Original Research

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.

Background

Rectal adenocarcinoma has long been associated with older adults, with routine screening typically beginning at age 45 or older. However, recent data reveal a concerning rise in rectal cancer incidence among adults under 40. These early-onset cases often present at later stages and may have distinct biological features. While some research attributes this trend to genetic or environmental factors, the contribution of socioeconomic disparities and healthcare access has not been fully explored. Identifying these influences is essential to shaping targeted prevention and early detection strategies for younger populations.

Objective

To evaluate temporal trends in rectal adenocarcinoma among young adults and assess demographic and socioeconomic predictors of early-onset diagnosis.

Methods

Data were drawn from the National Cancer Database (NCDB) for patients diagnosed with rectal adenocarcinoma from 2004 to 2022. Among 440,316 cases, 17,842 (4.1%) occurred in individuals under 40. Linear regression assessed temporal trends, while logistic regression evaluated associations between early-onset diagnosis and variables including sex, race, insurance status, income level, Charlson-Deyo comorbidity score, and tumor stage. Statistical significance was defined as α = 0.05.

Results

The number of young adults diagnosed rose from 424 in 2004 to 937 in 2022—an increase of over 120%. Each year was associated with a 1.7% rise in odds of early diagnosis (OR = 1.017, p < 0.001). Male patients had 24.7% higher odds (OR = 1.247, p < 0.001), and Black patients had 59.3% higher odds compared to White patients (OR = 1.593, p < 0.001). Non-private insurance was linked to a 41.6% decrease in early diagnosis (OR = 0.584, p < 0.001). Income level was not significant (p = 0.426). Lower Charlson-Deyo scores and higher tumor stages were also associated with early-onset cases.

Conclusions

Rectal adenocarcinoma is increasingly affecting younger adults, with significant associations across demographic and insurance variables. These findings call for improved awareness, early diagnostic strategies, and further research into underlying causes to mitigate this growing public health concern.

Background

Rectal adenocarcinoma has long been associated with older adults, with routine screening typically beginning at age 45 or older. However, recent data reveal a concerning rise in rectal cancer incidence among adults under 40. These early-onset cases often present at later stages and may have distinct biological features. While some research attributes this trend to genetic or environmental factors, the contribution of socioeconomic disparities and healthcare access has not been fully explored. Identifying these influences is essential to shaping targeted prevention and early detection strategies for younger populations.

Objective

To evaluate temporal trends in rectal adenocarcinoma among young adults and assess demographic and socioeconomic predictors of early-onset diagnosis.

Methods

Data were drawn from the National Cancer Database (NCDB) for patients diagnosed with rectal adenocarcinoma from 2004 to 2022. Among 440,316 cases, 17,842 (4.1%) occurred in individuals under 40. Linear regression assessed temporal trends, while logistic regression evaluated associations between early-onset diagnosis and variables including sex, race, insurance status, income level, Charlson-Deyo comorbidity score, and tumor stage. Statistical significance was defined as α = 0.05.

Results

The number of young adults diagnosed rose from 424 in 2004 to 937 in 2022—an increase of over 120%. Each year was associated with a 1.7% rise in odds of early diagnosis (OR = 1.017, p < 0.001). Male patients had 24.7% higher odds (OR = 1.247, p < 0.001), and Black patients had 59.3% higher odds compared to White patients (OR = 1.593, p < 0.001). Non-private insurance was linked to a 41.6% decrease in early diagnosis (OR = 0.584, p < 0.001). Income level was not significant (p = 0.426). Lower Charlson-Deyo scores and higher tumor stages were also associated with early-onset cases.

Conclusions

Rectal adenocarcinoma is increasingly affecting younger adults, with significant associations across demographic and insurance variables. These findings call for improved awareness, early diagnostic strategies, and further research into underlying causes to mitigate this growing public health concern.

Background

Rectal adenocarcinoma has long been associated with older adults, with routine screening typically beginning at age 45 or older. However, recent data reveal a concerning rise in rectal cancer incidence among adults under 40. These early-onset cases often present at later stages and may have distinct biological features. While some research attributes this trend to genetic or environmental factors, the contribution of socioeconomic disparities and healthcare access has not been fully explored. Identifying these influences is essential to shaping targeted prevention and early detection strategies for younger populations.

Objective

To evaluate temporal trends in rectal adenocarcinoma among young adults and assess demographic and socioeconomic predictors of early-onset diagnosis.

Methods

Data were drawn from the National Cancer Database (NCDB) for patients diagnosed with rectal adenocarcinoma from 2004 to 2022. Among 440,316 cases, 17,842 (4.1%) occurred in individuals under 40. Linear regression assessed temporal trends, while logistic regression evaluated associations between early-onset diagnosis and variables including sex, race, insurance status, income level, Charlson-Deyo comorbidity score, and tumor stage. Statistical significance was defined as α = 0.05.

Results

The number of young adults diagnosed rose from 424 in 2004 to 937 in 2022—an increase of over 120%. Each year was associated with a 1.7% rise in odds of early diagnosis (OR = 1.017, p < 0.001). Male patients had 24.7% higher odds (OR = 1.247, p < 0.001), and Black patients had 59.3% higher odds compared to White patients (OR = 1.593, p < 0.001). Non-private insurance was linked to a 41.6% decrease in early diagnosis (OR = 0.584, p < 0.001). Income level was not significant (p = 0.426). Lower Charlson-Deyo scores and higher tumor stages were also associated with early-onset cases.

Conclusions

Rectal adenocarcinoma is increasingly affecting younger adults, with significant associations across demographic and insurance variables. These findings call for improved awareness, early diagnostic strategies, and further research into underlying causes to mitigate this growing public health concern.

Background

Parotid gland malignancies are a rare subset of salivary gland tumors, comprising approximately 1–3% of all head and neck cancers. While distant metastases commonly involve the lungs, brain metastases are exceedingly rare and remain poorly characterized. Management typically includes stereotactic radiosurgery or whole-brain radiation. This study evaluates the incidence, clinicopathologic features, and survival outcomes of patients with parotid gland tumors and brain metastases using data from Surveillance, Epidemiology, and End Results (SEER) database.

Methods

SEER database (2010–2022) was queried for patients diagnosed with primary malignant neoplasms of the parotid gland (ICD-O-3 site code C07.9). Cases of brain metastases were identified using SEER metastatic site variables. Age-adjusted incidence rates (IR) per 100,000 population were calculated using SEER*Stat 8.4.5. Kaplan-Meier survival analyses were conducted using GraphPad Prism, and survival differences were assessed using the log-rank test.

Results

Among 12,951 patients diagnosed with parotid malignancy, 47 (0.36%) had brain metastases. The median age at diagnosis was 67 years, and 77.5% were male. The overall incidence rate (IR) of brain metastases was 0.00235 per 100,000 population, with a significantly higher rate observed in males compared to females (p < 0.0001). The most common histologic subtype associated with brain involvement was squamous cell carcinoma (SCC, n=10), followed by adenocarcinoma. Median overall survival (mOS) for patients with brain metastases was 2 months (hazard ratio [HR] 6.28; 95% CI: 2.71–14.55), compared to 131 months for those without brain involvement (p < 0.001). 1-year cancer-specific survival for patients with brain metastases was 38%. Among patients with parotid SCC and brain metastases, mOS was 3 months, compared to 39 months in those without brain involvement (HR 5.70; 95% CI: 1.09–29.68; p < 0.0001).

Conclusions

Brain metastases from parotid gland cancers, though rare, are associated with markedly poor outcomes. This highlights the importance of early neurologic assessment and brain imaging in high-risk patients, particularly with SCC histology. Prior studies have shown that TP53 mutations are common in parotid SCC, but their role in CNS spread remains unclear. Future research should explore molecular pathways underlying neurotropism in parotid cancers and investigate targeted systemic therapies with CNS penetration to improve outcomes.

Background

Parotid gland malignancies are a rare subset of salivary gland tumors, comprising approximately 1–3% of all head and neck cancers. While distant metastases commonly involve the lungs, brain metastases are exceedingly rare and remain poorly characterized. Management typically includes stereotactic radiosurgery or whole-brain radiation. This study evaluates the incidence, clinicopathologic features, and survival outcomes of patients with parotid gland tumors and brain metastases using data from Surveillance, Epidemiology, and End Results (SEER) database.

Methods

SEER database (2010–2022) was queried for patients diagnosed with primary malignant neoplasms of the parotid gland (ICD-O-3 site code C07.9). Cases of brain metastases were identified using SEER metastatic site variables. Age-adjusted incidence rates (IR) per 100,000 population were calculated using SEER*Stat 8.4.5. Kaplan-Meier survival analyses were conducted using GraphPad Prism, and survival differences were assessed using the log-rank test.

Results

Among 12,951 patients diagnosed with parotid malignancy, 47 (0.36%) had brain metastases. The median age at diagnosis was 67 years, and 77.5% were male. The overall incidence rate (IR) of brain metastases was 0.00235 per 100,000 population, with a significantly higher rate observed in males compared to females (p < 0.0001). The most common histologic subtype associated with brain involvement was squamous cell carcinoma (SCC, n=10), followed by adenocarcinoma. Median overall survival (mOS) for patients with brain metastases was 2 months (hazard ratio [HR] 6.28; 95% CI: 2.71–14.55), compared to 131 months for those without brain involvement (p < 0.001). 1-year cancer-specific survival for patients with brain metastases was 38%. Among patients with parotid SCC and brain metastases, mOS was 3 months, compared to 39 months in those without brain involvement (HR 5.70; 95% CI: 1.09–29.68; p < 0.0001).

Conclusions

Brain metastases from parotid gland cancers, though rare, are associated with markedly poor outcomes. This highlights the importance of early neurologic assessment and brain imaging in high-risk patients, particularly with SCC histology. Prior studies have shown that TP53 mutations are common in parotid SCC, but their role in CNS spread remains unclear. Future research should explore molecular pathways underlying neurotropism in parotid cancers and investigate targeted systemic therapies with CNS penetration to improve outcomes.

Background

Parotid gland malignancies are a rare subset of salivary gland tumors, comprising approximately 1–3% of all head and neck cancers. While distant metastases commonly involve the lungs, brain metastases are exceedingly rare and remain poorly characterized. Management typically includes stereotactic radiosurgery or whole-brain radiation. This study evaluates the incidence, clinicopathologic features, and survival outcomes of patients with parotid gland tumors and brain metastases using data from Surveillance, Epidemiology, and End Results (SEER) database.

Methods

SEER database (2010–2022) was queried for patients diagnosed with primary malignant neoplasms of the parotid gland (ICD-O-3 site code C07.9). Cases of brain metastases were identified using SEER metastatic site variables. Age-adjusted incidence rates (IR) per 100,000 population were calculated using SEER*Stat 8.4.5. Kaplan-Meier survival analyses were conducted using GraphPad Prism, and survival differences were assessed using the log-rank test.

Results

Among 12,951 patients diagnosed with parotid malignancy, 47 (0.36%) had brain metastases. The median age at diagnosis was 67 years, and 77.5% were male. The overall incidence rate (IR) of brain metastases was 0.00235 per 100,000 population, with a significantly higher rate observed in males compared to females (p < 0.0001). The most common histologic subtype associated with brain involvement was squamous cell carcinoma (SCC, n=10), followed by adenocarcinoma. Median overall survival (mOS) for patients with brain metastases was 2 months (hazard ratio [HR] 6.28; 95% CI: 2.71–14.55), compared to 131 months for those without brain involvement (p < 0.001). 1-year cancer-specific survival for patients with brain metastases was 38%. Among patients with parotid SCC and brain metastases, mOS was 3 months, compared to 39 months in those without brain involvement (HR 5.70; 95% CI: 1.09–29.68; p < 0.0001).

Conclusions

Brain metastases from parotid gland cancers, though rare, are associated with markedly poor outcomes. This highlights the importance of early neurologic assessment and brain imaging in high-risk patients, particularly with SCC histology. Prior studies have shown that TP53 mutations are common in parotid SCC, but their role in CNS spread remains unclear. Future research should explore molecular pathways underlying neurotropism in parotid cancers and investigate targeted systemic therapies with CNS penetration to improve outcomes.

Background

Papillary cystadenocarcinoma is a rare, aggressive malignancy typically arising in the ovaries, often following malignant transformation of benign precursors. Characterized by local invasion and recurrence, it lacks standardized treatment protocols and comprehensive epidemiological data. Existing literature is limited to case reports and small series, leaving gaps in population-level data to guide clinical decision-making. This study uses the National Cancer Database (NCDB) to assess demographic, socioeconomic, and treatment patterns to identify disparities and inform management.

Methods

A retrospective cohort analysis of 345 patients with histologically confirmed papillary cystadenocarcinoma (ICD-O-3 code 8450) was conducted using the 2004–2020 NCDB. Demographic, treatment, and survival data were described; incidence trends were assessed via linear regression; and survival was analyzed using Kaplan-Meier curves.

Results

The cohort was predominantly female (97.1%), mean age 62.1 years (SD = 14.0), and 87.2% White. Most had private insurance (44.9%) or Medicare (40.9%). Over half (51.9%) resided in metropolitan areas >1 million. Primary tumor sites were ovarian (80.0%) and endometrial (5.2%), with 39.7% presenting at Stage III. Surgery was performed in 90.4% of cases, with 51.9% achieving negative margins. Most were treated at comprehensive community (41.0%) or academic/research programs (28.7%). Primary therapies included chemotherapy (62.3%), radiation (6.4%), and hormone therapy (1.7%). Thirty-day mortality was 1.9%, and 90-day mortality was 5.4%. Survival was 97.7% at 2 years, 94.2% at 5 years, and 88.6% at 10 years. Mean survival was 97.5 months (95% CI: 88.2–106.7).

Conclusions

This is the first NCDB-based analysis of papillary cystadenocarcinoma, offering insight into its clinical characteristics. Ovarian and endometrial origins were most common, reinforcing its gynecologic profile. High surgical rates and margin negativity suggest aggressive local treatment is central to management. Disparities emerged: patients were more likely to live in urban areas, hold private insurance, and receive care at community programs. These findings highlight the need for further investigation into socioeconomic inequities and may inform future guidelines to improve equitable care delivery across health systems, including community-based programs such as the VHA.

Background

Papillary cystadenocarcinoma is a rare, aggressive malignancy typically arising in the ovaries, often following malignant transformation of benign precursors. Characterized by local invasion and recurrence, it lacks standardized treatment protocols and comprehensive epidemiological data. Existing literature is limited to case reports and small series, leaving gaps in population-level data to guide clinical decision-making. This study uses the National Cancer Database (NCDB) to assess demographic, socioeconomic, and treatment patterns to identify disparities and inform management.

Methods

A retrospective cohort analysis of 345 patients with histologically confirmed papillary cystadenocarcinoma (ICD-O-3 code 8450) was conducted using the 2004–2020 NCDB. Demographic, treatment, and survival data were described; incidence trends were assessed via linear regression; and survival was analyzed using Kaplan-Meier curves.

Results

The cohort was predominantly female (97.1%), mean age 62.1 years (SD = 14.0), and 87.2% White. Most had private insurance (44.9%) or Medicare (40.9%). Over half (51.9%) resided in metropolitan areas >1 million. Primary tumor sites were ovarian (80.0%) and endometrial (5.2%), with 39.7% presenting at Stage III. Surgery was performed in 90.4% of cases, with 51.9% achieving negative margins. Most were treated at comprehensive community (41.0%) or academic/research programs (28.7%). Primary therapies included chemotherapy (62.3%), radiation (6.4%), and hormone therapy (1.7%). Thirty-day mortality was 1.9%, and 90-day mortality was 5.4%. Survival was 97.7% at 2 years, 94.2% at 5 years, and 88.6% at 10 years. Mean survival was 97.5 months (95% CI: 88.2–106.7).

Conclusions

This is the first NCDB-based analysis of papillary cystadenocarcinoma, offering insight into its clinical characteristics. Ovarian and endometrial origins were most common, reinforcing its gynecologic profile. High surgical rates and margin negativity suggest aggressive local treatment is central to management. Disparities emerged: patients were more likely to live in urban areas, hold private insurance, and receive care at community programs. These findings highlight the need for further investigation into socioeconomic inequities and may inform future guidelines to improve equitable care delivery across health systems, including community-based programs such as the VHA.

Background

Papillary cystadenocarcinoma is a rare, aggressive malignancy typically arising in the ovaries, often following malignant transformation of benign precursors. Characterized by local invasion and recurrence, it lacks standardized treatment protocols and comprehensive epidemiological data. Existing literature is limited to case reports and small series, leaving gaps in population-level data to guide clinical decision-making. This study uses the National Cancer Database (NCDB) to assess demographic, socioeconomic, and treatment patterns to identify disparities and inform management.

Methods

A retrospective cohort analysis of 345 patients with histologically confirmed papillary cystadenocarcinoma (ICD-O-3 code 8450) was conducted using the 2004–2020 NCDB. Demographic, treatment, and survival data were described; incidence trends were assessed via linear regression; and survival was analyzed using Kaplan-Meier curves.

Results

The cohort was predominantly female (97.1%), mean age 62.1 years (SD = 14.0), and 87.2% White. Most had private insurance (44.9%) or Medicare (40.9%). Over half (51.9%) resided in metropolitan areas >1 million. Primary tumor sites were ovarian (80.0%) and endometrial (5.2%), with 39.7% presenting at Stage III. Surgery was performed in 90.4% of cases, with 51.9% achieving negative margins. Most were treated at comprehensive community (41.0%) or academic/research programs (28.7%). Primary therapies included chemotherapy (62.3%), radiation (6.4%), and hormone therapy (1.7%). Thirty-day mortality was 1.9%, and 90-day mortality was 5.4%. Survival was 97.7% at 2 years, 94.2% at 5 years, and 88.6% at 10 years. Mean survival was 97.5 months (95% CI: 88.2–106.7).

Conclusions

This is the first NCDB-based analysis of papillary cystadenocarcinoma, offering insight into its clinical characteristics. Ovarian and endometrial origins were most common, reinforcing its gynecologic profile. High surgical rates and margin negativity suggest aggressive local treatment is central to management. Disparities emerged: patients were more likely to live in urban areas, hold private insurance, and receive care at community programs. These findings highlight the need for further investigation into socioeconomic inequities and may inform future guidelines to improve equitable care delivery across health systems, including community-based programs such as the VHA.

Background

19.8% of all deaths in the US in 2023 were due to cancer. Despite its prevalence, there is minimal literature analyzing geographical trends in end-of-life care in cancer patients. This study aims to assess the evolution of end-of-life preferences in cancer patients, particularly during the COVID-19 pandemic, and account for geographical disparities to optimize palliative care delivery.

Methods

The CDC WONDER database was used to collect data on place of death (home, hospice, medical facilities, nursing homes) in patients over 25 years old that died with malignant neoplasms (ICD 10: C00- C97) in the US from 2003-2023. Deaths were stratified by region and urbanization. Proportional mortality was calculated, and statistically significant trends in mortality over time were identified using Joinpoint regression.

Results

There were 13,654,631 total deaths from malignant neoplasms over the study period. Home (40.3%) was the most common place of death followed by medical facilities (30.4%), nursing homes (14.3%), and hospice (8.9%). In 2020, all places experienced a decreased in proportion except for home which rose 7.0% from 41.7% to 48.7%. The South had the highest hospice rates (11.3%); 5.0% greater than the next highest region (Northeast; 8.3%). The West had the highest home rates (47.1%); 6.2% greater than the next closest region (South; 40.9%). The Northeast had the highest medical facility rates (36.0%); 5.5% higher than the next highest region (South, 30.5%). Nonmetro areas (< 50,000 population) had the lowest hospice (4.9%) and highest nursing home rates (15.8%). They also saw a substantial jump (+15.4%) in home deaths from 2019-21. All urbanizations saw a drop in medical facility deaths in 2020 but all have since climbed to surpass their 2019 rates except for nonmetro areas which have dropped 7.3% from 2020-2023.

Conclusion

Hospice and home deaths have increased in frequency with home deaths spiking during the COVID-19 pandemic. Geographical disparities persist in end-of-life care, particularly in nonmetro areas. This highlights the need to increase education and access to palliative care. Further research should aim at why the rural populations have failed to revert to pre-COVID trends like the other urbanization groups.

Background

19.8% of all deaths in the US in 2023 were due to cancer. Despite its prevalence, there is minimal literature analyzing geographical trends in end-of-life care in cancer patients. This study aims to assess the evolution of end-of-life preferences in cancer patients, particularly during the COVID-19 pandemic, and account for geographical disparities to optimize palliative care delivery.

Methods

The CDC WONDER database was used to collect data on place of death (home, hospice, medical facilities, nursing homes) in patients over 25 years old that died with malignant neoplasms (ICD 10: C00- C97) in the US from 2003-2023. Deaths were stratified by region and urbanization. Proportional mortality was calculated, and statistically significant trends in mortality over time were identified using Joinpoint regression.

Results

There were 13,654,631 total deaths from malignant neoplasms over the study period. Home (40.3%) was the most common place of death followed by medical facilities (30.4%), nursing homes (14.3%), and hospice (8.9%). In 2020, all places experienced a decreased in proportion except for home which rose 7.0% from 41.7% to 48.7%. The South had the highest hospice rates (11.3%); 5.0% greater than the next highest region (Northeast; 8.3%). The West had the highest home rates (47.1%); 6.2% greater than the next closest region (South; 40.9%). The Northeast had the highest medical facility rates (36.0%); 5.5% higher than the next highest region (South, 30.5%). Nonmetro areas (< 50,000 population) had the lowest hospice (4.9%) and highest nursing home rates (15.8%). They also saw a substantial jump (+15.4%) in home deaths from 2019-21. All urbanizations saw a drop in medical facility deaths in 2020 but all have since climbed to surpass their 2019 rates except for nonmetro areas which have dropped 7.3% from 2020-2023.

Conclusion

Hospice and home deaths have increased in frequency with home deaths spiking during the COVID-19 pandemic. Geographical disparities persist in end-of-life care, particularly in nonmetro areas. This highlights the need to increase education and access to palliative care. Further research should aim at why the rural populations have failed to revert to pre-COVID trends like the other urbanization groups.

Background

19.8% of all deaths in the US in 2023 were due to cancer. Despite its prevalence, there is minimal literature analyzing geographical trends in end-of-life care in cancer patients. This study aims to assess the evolution of end-of-life preferences in cancer patients, particularly during the COVID-19 pandemic, and account for geographical disparities to optimize palliative care delivery.

Methods

The CDC WONDER database was used to collect data on place of death (home, hospice, medical facilities, nursing homes) in patients over 25 years old that died with malignant neoplasms (ICD 10: C00- C97) in the US from 2003-2023. Deaths were stratified by region and urbanization. Proportional mortality was calculated, and statistically significant trends in mortality over time were identified using Joinpoint regression.

Results

There were 13,654,631 total deaths from malignant neoplasms over the study period. Home (40.3%) was the most common place of death followed by medical facilities (30.4%), nursing homes (14.3%), and hospice (8.9%). In 2020, all places experienced a decreased in proportion except for home which rose 7.0% from 41.7% to 48.7%. The South had the highest hospice rates (11.3%); 5.0% greater than the next highest region (Northeast; 8.3%). The West had the highest home rates (47.1%); 6.2% greater than the next closest region (South; 40.9%). The Northeast had the highest medical facility rates (36.0%); 5.5% higher than the next highest region (South, 30.5%). Nonmetro areas (< 50,000 population) had the lowest hospice (4.9%) and highest nursing home rates (15.8%). They also saw a substantial jump (+15.4%) in home deaths from 2019-21. All urbanizations saw a drop in medical facility deaths in 2020 but all have since climbed to surpass their 2019 rates except for nonmetro areas which have dropped 7.3% from 2020-2023.

Conclusion

Hospice and home deaths have increased in frequency with home deaths spiking during the COVID-19 pandemic. Geographical disparities persist in end-of-life care, particularly in nonmetro areas. This highlights the need to increase education and access to palliative care. Further research should aim at why the rural populations have failed to revert to pre-COVID trends like the other urbanization groups.