients with AAA were eligible and agreed to participate. Of these, 91 (78%) completed the survey at baseline and 12 months later. Participation was voluntary; written informed consent was obtained from every patient before completing the survey. This study included only male patients due to their higher prevalence than female patients.⁴ Patients were also eligible if they were aged > 18 years and had a previously known AAA that was being followed with a recorded clinical imaging study in the NCHCS vascular clinic. Patients were excluded if they were unable to return for their 12-month follow-up investigation, were incapable of giving informed consent, were unable to complete the 12-item short form health survey version 2 (SF-12v2), had a documented history of psychiatric illness, or refused to participate. The SF-12v2, an abbreviated version of the 36-item short form health survey (SF-36), is a generic health-related quality-of-life survey that measures 8 domains of general health status: general health (GH), physical functioning (PF), role limitations due to physical problems (RP), bodily pain (BP), vitality (VT), social functioning (SF), role emotional (RE), and mental health (MH). A higher number on the QoL scale indicates better QoL. The GH, PF, RP, and BP scales yield a physical component score (PCS), and the VT, SF, RE, and MH scales generate a mental component score (MCS). Although SF-12v2 has not been validated for patients with AAA, it has been widely used and validated to measure health-related QoL in cohorts of healthy and chronically ill individuals.^12,13

Analysis

Descriptive statistics, including means, SDs, frequency, percentages, 95% CIs, and correlations were calculated. The t test was used to analyze differences in mean scores. For continuous variables, such as SF-12v2 domains, PCS, and MCS, mean, SD, 95% CI, and range were determined. Comparisons were performed using X² or t test. P < .05 was considered statistically significant. Clinical risk factors, including age, race, body mass index (BMI), diabetes, hypertension, hyperlipidemia, coronary artery disease, cerebrovascular accident, myocardial infarction, and smoking status, were also recorded.

Results

Between January 1, 2019, and February 28, 2022, 91 patients were diagnosed with an AAA and completed the survey at the initial and 12-month investigations. Patients had a mean (SD) age of 76.0 (5.6) years (range, 64-93) and BMI of 29.7 (6.4). Comorbid diabetes was present in 31% of patients, hypertension in 75%, hyperlipidemia 66%, and coronary artery disease in 12% (Table 1). Most patients smoked tobacco: 71% indicated previous use and 22% were current users.

When comparing baseline vs 12-month follow-up, patients indicated a higher QoL in GH (3.2 vs 3.5, respectively; P < .05) and BP (3.1 vs 3.6, respectively; P < .05). No statistically significant difference was seen PF, RP, VT, SF, RE, MH, as well as PCS and MCS between baseline and follow-up with respect to QoL (P < .05). However, the 5 domains of SF-12v2: PF, RP, SF, RE, MH, and PCS had lower QoL scores at the 12-month follow-up when compared with baseline, but with no statistically significant difference between both investigations (Table 2).

Discussion

Previous studies have characterized the results of QoL measures as subjective because they are based on patient perceptions of their physical and psychological condition.^14,15 However, SF-36 and SF-12v2 responses provide a multifaceted account that encompasses the physical, psychological, and social aspects of QoL. Despite being the most widely used generic instrument in many fields of medicine, SF-36 is time consuming for clinicians who may prefer simpler and more time-efficient instruments.^16-18 The SF-12v2 not only imposes less burden on respondents but also generates accurate summary scores for patients physical and mental health.¹⁹

The replicability of SF-12v2 PCS and MCS scores has been demonstrated. In the United Kingdom, Jenkinson and Layte constructed SF-12v2 summary measures from a large scale dataset by sending the SF-36 and other questions on health and lifestyles to 9332 individuals and compared the results of the SF-36 and SF-12v2 across diverse patient groups (eg, Parkinson disease, congestive heart failure, sleep apnea, benign prostatic hypertrophy). Results from SF-36 PCS, SF-36 MCS, and PCS-12v2 (ρ, 0.94; P < .001) and SF-12v2 MCS (ρ, 0.96; P < .001) were found to be highly correlated, and also produced similar results, both in the community sample and across a variety of disease-specific groups.²⁰

The aim of this longitudinal observational study was to measure the QoL of males with an AAA ≥ 3.0 cm at baseline and 12 months later. The mean age of participants was 76 years, which aligns with previous research that found the prevalence of AAAs increased with age.¹ Study participants had a mean BMI of 29.7, which also supports previous research that indicated that obesity is independently associated with an AAA.²¹ Patients with an AAA and a history of smoking (former or current), hypertension, or hyperlipidemia had lower mean scores for 3 of 8 SF-12v2 domains at the 12-month follow-up.

These findings support previous research that indicated smoking is not only a very strong risk factor for the presence of an AAA but also associated with increased rates of expansion and the risk of rupture in patients with an AAA.²² Bath et al found that patients with an AAA compared to patients without an AAA were older (age 72.6 vs 69.8 years; P < .001), had a higher BMI (28.1 vs 27.0; P < .001), were more likely to be a current smoker (15.1% vs 5.2%; P < .001), and were more likely to have diabetes (18.8% vs 10.0%; P < .001), ischemic heart disease (12.2% vs 4.4%; P < .001), high cholesterol (53.2% vs 30.8%; P <. 001), previous stroke (6.1% vs 2.9%; P < .001), and a previous myocardial infarction (21.1% vs 5.8%; P < .001).²³ Lesjak et al found that men with AAA reported significantly lower scores in the domains of social functioning, pain, and general health 6 months after ultrasound compared with men without AAA.²⁴

Previous research indicates that patients with an AAA have a higher risk of cardiovascular diseases and comorbidities that may impact their perceived QoL. In a study assessing cardiovascular risk in 2323 patients with a small AAA, Bath et al found a high prevalence of coronary artery disease (44.9%), myocardial infarction (26.8%), heart failure (4.4%) and cerebrovascular accident (14.0%) which may have contributed to the decreased level of self-perceived QoL in these patients.²⁵

This aligned with a study by Golledge et al, who found that participants diagnosed with an AAA and peripheral artery disease not only had significantly poorer QoL scores in 5 SF-36 domains (PF, RP, GH, VT, and PCS)when compared with participants diagnosed with an AAA alone. They also had significantly poorer QoL scores in 7 domains of the SF-36 (PF, RP, GH, VT, SF, RE, and PCS) when compared with controls without an AAA.²⁶

Our analysis found that males with an AAA had a rise in SF-12v2 QoL scores from baseline to 12-month follow-up in the GH and BP domains. There was no statistically significant difference in QoL in the other 6 domains (PF, RP, VT, SF, RE, and MH) between the initial and 12-month investigations. Bath et al also found that men with an AAA had a transient reduction in mental QoL during the first year after the initial screening but returned to baseline.²³

Strengths and Limitations

This study is notable for its sample of patients who previously had a diagnosed AAA that were followed with a recorded clinical imaging study and the use of a validated QoL measure (SF-12v2) that provided virtually identical summary scores (PCS and MCS) as the SF-36.27 However, this study was limited by the brevity of the SF-12v2 instrument which made it difficult to extract sufficient reliable information for the 8 domains.²⁸ Subjective perception of patients is another limitation inherent to any QoL study. QoL scores were not available before the initial investigation. Measuring QoL at baseline and 12 months later does not capture the potential fluctuations and changes in QoL that the patient may experience some months later. Another limitation arises from the fact that the AAA patient population in the study included patients under surveillance and patients who had undergone repair.

Fourteen patients (15%) had received AAA repair: 10 had endovascular reconstruction and 4 had open surgical repair. Including patients with a previous AAA repair may have influenced reported QoL levels. Suckow et al performed a 2-phase study on 1008 patients, 351 (35%) were under surveillance and 657 (65%) had undergone repair. In that study, patients under AAA surveillance had worse emotional impact scores compared with patients with repair (22 vs 13; P < .001).¹¹ Additionally, the size of the abdominal aorta at the time of survey was not addressed in the study, which could constitute explanatory variables.

Conclusions

This study found higher QoL at 12-month follow-up compared to baseline in both the GH and BP domains of the SF-12v2 health survey for male veterans with an AAA. Periodic QoL assessments for patients with an AAA may be helpful in tracking QoL course, minimizing their physical and psychological concerns, and improving overall care and support. However, further research is necessary to assess the QoL of patients with an AAA who are under surveillance compared with those who had an aneurysm repair to accurately measure the impact of an AAA on QoL.

Abdominal aortic aneurysm (AAA) is a public health threat, with a global prevalence of 4.8% and a prevalence in males that increases with age, from 1.3% between ages 45 and 54 years to 12.5% between ages 75 and 84 years.¹ AAA is often asymptomatic until it ruptures and can become life-threatening, with mortality rates near 90% in the event of rupture with survival rates of about 50% to 70% for individuals with rupture who require urgent surgical intervention.^2,3 Males experience AAA at 4 times the rate of females.⁴

Previous research has found that the awareness of having an AAA causes anxiety that some have described as “living with a ticking time bomb.”⁵ Others reported worries and concerns about life’s fragility and mortality due to an AAA diagnosis.⁶ However, the psychological impact on the individuals’ quality of life (QoL) remains unclear, especially for individuals with a small AAA (< 5.5 cm).⁷ Factors such as age, male sex, smoking, family history, hypertension, carotid artery disease, and hypercholesterolemia have been strongly associated with increased growth rate and the risk of small AAA ruptures.^8,9

Most patients with a small AAA enter surveillance awaiting future repair and not only have the anxiety of living with an AAA despite the low risk of rupture, but also a worse QoL than those who have undergone repair.^10,11 However, data are sparse regarding the effects on QoL of knowing they have an AAA, whether repaired or not. This study sought to examine the impact an AAA diagnosis had on male QoL at the initial investigation and after 12 months.

Methods

This prospective study was examined and approved by the Veterans Affairs Northern California Health Care System (NCHCS) Institutional Review Board. It was conducted at the Sacramento US Department of Veterans Affairs (VA) Medical Center from January 1, 2019, to February 28, 2022. Patients were identified through the vascular clinic. One hundred sixteen patients with AAA were eligible and agreed to participate. Of these, 91 (78%) completed the survey at baseline and 12 months later. Participation was voluntary; written informed consent was obtained from every patient before completing the survey. This study included only male patients due to their higher prevalence than female patients.⁴ Patients were also eligible if they were aged > 18 years and had a previously known AAA that was being followed with a recorded clinical imaging study in the NCHCS vascular clinic. Patients were excluded if they were unable to return for their 12-month follow-up investigation, were incapable of giving informed consent, were unable to complete the 12-item short form health survey version 2 (SF-12v2), had a documented history of psychiatric illness, or refused to participate. The SF-12v2, an abbreviated version of the 36-item short form health survey (SF-36), is a generic health-related quality-of-life survey that measures 8 domains of general health status: general health (GH), physical functioning (PF), role limitations due to physical problems (RP), bodily pain (BP), vitality (VT), social functioning (SF), role emotional (RE), and mental health (MH). A higher number on the QoL scale indicates better QoL. The GH, PF, RP, and BP scales yield a physical component score (PCS), and the VT, SF, RE, and MH scales generate a mental component score (MCS). Although SF-12v2 has not been validated for patients with AAA, it has been widely used and validated to measure health-related QoL in cohorts of healthy and chronically ill individuals.^12,13

Analysis

Descriptive statistics, including means, SDs, frequency, percentages, 95% CIs, and correlations were calculated. The t test was used to analyze differences in mean scores. For continuous variables, such as SF-12v2 domains, PCS, and MCS, mean, SD, 95% CI, and range were determined. Comparisons were performed using X² or t test. P < .05 was considered statistically significant. Clinical risk factors, including age, race, body mass index (BMI), diabetes, hypertension, hyperlipidemia, coronary artery disease, cerebrovascular accident, myocardial infarction, and smoking status, were also recorded.

Results

Between January 1, 2019, and February 28, 2022, 91 patients were diagnosed with an AAA and completed the survey at the initial and 12-month investigations. Patients had a mean (SD) age of 76.0 (5.6) years (range, 64-93) and BMI of 29.7 (6.4). Comorbid diabetes was present in 31% of patients, hypertension in 75%, hyperlipidemia 66%, and coronary artery disease in 12% (Table 1). Most patients smoked tobacco: 71% indicated previous use and 22% were current users.

When comparing baseline vs 12-month follow-up, patients indicated a higher QoL in GH (3.2 vs 3.5, respectively; P < .05) and BP (3.1 vs 3.6, respectively; P < .05). No statistically significant difference was seen PF, RP, VT, SF, RE, MH, as well as PCS and MCS between baseline and follow-up with respect to QoL (P < .05). However, the 5 domains of SF-12v2: PF, RP, SF, RE, MH, and PCS had lower QoL scores at the 12-month follow-up when compared with baseline, but with no statistically significant difference between both investigations (Table 2).

Discussion

Previous studies have characterized the results of QoL measures as subjective because they are based on patient perceptions of their physical and psychological condition.^14,15 However, SF-36 and SF-12v2 responses provide a multifaceted account that encompasses the physical, psychological, and social aspects of QoL. Despite being the most widely used generic instrument in many fields of medicine, SF-36 is time consuming for clinicians who may prefer simpler and more time-efficient instruments.^16-18 The SF-12v2 not only imposes less burden on respondents but also generates accurate summary scores for patients physical and mental health.¹⁹

The replicability of SF-12v2 PCS and MCS scores has been demonstrated. In the United Kingdom, Jenkinson and Layte constructed SF-12v2 summary measures from a large scale dataset by sending the SF-36 and other questions on health and lifestyles to 9332 individuals and compared the results of the SF-36 and SF-12v2 across diverse patient groups (eg, Parkinson disease, congestive heart failure, sleep apnea, benign prostatic hypertrophy). Results from SF-36 PCS, SF-36 MCS, and PCS-12v2 (ρ, 0.94; P < .001) and SF-12v2 MCS (ρ, 0.96; P < .001) were found to be highly correlated, and also produced similar results, both in the community sample and across a variety of disease-specific groups.²⁰

The aim of this longitudinal observational study was to measure the QoL of males with an AAA ≥ 3.0 cm at baseline and 12 months later. The mean age of participants was 76 years, which aligns with previous research that found the prevalence of AAAs increased with age.¹ Study participants had a mean BMI of 29.7, which also supports previous research that indicated that obesity is independently associated with an AAA.²¹ Patients with an AAA and a history of smoking (former or current), hypertension, or hyperlipidemia had lower mean scores for 3 of 8 SF-12v2 domains at the 12-month follow-up.

These findings support previous research that indicated smoking is not only a very strong risk factor for the presence of an AAA but also associated with increased rates of expansion and the risk of rupture in patients with an AAA.²² Bath et al found that patients with an AAA compared to patients without an AAA were older (age 72.6 vs 69.8 years; P < .001), had a higher BMI (28.1 vs 27.0; P < .001), were more likely to be a current smoker (15.1% vs 5.2%; P < .001), and were more likely to have diabetes (18.8% vs 10.0%; P < .001), ischemic heart disease (12.2% vs 4.4%; P < .001), high cholesterol (53.2% vs 30.8%; P <. 001), previous stroke (6.1% vs 2.9%; P < .001), and a previous myocardial infarction (21.1% vs 5.8%; P < .001).²³ Lesjak et al found that men with AAA reported significantly lower scores in the domains of social functioning, pain, and general health 6 months after ultrasound compared with men without AAA.²⁴

Previous research indicates that patients with an AAA have a higher risk of cardiovascular diseases and comorbidities that may impact their perceived QoL. In a study assessing cardiovascular risk in 2323 patients with a small AAA, Bath et al found a high prevalence of coronary artery disease (44.9%), myocardial infarction (26.8%), heart failure (4.4%) and cerebrovascular accident (14.0%) which may have contributed to the decreased level of self-perceived QoL in these patients.²⁵

This aligned with a study by Golledge et al, who found that participants diagnosed with an AAA and peripheral artery disease not only had significantly poorer QoL scores in 5 SF-36 domains (PF, RP, GH, VT, and PCS)when compared with participants diagnosed with an AAA alone. They also had significantly poorer QoL scores in 7 domains of the SF-36 (PF, RP, GH, VT, SF, RE, and PCS) when compared with controls without an AAA.²⁶

Our analysis found that males with an AAA had a rise in SF-12v2 QoL scores from baseline to 12-month follow-up in the GH and BP domains. There was no statistically significant difference in QoL in the other 6 domains (PF, RP, VT, SF, RE, and MH) between the initial and 12-month investigations. Bath et al also found that men with an AAA had a transient reduction in mental QoL during the first year after the initial screening but returned to baseline.²³

Strengths and Limitations

This study is notable for its sample of patients who previously had a diagnosed AAA that were followed with a recorded clinical imaging study and the use of a validated QoL measure (SF-12v2) that provided virtually identical summary scores (PCS and MCS) as the SF-36.27 However, this study was limited by the brevity of the SF-12v2 instrument which made it difficult to extract sufficient reliable information for the 8 domains.²⁸ Subjective perception of patients is another limitation inherent to any QoL study. QoL scores were not available before the initial investigation. Measuring QoL at baseline and 12 months later does not capture the potential fluctuations and changes in QoL that the patient may experience some months later. Another limitation arises from the fact that the AAA patient population in the study included patients under surveillance and patients who had undergone repair.

Fourteen patients (15%) had received AAA repair: 10 had endovascular reconstruction and 4 had open surgical repair. Including patients with a previous AAA repair may have influenced reported QoL levels. Suckow et al performed a 2-phase study on 1008 patients, 351 (35%) were under surveillance and 657 (65%) had undergone repair. In that study, patients under AAA surveillance had worse emotional impact scores compared with patients with repair (22 vs 13; P < .001).¹¹ Additionally, the size of the abdominal aorta at the time of survey was not addressed in the study, which could constitute explanatory variables.

Conclusions

This study found higher QoL at 12-month follow-up compared to baseline in both the GH and BP domains of the SF-12v2 health survey for male veterans with an AAA. Periodic QoL assessments for patients with an AAA may be helpful in tracking QoL course, minimizing their physical and psychological concerns, and improving overall care and support. However, further research is necessary to assess the QoL of patients with an AAA who are under surveillance compared with those who had an aneurysm repair to accurately measure the impact of an AAA on QoL.

Abdominal aortic aneurysm (AAA) is a public health threat, with a global prevalence of 4.8% and a prevalence in males that increases with age, from 1.3% between ages 45 and 54 years to 12.5% between ages 75 and 84 years.¹ AAA is often asymptomatic until it ruptures and can become life-threatening, with mortality rates near 90% in the event of rupture with survival rates of about 50% to 70% for individuals with rupture who require urgent surgical intervention.^2,3 Males experience AAA at 4 times the rate of females.⁴

Previous research has found that the awareness of having an AAA causes anxiety that some have described as “living with a ticking time bomb.”⁵ Others reported worries and concerns about life’s fragility and mortality due to an AAA diagnosis.⁶ However, the psychological impact on the individuals’ quality of life (QoL) remains unclear, especially for individuals with a small AAA (< 5.5 cm).⁷ Factors such as age, male sex, smoking, family history, hypertension, carotid artery disease, and hypercholesterolemia have been strongly associated with increased growth rate and the risk of small AAA ruptures.^8,9

Most patients with a small AAA enter surveillance awaiting future repair and not only have the anxiety of living with an AAA despite the low risk of rupture, but also a worse QoL than those who have undergone repair.^10,11 However, data are sparse regarding the effects on QoL of knowing they have an AAA, whether repaired or not. This study sought to examine the impact an AAA diagnosis had on male QoL at the initial investigation and after 12 months.

Methods

This prospective study was examined and approved by the Veterans Affairs Northern California Health Care System (NCHCS) Institutional Review Board. It was conducted at the Sacramento US Department of Veterans Affairs (VA) Medical Center from January 1, 2019, to February 28, 2022. Patients were identified through the vascular clinic. One hundred sixteen patients with AAA were eligible and agreed to participate. Of these, 91 (78%) completed the survey at baseline and 12 months later. Participation was voluntary; written informed consent was obtained from every patient before completing the survey. This study included only male patients due to their higher prevalence than female patients.⁴ Patients were also eligible if they were aged > 18 years and had a previously known AAA that was being followed with a recorded clinical imaging study in the NCHCS vascular clinic. Patients were excluded if they were unable to return for their 12-month follow-up investigation, were incapable of giving informed consent, were unable to complete the 12-item short form health survey version 2 (SF-12v2), had a documented history of psychiatric illness, or refused to participate. The SF-12v2, an abbreviated version of the 36-item short form health survey (SF-36), is a generic health-related quality-of-life survey that measures 8 domains of general health status: general health (GH), physical functioning (PF), role limitations due to physical problems (RP), bodily pain (BP), vitality (VT), social functioning (SF), role emotional (RE), and mental health (MH). A higher number on the QoL scale indicates better QoL. The GH, PF, RP, and BP scales yield a physical component score (PCS), and the VT, SF, RE, and MH scales generate a mental component score (MCS). Although SF-12v2 has not been validated for patients with AAA, it has been widely used and validated to measure health-related QoL in cohorts of healthy and chronically ill individuals.^12,13

Analysis

Descriptive statistics, including means, SDs, frequency, percentages, 95% CIs, and correlations were calculated. The t test was used to analyze differences in mean scores. For continuous variables, such as SF-12v2 domains, PCS, and MCS, mean, SD, 95% CI, and range were determined. Comparisons were performed using X² or t test. P < .05 was considered statistically significant. Clinical risk factors, including age, race, body mass index (BMI), diabetes, hypertension, hyperlipidemia, coronary artery disease, cerebrovascular accident, myocardial infarction, and smoking status, were also recorded.

Results

Between January 1, 2019, and February 28, 2022, 91 patients were diagnosed with an AAA and completed the survey at the initial and 12-month investigations. Patients had a mean (SD) age of 76.0 (5.6) years (range, 64-93) and BMI of 29.7 (6.4). Comorbid diabetes was present in 31% of patients, hypertension in 75%, hyperlipidemia 66%, and coronary artery disease in 12% (Table 1). Most patients smoked tobacco: 71% indicated previous use and 22% were current users.

When comparing baseline vs 12-month follow-up, patients indicated a higher QoL in GH (3.2 vs 3.5, respectively; P < .05) and BP (3.1 vs 3.6, respectively; P < .05). No statistically significant difference was seen PF, RP, VT, SF, RE, MH, as well as PCS and MCS between baseline and follow-up with respect to QoL (P < .05). However, the 5 domains of SF-12v2: PF, RP, SF, RE, MH, and PCS had lower QoL scores at the 12-month follow-up when compared with baseline, but with no statistically significant difference between both investigations (Table 2).

Discussion

Previous studies have characterized the results of QoL measures as subjective because they are based on patient perceptions of their physical and psychological condition.^14,15 However, SF-36 and SF-12v2 responses provide a multifaceted account that encompasses the physical, psychological, and social aspects of QoL. Despite being the most widely used generic instrument in many fields of medicine, SF-36 is time consuming for clinicians who may prefer simpler and more time-efficient instruments.^16-18 The SF-12v2 not only imposes less burden on respondents but also generates accurate summary scores for patients physical and mental health.¹⁹

The replicability of SF-12v2 PCS and MCS scores has been demonstrated. In the United Kingdom, Jenkinson and Layte constructed SF-12v2 summary measures from a large scale dataset by sending the SF-36 and other questions on health and lifestyles to 9332 individuals and compared the results of the SF-36 and SF-12v2 across diverse patient groups (eg, Parkinson disease, congestive heart failure, sleep apnea, benign prostatic hypertrophy). Results from SF-36 PCS, SF-36 MCS, and PCS-12v2 (ρ, 0.94; P < .001) and SF-12v2 MCS (ρ, 0.96; P < .001) were found to be highly correlated, and also produced similar results, both in the community sample and across a variety of disease-specific groups.²⁰

The aim of this longitudinal observational study was to measure the QoL of males with an AAA ≥ 3.0 cm at baseline and 12 months later. The mean age of participants was 76 years, which aligns with previous research that found the prevalence of AAAs increased with age.¹ Study participants had a mean BMI of 29.7, which also supports previous research that indicated that obesity is independently associated with an AAA.²¹ Patients with an AAA and a history of smoking (former or current), hypertension, or hyperlipidemia had lower mean scores for 3 of 8 SF-12v2 domains at the 12-month follow-up.

These findings support previous research that indicated smoking is not only a very strong risk factor for the presence of an AAA but also associated with increased rates of expansion and the risk of rupture in patients with an AAA.²² Bath et al found that patients with an AAA compared to patients without an AAA were older (age 72.6 vs 69.8 years; P < .001), had a higher BMI (28.1 vs 27.0; P < .001), were more likely to be a current smoker (15.1% vs 5.2%; P < .001), and were more likely to have diabetes (18.8% vs 10.0%; P < .001), ischemic heart disease (12.2% vs 4.4%; P < .001), high cholesterol (53.2% vs 30.8%; P <. 001), previous stroke (6.1% vs 2.9%; P < .001), and a previous myocardial infarction (21.1% vs 5.8%; P < .001).²³ Lesjak et al found that men with AAA reported significantly lower scores in the domains of social functioning, pain, and general health 6 months after ultrasound compared with men without AAA.²⁴

Previous research indicates that patients with an AAA have a higher risk of cardiovascular diseases and comorbidities that may impact their perceived QoL. In a study assessing cardiovascular risk in 2323 patients with a small AAA, Bath et al found a high prevalence of coronary artery disease (44.9%), myocardial infarction (26.8%), heart failure (4.4%) and cerebrovascular accident (14.0%) which may have contributed to the decreased level of self-perceived QoL in these patients.²⁵

This aligned with a study by Golledge et al, who found that participants diagnosed with an AAA and peripheral artery disease not only had significantly poorer QoL scores in 5 SF-36 domains (PF, RP, GH, VT, and PCS)when compared with participants diagnosed with an AAA alone. They also had significantly poorer QoL scores in 7 domains of the SF-36 (PF, RP, GH, VT, SF, RE, and PCS) when compared with controls without an AAA.²⁶

Our analysis found that males with an AAA had a rise in SF-12v2 QoL scores from baseline to 12-month follow-up in the GH and BP domains. There was no statistically significant difference in QoL in the other 6 domains (PF, RP, VT, SF, RE, and MH) between the initial and 12-month investigations. Bath et al also found that men with an AAA had a transient reduction in mental QoL during the first year after the initial screening but returned to baseline.²³

Strengths and Limitations

This study is notable for its sample of patients who previously had a diagnosed AAA that were followed with a recorded clinical imaging study and the use of a validated QoL measure (SF-12v2) that provided virtually identical summary scores (PCS and MCS) as the SF-36.27 However, this study was limited by the brevity of the SF-12v2 instrument which made it difficult to extract sufficient reliable information for the 8 domains.²⁸ Subjective perception of patients is another limitation inherent to any QoL study. QoL scores were not available before the initial investigation. Measuring QoL at baseline and 12 months later does not capture the potential fluctuations and changes in QoL that the patient may experience some months later. Another limitation arises from the fact that the AAA patient population in the study included patients under surveillance and patients who had undergone repair.

Fourteen patients (15%) had received AAA repair: 10 had endovascular reconstruction and 4 had open surgical repair. Including patients with a previous AAA repair may have influenced reported QoL levels. Suckow et al performed a 2-phase study on 1008 patients, 351 (35%) were under surveillance and 657 (65%) had undergone repair. In that study, patients under AAA surveillance had worse emotional impact scores compared with patients with repair (22 vs 13; P < .001).¹¹ Additionally, the size of the abdominal aorta at the time of survey was not addressed in the study, which could constitute explanatory variables.

Conclusions

This study found higher QoL at 12-month follow-up compared to baseline in both the GH and BP domains of the SF-12v2 health survey for male veterans with an AAA. Periodic QoL assessments for patients with an AAA may be helpful in tracking QoL course, minimizing their physical and psychological concerns, and improving overall care and support. However, further research is necessary to assess the QoL of patients with an AAA who are under surveillance compared with those who had an aneurysm repair to accurately measure the impact of an AAA on QoL.

Background

Urothelial carcinoma (UC) is among the top 10 frequently diagnosed cancers in the world. Mutations in FGFR3, ARID1A, and TP53 are well documented as being some of the most frequent mutations found in UC. Despite advances in treatment, survival outcomes remain poor, especially in advanced stages. To promote future pharmacotherapeutic development, the molecular understanding of UC needs to be continually updated using more recently available databases.

Methods

This study utilizes the AACR Project GENIE database from the American Association for Cancer Research to explore the mutational profiles of patients with UC. Gene mutation frequencies were calculated, and two Kaplan-Meier curves were drawn for each gene, showing one curve for patients with the mutation and one for those without. Log-Rank tests were calculated with subsequent FDR (Benjamini–Hochberg) correction applied to account for multiple hypothesis testing. Data was analyzed using R 4.4.2 and statistical significance was set at α = 0.05.

Results

In this study, 4525 patients had histology consistent with UC. The 5 most common mutations were TERT (n = 1714, 37.9%), TP53 (n = 1689, 37.3%), KDM6A (n = 1091, 24.1%), ARID1A (n = 872, 19.3%), and FGFR3 (n = 762, 16.8%). Mutations associated with differential survival outcomes included ERCC2 (mutated n = 387, wild type n = 3751, p < 0.0001), KDM6A (mutated n = 1091, wild type n = 3047, p < 0.0001), TERT (mutated n = 1714, wild type n = 2424), and TP53 (mutated n = 1689, wild type n = 2449, p < 0.0001).

Conclusions

Interestingly, while mutations in TP53 and ERCC2 were associated with shorter median survival, mutations in KDM6A and TERT were associated with longer median survival.

Background

Urothelial carcinoma (UC) is among the top 10 frequently diagnosed cancers in the world. Mutations in FGFR3, ARID1A, and TP53 are well documented as being some of the most frequent mutations found in UC. Despite advances in treatment, survival outcomes remain poor, especially in advanced stages. To promote future pharmacotherapeutic development, the molecular understanding of UC needs to be continually updated using more recently available databases.

Methods

This study utilizes the AACR Project GENIE database from the American Association for Cancer Research to explore the mutational profiles of patients with UC. Gene mutation frequencies were calculated, and two Kaplan-Meier curves were drawn for each gene, showing one curve for patients with the mutation and one for those without. Log-Rank tests were calculated with subsequent FDR (Benjamini–Hochberg) correction applied to account for multiple hypothesis testing. Data was analyzed using R 4.4.2 and statistical significance was set at α = 0.05.

Results

In this study, 4525 patients had histology consistent with UC. The 5 most common mutations were TERT (n = 1714, 37.9%), TP53 (n = 1689, 37.3%), KDM6A (n = 1091, 24.1%), ARID1A (n = 872, 19.3%), and FGFR3 (n = 762, 16.8%). Mutations associated with differential survival outcomes included ERCC2 (mutated n = 387, wild type n = 3751, p < 0.0001), KDM6A (mutated n = 1091, wild type n = 3047, p < 0.0001), TERT (mutated n = 1714, wild type n = 2424), and TP53 (mutated n = 1689, wild type n = 2449, p < 0.0001).

Conclusions

Interestingly, while mutations in TP53 and ERCC2 were associated with shorter median survival, mutations in KDM6A and TERT were associated with longer median survival.

Background

Urothelial carcinoma (UC) is among the top 10 frequently diagnosed cancers in the world. Mutations in FGFR3, ARID1A, and TP53 are well documented as being some of the most frequent mutations found in UC. Despite advances in treatment, survival outcomes remain poor, especially in advanced stages. To promote future pharmacotherapeutic development, the molecular understanding of UC needs to be continually updated using more recently available databases.

Methods

This study utilizes the AACR Project GENIE database from the American Association for Cancer Research to explore the mutational profiles of patients with UC. Gene mutation frequencies were calculated, and two Kaplan-Meier curves were drawn for each gene, showing one curve for patients with the mutation and one for those without. Log-Rank tests were calculated with subsequent FDR (Benjamini–Hochberg) correction applied to account for multiple hypothesis testing. Data was analyzed using R 4.4.2 and statistical significance was set at α = 0.05.

Results

In this study, 4525 patients had histology consistent with UC. The 5 most common mutations were TERT (n = 1714, 37.9%), TP53 (n = 1689, 37.3%), KDM6A (n = 1091, 24.1%), ARID1A (n = 872, 19.3%), and FGFR3 (n = 762, 16.8%). Mutations associated with differential survival outcomes included ERCC2 (mutated n = 387, wild type n = 3751, p < 0.0001), KDM6A (mutated n = 1091, wild type n = 3047, p < 0.0001), TERT (mutated n = 1714, wild type n = 2424), and TP53 (mutated n = 1689, wild type n = 2449, p < 0.0001).

Conclusions

Interestingly, while mutations in TP53 and ERCC2 were associated with shorter median survival, mutations in KDM6A and TERT were associated with longer median survival.

Background

We examined characteristics of Veterans receiving care through NTO and their CM (e.g., telephone only [T], video only [V], or both [TV]). Relevant background: In-person VA cancer care can be challenging for many Veterans due to rurality, transportation, finances, and distance to subspecialists. Such factors may impact care modality preferences.

Methods

We linked a list of all Veterans who received NTO care with Corporate Data Warehouse data to confirm an ICD-10 diagnostic code for malignancy, and to define the number of NTO interactions, latency of days between diagnosis and first NTO interaction, and demographics. The Office of Rural Health categories for rurality and NIH categories for race were used.

Data analysis

We report descriptive statistics for CM. To compare differences between Veterans by CM, we report chi-squared tests for categorical variables and ANOVAs for continuous variables.

Results

Among 13,902 NTO Veterans with CM data, most were V (9,998, 72%), few were T 2% (n= 295), and some were TV 26% (n= 3,609). There were statistically significant differences between CM in number of interactions, latency between diagnosis and first NTO interaction, age at first NTO interaction, sex, race, rurality, and cancer type. Veterans diagnosed with lung cancer were more likely to exclusively use T. Veterans with breast cancer were more likely to exclusively use V. Specifically, T were oldest (mean age = 74.3), followed by TV (69.0) and V (61.6; p < .001). Women were most represented in V (28.3%) and Rural or highly rural residence was most common among T users (54.6%), compared to V (36.8%) and TV (43.0%; p < .001). Urban users were more prevalent in the TV group (61.9%) than in the T only group (45.4%).

Implications

We identified differences in communication modality based on Veteran characteristics. This could suggest differences in Veteran or provider preference, feasibility, or acceptability, based on CM.

Significance

While V communications appear to be achievable for many Veterans, more work is needed to determine preference, feasibility, and acceptability among Veterans and their care teams regarding V and T only cancer care.

Background

We examined characteristics of Veterans receiving care through NTO and their CM (e.g., telephone only [T], video only [V], or both [TV]). Relevant background: In-person VA cancer care can be challenging for many Veterans due to rurality, transportation, finances, and distance to subspecialists. Such factors may impact care modality preferences.

Methods

We linked a list of all Veterans who received NTO care with Corporate Data Warehouse data to confirm an ICD-10 diagnostic code for malignancy, and to define the number of NTO interactions, latency of days between diagnosis and first NTO interaction, and demographics. The Office of Rural Health categories for rurality and NIH categories for race were used.

Data analysis

We report descriptive statistics for CM. To compare differences between Veterans by CM, we report chi-squared tests for categorical variables and ANOVAs for continuous variables.

Results

Among 13,902 NTO Veterans with CM data, most were V (9,998, 72%), few were T 2% (n= 295), and some were TV 26% (n= 3,609). There were statistically significant differences between CM in number of interactions, latency between diagnosis and first NTO interaction, age at first NTO interaction, sex, race, rurality, and cancer type. Veterans diagnosed with lung cancer were more likely to exclusively use T. Veterans with breast cancer were more likely to exclusively use V. Specifically, T were oldest (mean age = 74.3), followed by TV (69.0) and V (61.6; p < .001). Women were most represented in V (28.3%) and Rural or highly rural residence was most common among T users (54.6%), compared to V (36.8%) and TV (43.0%; p < .001). Urban users were more prevalent in the TV group (61.9%) than in the T only group (45.4%).

Implications

We identified differences in communication modality based on Veteran characteristics. This could suggest differences in Veteran or provider preference, feasibility, or acceptability, based on CM.

Significance

While V communications appear to be achievable for many Veterans, more work is needed to determine preference, feasibility, and acceptability among Veterans and their care teams regarding V and T only cancer care.

Background

We examined characteristics of Veterans receiving care through NTO and their CM (e.g., telephone only [T], video only [V], or both [TV]). Relevant background: In-person VA cancer care can be challenging for many Veterans due to rurality, transportation, finances, and distance to subspecialists. Such factors may impact care modality preferences.

Methods

We linked a list of all Veterans who received NTO care with Corporate Data Warehouse data to confirm an ICD-10 diagnostic code for malignancy, and to define the number of NTO interactions, latency of days between diagnosis and first NTO interaction, and demographics. The Office of Rural Health categories for rurality and NIH categories for race were used.

Data analysis

We report descriptive statistics for CM. To compare differences between Veterans by CM, we report chi-squared tests for categorical variables and ANOVAs for continuous variables.

Results

Among 13,902 NTO Veterans with CM data, most were V (9,998, 72%), few were T 2% (n= 295), and some were TV 26% (n= 3,609). There were statistically significant differences between CM in number of interactions, latency between diagnosis and first NTO interaction, age at first NTO interaction, sex, race, rurality, and cancer type. Veterans diagnosed with lung cancer were more likely to exclusively use T. Veterans with breast cancer were more likely to exclusively use V. Specifically, T were oldest (mean age = 74.3), followed by TV (69.0) and V (61.6; p < .001). Women were most represented in V (28.3%) and Rural or highly rural residence was most common among T users (54.6%), compared to V (36.8%) and TV (43.0%; p < .001). Urban users were more prevalent in the TV group (61.9%) than in the T only group (45.4%).

Implications

We identified differences in communication modality based on Veteran characteristics. This could suggest differences in Veteran or provider preference, feasibility, or acceptability, based on CM.

Significance

While V communications appear to be achievable for many Veterans, more work is needed to determine preference, feasibility, and acceptability among Veterans and their care teams regarding V and T only cancer care.

Background

Patients diagnosed with lung cancer are predominantly non-small cell lung cancer (NSCLC), a leading cause of cancer-related deaths. Thus, it is imperative to investigate and distinguish the differences present at diagnosis to possibly improve survival outcomes. NSCLC commonly metastasizes within older patients near the mean age of 71 years, but also in early onset patients which represents the patients younger than the earliest lung cancer screening age of 50.

Objective

To reveal differences in ratios of metastasis locations in squamous cell carcinoma (SCC), adenocarcinoma (ACC), and adenosquamous carcinoma (ASC).

Methods

The National Cancer Database (NCDB) was utilized to identify patients diagnosed with SCC, ACC, and ASC using the histology codes 8070, 8140, and 8560 from the ICD-O-3.2 from 2004 to 2022. Age groups were 70 years. Metastases located to the brain, liver, bone, and lung were included. Chi-Square tests were performed. The data was analyzed using R version 4.4.2 and statistical significance was set to α = 0.05.

Results

In this study, 1,445,119 patients were analyzed. Chi-Square tests identified significant differences in the ratios of organ metastasis locations between age groups in each subtype (p < 0.001). SCC in each age group similarly metastasized most to bone (36.3%, 34.7%, 34.5%), but notably more local lung metastasis was observed in the oldest group (33.6%). In ACC and ASC, the oldest group also had greater ratios of spread within the lungs (28.0%, 27.2%). Overall, the younger the age group, distant spread to the brain increased (ex. 29.0%, 24.4%, 17.5%). This suggests a widely heterogenous distribution of metastases at diagnosis of NSCLC subtypes and patient age.

Conclusions

This study demonstrated that patients with SCC, ACC, or ASC subtypes of NSCLC share similar predominant locations based in part on patient age, irrespective of cancer origin. NSCLC may more distantly metastasize in younger patients to the brain, while older patients may have locally metastatic cancer. Further analysis of key demographic variables as well as common undertaken treatment options may prove informative and reveal existing differences in survival outcomes.

Background

Patients diagnosed with lung cancer are predominantly non-small cell lung cancer (NSCLC), a leading cause of cancer-related deaths. Thus, it is imperative to investigate and distinguish the differences present at diagnosis to possibly improve survival outcomes. NSCLC commonly metastasizes within older patients near the mean age of 71 years, but also in early onset patients which represents the patients younger than the earliest lung cancer screening age of 50.

Objective

To reveal differences in ratios of metastasis locations in squamous cell carcinoma (SCC), adenocarcinoma (ACC), and adenosquamous carcinoma (ASC).

Methods

The National Cancer Database (NCDB) was utilized to identify patients diagnosed with SCC, ACC, and ASC using the histology codes 8070, 8140, and 8560 from the ICD-O-3.2 from 2004 to 2022. Age groups were 70 years. Metastases located to the brain, liver, bone, and lung were included. Chi-Square tests were performed. The data was analyzed using R version 4.4.2 and statistical significance was set to α = 0.05.

Results

In this study, 1,445,119 patients were analyzed. Chi-Square tests identified significant differences in the ratios of organ metastasis locations between age groups in each subtype (p < 0.001). SCC in each age group similarly metastasized most to bone (36.3%, 34.7%, 34.5%), but notably more local lung metastasis was observed in the oldest group (33.6%). In ACC and ASC, the oldest group also had greater ratios of spread within the lungs (28.0%, 27.2%). Overall, the younger the age group, distant spread to the brain increased (ex. 29.0%, 24.4%, 17.5%). This suggests a widely heterogenous distribution of metastases at diagnosis of NSCLC subtypes and patient age.

Conclusions

This study demonstrated that patients with SCC, ACC, or ASC subtypes of NSCLC share similar predominant locations based in part on patient age, irrespective of cancer origin. NSCLC may more distantly metastasize in younger patients to the brain, while older patients may have locally metastatic cancer. Further analysis of key demographic variables as well as common undertaken treatment options may prove informative and reveal existing differences in survival outcomes.

Background

Patients diagnosed with lung cancer are predominantly non-small cell lung cancer (NSCLC), a leading cause of cancer-related deaths. Thus, it is imperative to investigate and distinguish the differences present at diagnosis to possibly improve survival outcomes. NSCLC commonly metastasizes within older patients near the mean age of 71 years, but also in early onset patients which represents the patients younger than the earliest lung cancer screening age of 50.

Objective

To reveal differences in ratios of metastasis locations in squamous cell carcinoma (SCC), adenocarcinoma (ACC), and adenosquamous carcinoma (ASC).

Methods

The National Cancer Database (NCDB) was utilized to identify patients diagnosed with SCC, ACC, and ASC using the histology codes 8070, 8140, and 8560 from the ICD-O-3.2 from 2004 to 2022. Age groups were 70 years. Metastases located to the brain, liver, bone, and lung were included. Chi-Square tests were performed. The data was analyzed using R version 4.4.2 and statistical significance was set to α = 0.05.

Results

In this study, 1,445,119 patients were analyzed. Chi-Square tests identified significant differences in the ratios of organ metastasis locations between age groups in each subtype (p < 0.001). SCC in each age group similarly metastasized most to bone (36.3%, 34.7%, 34.5%), but notably more local lung metastasis was observed in the oldest group (33.6%). In ACC and ASC, the oldest group also had greater ratios of spread within the lungs (28.0%, 27.2%). Overall, the younger the age group, distant spread to the brain increased (ex. 29.0%, 24.4%, 17.5%). This suggests a widely heterogenous distribution of metastases at diagnosis of NSCLC subtypes and patient age.

Conclusions

This study demonstrated that patients with SCC, ACC, or ASC subtypes of NSCLC share similar predominant locations based in part on patient age, irrespective of cancer origin. NSCLC may more distantly metastasize in younger patients to the brain, while older patients may have locally metastatic cancer. Further analysis of key demographic variables as well as common undertaken treatment options may prove informative and reveal existing differences in survival outcomes.