Stephen J. Schafers, PharmD, BCPS

Hyperkalemia occurs in as many as 10% of all hospitalized patients,[1] leading to potentially fatal arrhythmias or cardiac arrest that results from ionic imbalance within the resting membrane potential of myocardial tissue.[2] Acute instances may be stabilized with insulin to stimulate intracellular uptake of potassium, but this increases the risk of hypoglycemia.[2] Centers for Medicare and Medicaid Services quality measures require hospitals to minimize hypoglycemic events, particularly serious events with blood glucose (BG) <40 mg/dL,[3] due to an association with an increase in mortality in the hospital setting.[4] Previous research at our tertiary care hospital found that 8.7% of patients had suffered a hypoglycemic event following insulin administration pursuant to acute hyperkalemia treatment, and that patients with a lower body weight are at increased risk of hypoglycemia, particularly severe hypoglycemia (BG <40 mg/dL).[5] Increasing the total dose of dextrose provided around the time of insulin administration is suggested to reduce this concern.[5]

Patients at our institution receive 50 g of dextrose in conjunction with intravenous (IV) insulin for hyperkalemia treatment. To further reduce the potential for hypoglycemia, our institution amended the acute hyperkalemia order set to provide prescribers an alternative dosing strategy to the standard 10 U of IV insulin traditionally used for this purpose. Beginning November 10, 2013, our computer prescriber order entry (CPOE) system automatically prepopulated a dose of 0.1 U/kg of body weight for any patients weighing <95 kg (doses rounded to the nearest whole unit) when the acute hyperkalemia order set was utilized. The maximum dose allowed continued to be 10 U. The revised order set also changed nursing orders to require BG monitoring as frequently as every hour following the administration of insulin and dextrose for the treatment of hyperkalemia.

The purpose of this study is to investigate whether weight‐based insulin dosing (0.1 U/kg) for patients weighing <95 kg, rather than a standard 10‐U insulin dose, resulted in fewer hypoglycemic episodes and patients affected. Secondarily, this study sought to determine the impact of weight‐based insulin dosing on potassium‐lowering effects of therapy and to detect any risk factors for development of hypoglycemia among this patient population.

METHODS

This institutional review boardapproved, single‐center, retrospective chart review examined patients for whom the physician order entry set for hyperkalemia therapy was utilized, including patients who weighed less than 95 kg and received regular insulin via weight‐based dosing (0.1 U/kg of body weight up to a maximum of 10 U) during the period November 10, 2013 to May 31, 2014, versus those who received fixed insulin dosing (10 U regardless of body weight) during the period May 1, 2013 to November 9, 2013. During each of these periods, the CPOE system autopopulated the recommended insulin dose, with the possibility for physician manual dose entry. Data collection was limited to the first use of insulin for hyperkalemia treatment per patient in each period.

Patients weighing <95 kg were the focus of this study because they received <10 U of insulin under the weight‐based dosing strategy. Patients were excluded from the study if they had a body weight >95 kg or no weight recorded, were not administered insulin as ordered, received greater than the CPOE‐specified insulin dose, or had no BG readings recorded within 24 hours of insulin administration. The first 66 patients within each group meeting all inclusion and exclusion criteria were randomly selected for analysis. This recruitment target was developed to provide enough patients for a meaningful analysis of hypoglycemia events based on previous reports from our institution.[5]

Hypoglycemia was defined as a recorded BG level <70 mg/dL within 24 hours after insulin administration; severe hypoglycemia was defined as a recorded BG <40 mg/dL within 24 hours. Individual episodes of hypoglycemia and severe hypoglycemia were recorded for each instance of such event separated by at least 1 hour from the time of the first recorded event. In addition, episodes of hypoglycemia or severe hypoglycemia and number of patients affected were assessed at within 6 hours, 6 to 12 hours, and 12 to 24 hours after insulin administration as separate subsets for statistical analysis.

For the purpose of assessing the potassium‐lowering efficacy of weight‐based versus traditional dosing of insulin, maximum serum potassium levels were examined in the 12‐hour interval before the hyperkalemia order set was implemented and compared with minimum potassium levels in the 12 hours after insulin was administered. A comparison of the treatment groups assessed differences between the mean decrease in serum potassium from baseline, the mean minimum potassium achieved, the number of patients achieving minimum potassium below 5.0 mEq/L, and the number of patients who subsequently received repeat treatment for hyperkalemia within 24 hours of treatment with insulin.

Statistical analysis was conducted utilizing ² and Fisher exact tests for nominal data and Student t test for continuous data to detect statistically significant differences between the groups. Binomial logistic multivariable analysis using a backward stepwise approach was used to determine factors for development of hypoglycemia, analyzed on a per‐patient basis to prevent characteristics from being over‐represented when events occurred multiple times to a single patient. All analyses were completed by using SPSS version 18 (SPSS Inc., Chicago, IL).

RESULTS

In total, 1734 entries were available for the acute hyperkalemia order set with insulin during the 2 periods investigated. Only 464 patients were eligible for manual chart review once weight‐based exclusions were identified by electronic database, with additional exclusion criteria later extracted from patient charts. Patients in both treatment groups were fairly well balanced, with a slightly lower body weight in the 10‐U insulin group recorded (Table 1). Patients in the weight‐based dosing group received between 4 and 9 U of insulin, depending on body weight.

A reduction in the number of hypoglycemic episodes was detected in the weight‐based dosing group of 56% within 24 hours, from 18 to 8 events (P = 0.05) (Table 2). The number of hypoglycemic events in every subset of time intervals was likewise reduced by at least 50% using weight‐based dosing (from 7 to 3 events within 6 hours, from 5 to 2 events in 612 hours, from 6 to 3 events in 1224 hours). The number of patients who experienced hypoglycemia within 24 hours after receiving insulin also was reduced in the weight‐based dosing group by 46% (P = 0.22).

Potassium lowering was comparable across both dosing strategies in every measure assessed (Table 2). Multivariate analysis revealed that baseline BG <140 mg/dL (adjusted odds ratio: 4.3, 95% confidence interval [CI]: 1.4‐13.7, P = 0.01) and female gender (adjusted odds ratio: 3.2, 95% CI: 1.1‐9.1, P = 0.03) were associated with an increased risk of hypoglycemia. Other factors, including administration of insulin beyond that for hyperkalemia treatment and use of additional hypoglycemic agents, were not associated with the development of hypoglycemia, which is consistent with previous reports.[6]

CONCLUSIONS

Our findings indicate that using a weight‐based approach to insulin dosing when treating hyperkalemia may lead to a reduction in hypoglycemia without sacrificing the efficacy of potassium lowering. Females and patients with glucose values <140 mg/dL were at increased risk of hypoglycemia in this cohort. Based on the results of this research, a weight‐based dosing strategy of 0.1 U/kg IV insulin up to a maximum of 10 U should be considered, with further research desirable to validate these results.

This study was strengthened by the inclusion of all patients regardless of baseline glucose, baseline potassium, administration of other insulins, level of renal impairment, or symptomatic display of hypoglycemia or cardiac dysfunction, thus providing a broad representation of patients treated for acute hyperkalemia. This pilot study was limited in its scope by data collection for only 66 randomized patients per group rather than the entire patient population. In addition, the study utilized patient information from a single site, with few ethnicities represented. Validation of this research using a larger sample size should include greater variation in the patients served. Our inclusion of a hypoglycemia definition up to 24 hours after treatment may also be criticized. However, this is similar to previous reports and allows for a liberal time period for follow‐up glucose monitoring to be recorded.[7]

Because of its small sample size and the low event rate, this study was unable to draw conclusions about the ability of weight‐based insulin dosing to affect severe hypoglycemic events (<40 mg/dL). A study of more than 400 patients would be necessary to find statistically significant differences in the risk of severe hypoglycemia. Furthermore, because we did not examine the results from all patients in this cohort, we cannot conclusively determine the impact of treatment. The retrospective nature of this study limited our ability to capture hypoglycemic episodes during periods in which BG levels were not recorded. Additionally, changes to the post‐treatment glucose monitoring protocol may have also affected the incidence of hypoglycemia in 2 potential ways. First, early and unrecorded interventions may have occurred in patients with a trend toward hypoglycemia. Second, the longer time to follow‐up in the nonweight‐based group may have led to additional hypoglycemic episodes being missed. A prospective trial design could provide more comprehensive information about patient response to weight‐based versus traditional dosing of IV insulin for hyperkalemia. Further investigations on reducing adverse effects of insulin when treating hyperkalemia should focus on female patients and those with lower baseline BG values. Additionally, as newer agents to treat hyperkalemia are developed and tested, the approach to management should be revisited.[8, 9, 10]

Disclosures: Garry S. Tobin, MD, lectures or is on the speakers bureau for Eli Lilly, Jansen, Boehringher Ingelheim, and Novo Nordisk, and performs data safety monitoring for Novo Nordisk. The authors report no other potential conflicts of interest.

Hyperkalemia occurs in as many as 10% of all hospitalized patients,[1] leading to potentially fatal arrhythmias or cardiac arrest that results from ionic imbalance within the resting membrane potential of myocardial tissue.[2] Acute instances may be stabilized with insulin to stimulate intracellular uptake of potassium, but this increases the risk of hypoglycemia.[2] Centers for Medicare and Medicaid Services quality measures require hospitals to minimize hypoglycemic events, particularly serious events with blood glucose (BG) <40 mg/dL,[3] due to an association with an increase in mortality in the hospital setting.[4] Previous research at our tertiary care hospital found that 8.7% of patients had suffered a hypoglycemic event following insulin administration pursuant to acute hyperkalemia treatment, and that patients with a lower body weight are at increased risk of hypoglycemia, particularly severe hypoglycemia (BG <40 mg/dL).[5] Increasing the total dose of dextrose provided around the time of insulin administration is suggested to reduce this concern.[5]

Patients at our institution receive 50 g of dextrose in conjunction with intravenous (IV) insulin for hyperkalemia treatment. To further reduce the potential for hypoglycemia, our institution amended the acute hyperkalemia order set to provide prescribers an alternative dosing strategy to the standard 10 U of IV insulin traditionally used for this purpose. Beginning November 10, 2013, our computer prescriber order entry (CPOE) system automatically prepopulated a dose of 0.1 U/kg of body weight for any patients weighing <95 kg (doses rounded to the nearest whole unit) when the acute hyperkalemia order set was utilized. The maximum dose allowed continued to be 10 U. The revised order set also changed nursing orders to require BG monitoring as frequently as every hour following the administration of insulin and dextrose for the treatment of hyperkalemia.

The purpose of this study is to investigate whether weight‐based insulin dosing (0.1 U/kg) for patients weighing <95 kg, rather than a standard 10‐U insulin dose, resulted in fewer hypoglycemic episodes and patients affected. Secondarily, this study sought to determine the impact of weight‐based insulin dosing on potassium‐lowering effects of therapy and to detect any risk factors for development of hypoglycemia among this patient population.

METHODS

This institutional review boardapproved, single‐center, retrospective chart review examined patients for whom the physician order entry set for hyperkalemia therapy was utilized, including patients who weighed less than 95 kg and received regular insulin via weight‐based dosing (0.1 U/kg of body weight up to a maximum of 10 U) during the period November 10, 2013 to May 31, 2014, versus those who received fixed insulin dosing (10 U regardless of body weight) during the period May 1, 2013 to November 9, 2013. During each of these periods, the CPOE system autopopulated the recommended insulin dose, with the possibility for physician manual dose entry. Data collection was limited to the first use of insulin for hyperkalemia treatment per patient in each period.

Patients weighing <95 kg were the focus of this study because they received <10 U of insulin under the weight‐based dosing strategy. Patients were excluded from the study if they had a body weight >95 kg or no weight recorded, were not administered insulin as ordered, received greater than the CPOE‐specified insulin dose, or had no BG readings recorded within 24 hours of insulin administration. The first 66 patients within each group meeting all inclusion and exclusion criteria were randomly selected for analysis. This recruitment target was developed to provide enough patients for a meaningful analysis of hypoglycemia events based on previous reports from our institution.[5]

Hypoglycemia was defined as a recorded BG level <70 mg/dL within 24 hours after insulin administration; severe hypoglycemia was defined as a recorded BG <40 mg/dL within 24 hours. Individual episodes of hypoglycemia and severe hypoglycemia were recorded for each instance of such event separated by at least 1 hour from the time of the first recorded event. In addition, episodes of hypoglycemia or severe hypoglycemia and number of patients affected were assessed at within 6 hours, 6 to 12 hours, and 12 to 24 hours after insulin administration as separate subsets for statistical analysis.

For the purpose of assessing the potassium‐lowering efficacy of weight‐based versus traditional dosing of insulin, maximum serum potassium levels were examined in the 12‐hour interval before the hyperkalemia order set was implemented and compared with minimum potassium levels in the 12 hours after insulin was administered. A comparison of the treatment groups assessed differences between the mean decrease in serum potassium from baseline, the mean minimum potassium achieved, the number of patients achieving minimum potassium below 5.0 mEq/L, and the number of patients who subsequently received repeat treatment for hyperkalemia within 24 hours of treatment with insulin.

Statistical analysis was conducted utilizing ² and Fisher exact tests for nominal data and Student t test for continuous data to detect statistically significant differences between the groups. Binomial logistic multivariable analysis using a backward stepwise approach was used to determine factors for development of hypoglycemia, analyzed on a per‐patient basis to prevent characteristics from being over‐represented when events occurred multiple times to a single patient. All analyses were completed by using SPSS version 18 (SPSS Inc., Chicago, IL).

RESULTS

In total, 1734 entries were available for the acute hyperkalemia order set with insulin during the 2 periods investigated. Only 464 patients were eligible for manual chart review once weight‐based exclusions were identified by electronic database, with additional exclusion criteria later extracted from patient charts. Patients in both treatment groups were fairly well balanced, with a slightly lower body weight in the 10‐U insulin group recorded (Table 1). Patients in the weight‐based dosing group received between 4 and 9 U of insulin, depending on body weight.

A reduction in the number of hypoglycemic episodes was detected in the weight‐based dosing group of 56% within 24 hours, from 18 to 8 events (P = 0.05) (Table 2). The number of hypoglycemic events in every subset of time intervals was likewise reduced by at least 50% using weight‐based dosing (from 7 to 3 events within 6 hours, from 5 to 2 events in 612 hours, from 6 to 3 events in 1224 hours). The number of patients who experienced hypoglycemia within 24 hours after receiving insulin also was reduced in the weight‐based dosing group by 46% (P = 0.22).

Potassium lowering was comparable across both dosing strategies in every measure assessed (Table 2). Multivariate analysis revealed that baseline BG <140 mg/dL (adjusted odds ratio: 4.3, 95% confidence interval [CI]: 1.4‐13.7, P = 0.01) and female gender (adjusted odds ratio: 3.2, 95% CI: 1.1‐9.1, P = 0.03) were associated with an increased risk of hypoglycemia. Other factors, including administration of insulin beyond that for hyperkalemia treatment and use of additional hypoglycemic agents, were not associated with the development of hypoglycemia, which is consistent with previous reports.[6]

CONCLUSIONS

Our findings indicate that using a weight‐based approach to insulin dosing when treating hyperkalemia may lead to a reduction in hypoglycemia without sacrificing the efficacy of potassium lowering. Females and patients with glucose values <140 mg/dL were at increased risk of hypoglycemia in this cohort. Based on the results of this research, a weight‐based dosing strategy of 0.1 U/kg IV insulin up to a maximum of 10 U should be considered, with further research desirable to validate these results.

This study was strengthened by the inclusion of all patients regardless of baseline glucose, baseline potassium, administration of other insulins, level of renal impairment, or symptomatic display of hypoglycemia or cardiac dysfunction, thus providing a broad representation of patients treated for acute hyperkalemia. This pilot study was limited in its scope by data collection for only 66 randomized patients per group rather than the entire patient population. In addition, the study utilized patient information from a single site, with few ethnicities represented. Validation of this research using a larger sample size should include greater variation in the patients served. Our inclusion of a hypoglycemia definition up to 24 hours after treatment may also be criticized. However, this is similar to previous reports and allows for a liberal time period for follow‐up glucose monitoring to be recorded.[7]

Because of its small sample size and the low event rate, this study was unable to draw conclusions about the ability of weight‐based insulin dosing to affect severe hypoglycemic events (<40 mg/dL). A study of more than 400 patients would be necessary to find statistically significant differences in the risk of severe hypoglycemia. Furthermore, because we did not examine the results from all patients in this cohort, we cannot conclusively determine the impact of treatment. The retrospective nature of this study limited our ability to capture hypoglycemic episodes during periods in which BG levels were not recorded. Additionally, changes to the post‐treatment glucose monitoring protocol may have also affected the incidence of hypoglycemia in 2 potential ways. First, early and unrecorded interventions may have occurred in patients with a trend toward hypoglycemia. Second, the longer time to follow‐up in the nonweight‐based group may have led to additional hypoglycemic episodes being missed. A prospective trial design could provide more comprehensive information about patient response to weight‐based versus traditional dosing of IV insulin for hyperkalemia. Further investigations on reducing adverse effects of insulin when treating hyperkalemia should focus on female patients and those with lower baseline BG values. Additionally, as newer agents to treat hyperkalemia are developed and tested, the approach to management should be revisited.[8, 9, 10]

Disclosures: Garry S. Tobin, MD, lectures or is on the speakers bureau for Eli Lilly, Jansen, Boehringher Ingelheim, and Novo Nordisk, and performs data safety monitoring for Novo Nordisk. The authors report no other potential conflicts of interest.

Hyperkalemia occurs in as many as 10% of all hospitalized patients,[1] leading to potentially fatal arrhythmias or cardiac arrest that results from ionic imbalance within the resting membrane potential of myocardial tissue.[2] Acute instances may be stabilized with insulin to stimulate intracellular uptake of potassium, but this increases the risk of hypoglycemia.[2] Centers for Medicare and Medicaid Services quality measures require hospitals to minimize hypoglycemic events, particularly serious events with blood glucose (BG) <40 mg/dL,[3] due to an association with an increase in mortality in the hospital setting.[4] Previous research at our tertiary care hospital found that 8.7% of patients had suffered a hypoglycemic event following insulin administration pursuant to acute hyperkalemia treatment, and that patients with a lower body weight are at increased risk of hypoglycemia, particularly severe hypoglycemia (BG <40 mg/dL).[5] Increasing the total dose of dextrose provided around the time of insulin administration is suggested to reduce this concern.[5]

Patients at our institution receive 50 g of dextrose in conjunction with intravenous (IV) insulin for hyperkalemia treatment. To further reduce the potential for hypoglycemia, our institution amended the acute hyperkalemia order set to provide prescribers an alternative dosing strategy to the standard 10 U of IV insulin traditionally used for this purpose. Beginning November 10, 2013, our computer prescriber order entry (CPOE) system automatically prepopulated a dose of 0.1 U/kg of body weight for any patients weighing <95 kg (doses rounded to the nearest whole unit) when the acute hyperkalemia order set was utilized. The maximum dose allowed continued to be 10 U. The revised order set also changed nursing orders to require BG monitoring as frequently as every hour following the administration of insulin and dextrose for the treatment of hyperkalemia.

The purpose of this study is to investigate whether weight‐based insulin dosing (0.1 U/kg) for patients weighing <95 kg, rather than a standard 10‐U insulin dose, resulted in fewer hypoglycemic episodes and patients affected. Secondarily, this study sought to determine the impact of weight‐based insulin dosing on potassium‐lowering effects of therapy and to detect any risk factors for development of hypoglycemia among this patient population.

METHODS

This institutional review boardapproved, single‐center, retrospective chart review examined patients for whom the physician order entry set for hyperkalemia therapy was utilized, including patients who weighed less than 95 kg and received regular insulin via weight‐based dosing (0.1 U/kg of body weight up to a maximum of 10 U) during the period November 10, 2013 to May 31, 2014, versus those who received fixed insulin dosing (10 U regardless of body weight) during the period May 1, 2013 to November 9, 2013. During each of these periods, the CPOE system autopopulated the recommended insulin dose, with the possibility for physician manual dose entry. Data collection was limited to the first use of insulin for hyperkalemia treatment per patient in each period.

Patients weighing <95 kg were the focus of this study because they received <10 U of insulin under the weight‐based dosing strategy. Patients were excluded from the study if they had a body weight >95 kg or no weight recorded, were not administered insulin as ordered, received greater than the CPOE‐specified insulin dose, or had no BG readings recorded within 24 hours of insulin administration. The first 66 patients within each group meeting all inclusion and exclusion criteria were randomly selected for analysis. This recruitment target was developed to provide enough patients for a meaningful analysis of hypoglycemia events based on previous reports from our institution.[5]

Hypoglycemia was defined as a recorded BG level <70 mg/dL within 24 hours after insulin administration; severe hypoglycemia was defined as a recorded BG <40 mg/dL within 24 hours. Individual episodes of hypoglycemia and severe hypoglycemia were recorded for each instance of such event separated by at least 1 hour from the time of the first recorded event. In addition, episodes of hypoglycemia or severe hypoglycemia and number of patients affected were assessed at within 6 hours, 6 to 12 hours, and 12 to 24 hours after insulin administration as separate subsets for statistical analysis.

For the purpose of assessing the potassium‐lowering efficacy of weight‐based versus traditional dosing of insulin, maximum serum potassium levels were examined in the 12‐hour interval before the hyperkalemia order set was implemented and compared with minimum potassium levels in the 12 hours after insulin was administered. A comparison of the treatment groups assessed differences between the mean decrease in serum potassium from baseline, the mean minimum potassium achieved, the number of patients achieving minimum potassium below 5.0 mEq/L, and the number of patients who subsequently received repeat treatment for hyperkalemia within 24 hours of treatment with insulin.

Statistical analysis was conducted utilizing ² and Fisher exact tests for nominal data and Student t test for continuous data to detect statistically significant differences between the groups. Binomial logistic multivariable analysis using a backward stepwise approach was used to determine factors for development of hypoglycemia, analyzed on a per‐patient basis to prevent characteristics from being over‐represented when events occurred multiple times to a single patient. All analyses were completed by using SPSS version 18 (SPSS Inc., Chicago, IL).

RESULTS

In total, 1734 entries were available for the acute hyperkalemia order set with insulin during the 2 periods investigated. Only 464 patients were eligible for manual chart review once weight‐based exclusions were identified by electronic database, with additional exclusion criteria later extracted from patient charts. Patients in both treatment groups were fairly well balanced, with a slightly lower body weight in the 10‐U insulin group recorded (Table 1). Patients in the weight‐based dosing group received between 4 and 9 U of insulin, depending on body weight.

A reduction in the number of hypoglycemic episodes was detected in the weight‐based dosing group of 56% within 24 hours, from 18 to 8 events (P = 0.05) (Table 2). The number of hypoglycemic events in every subset of time intervals was likewise reduced by at least 50% using weight‐based dosing (from 7 to 3 events within 6 hours, from 5 to 2 events in 612 hours, from 6 to 3 events in 1224 hours). The number of patients who experienced hypoglycemia within 24 hours after receiving insulin also was reduced in the weight‐based dosing group by 46% (P = 0.22).

Potassium lowering was comparable across both dosing strategies in every measure assessed (Table 2). Multivariate analysis revealed that baseline BG <140 mg/dL (adjusted odds ratio: 4.3, 95% confidence interval [CI]: 1.4‐13.7, P = 0.01) and female gender (adjusted odds ratio: 3.2, 95% CI: 1.1‐9.1, P = 0.03) were associated with an increased risk of hypoglycemia. Other factors, including administration of insulin beyond that for hyperkalemia treatment and use of additional hypoglycemic agents, were not associated with the development of hypoglycemia, which is consistent with previous reports.[6]

CONCLUSIONS

Our findings indicate that using a weight‐based approach to insulin dosing when treating hyperkalemia may lead to a reduction in hypoglycemia without sacrificing the efficacy of potassium lowering. Females and patients with glucose values <140 mg/dL were at increased risk of hypoglycemia in this cohort. Based on the results of this research, a weight‐based dosing strategy of 0.1 U/kg IV insulin up to a maximum of 10 U should be considered, with further research desirable to validate these results.

This study was strengthened by the inclusion of all patients regardless of baseline glucose, baseline potassium, administration of other insulins, level of renal impairment, or symptomatic display of hypoglycemia or cardiac dysfunction, thus providing a broad representation of patients treated for acute hyperkalemia. This pilot study was limited in its scope by data collection for only 66 randomized patients per group rather than the entire patient population. In addition, the study utilized patient information from a single site, with few ethnicities represented. Validation of this research using a larger sample size should include greater variation in the patients served. Our inclusion of a hypoglycemia definition up to 24 hours after treatment may also be criticized. However, this is similar to previous reports and allows for a liberal time period for follow‐up glucose monitoring to be recorded.[7]

Because of its small sample size and the low event rate, this study was unable to draw conclusions about the ability of weight‐based insulin dosing to affect severe hypoglycemic events (<40 mg/dL). A study of more than 400 patients would be necessary to find statistically significant differences in the risk of severe hypoglycemia. Furthermore, because we did not examine the results from all patients in this cohort, we cannot conclusively determine the impact of treatment. The retrospective nature of this study limited our ability to capture hypoglycemic episodes during periods in which BG levels were not recorded. Additionally, changes to the post‐treatment glucose monitoring protocol may have also affected the incidence of hypoglycemia in 2 potential ways. First, early and unrecorded interventions may have occurred in patients with a trend toward hypoglycemia. Second, the longer time to follow‐up in the nonweight‐based group may have led to additional hypoglycemic episodes being missed. A prospective trial design could provide more comprehensive information about patient response to weight‐based versus traditional dosing of IV insulin for hyperkalemia. Further investigations on reducing adverse effects of insulin when treating hyperkalemia should focus on female patients and those with lower baseline BG values. Additionally, as newer agents to treat hyperkalemia are developed and tested, the approach to management should be revisited.[8, 9, 10]

Disclosures: Garry S. Tobin, MD, lectures or is on the speakers bureau for Eli Lilly, Jansen, Boehringher Ingelheim, and Novo Nordisk, and performs data safety monitoring for Novo Nordisk. The authors report no other potential conflicts of interest.

Chronic kidney disease is increasingly recognized as a significant public health issue, especially as our population ages. In the United States, it is estimated that 19.2 million individuals have chronic kidney disease (CKD), with an increasing prevalence in the elderly.1 CKD is associated with a higher mortality rate, as well as an increased risk of having several comorbidities, including anemia, coronary artery disease, and congestive heart failure.24 Early recognition, intervention, and management of patients with CKD by physicians has been shown to slow progression of disease and decrease complications.57 In the hospital setting, patients with CKD are at increased risk of medication dosing errors and acute renal failure (ARF).810

Serum creatinine is the most commonly used laboratory marker for assessing renal function. However, creatinine level is an imprecise measure of overall renal function, especially in older patients. The most recent National Kidney Foundation/ Kidney Disease Outcomes Quality Initiative (K/DOQI) guidelines recommend laboratory reporting of a calculated estimate of GFR.11 Equations used to calculate estimated GFR in adults, including the Cockcroft‐Gault (C‐G) equation, have been shown to provide an estimate of renal function, which can be used to clinically stratify varying levels of impaired renal function.11 Several studies have demonstrated that recognition of CKD by physicians is low in various clinical settings, especially in elderly patients.1215 Compliance with renal‐dose medication guidelines has also frequently been noted to be poor.16, 17

The investigators conducted a chart review study before and after reporting of estimated GFR to physicians in a hospital setting to assess the effect on physician recognition of CKD, the primary outcome. Secondary outcomes included the effect of reporting GFR on physician prescribing behaviors at the time of hospital discharge, including dosing of renal‐dosed antibiotics and use of nonsteroidal anti‐inflammatory (NSAID) and cyclooxygenase type 2 inhibitor (COX‐2) medications.

METHODS

This study was a retrospective chart review, with a prospective chart review as a comparison. Patients selected were admitted to a general medical floor in a 900‐bed academic medical center over the 2 years from 2002 to 2004. Computerized databases of laboratory values and weights obtained during hospitalization were used to select patients who fulfilled the following criteria: age > 65 years, all creatinine values during hospitalization < 1.6 mg/dL, and calculated estimated creatinine clearance (CrCl) < 60 mL/min using the Cockcroft‐Gault (C‐G) formula. The C‐G equation was developed for estimating CrCl and has also been extensively tested as a predictor of GFR. K/DOQI guidelines identify the C‐G equation as the most frequently used equation to estimate GFR in adults.11 To ensure steady‐state renal function, patients were excluded if creatinine varied by more than 0.4 mg/dL during their hospitalization. Based on an anticipated CKD recognition rate of 24%,13 our study sample size was selected to detect a 13% difference in the primary end point between the pre‐ and postintervention groups with 80% power. The study was approved by the institutional review board of the medical school.

Patient charts were reviewed with data obtained from the medical record, including physician notes, discharge summaries, orders, medication lists, and discharge prescriptions. Physician recognition was defined by documentation of CKD, calculated CrCl, or GFR in the physician notes or discharge summary. Charts were reviewed for diagnosis of hypertension (HTN) or diabetes (DM), and discharge medications including NSAID and COX‐2 medications and use and correct dosing of antibiotics requiring dose adjustment in patients with decreased GFR. Aspirin was not included as an NSAID.

For the prospective chart review portion, patients were selected at the time of admission on the basis of the same criteria. A notification was placed in the chart prominently listing the patient's estimated GFR calculated using the C‐G equation. Also included was a list of the stages of chronic renal disease based on the most recent K/DOQI guidelines11 and recommendations on dosing of select renal‐dosed antibiotics. Patients were again excluded if creatinine varied more than 0.4 mg/dL during their hospitalization.

RESULTS

DISCUSSION

This study has confirmed the findings of other investigators that significant CKD is underdiagnosed by physicians, especially in elderly patients with creatinine values within the normal laboratory range.13, 14 Investigators have demonstrated improved documentation of CKD with reporting of creatinine clearance and other simple educational interventions in an outpatient setting.13 In this study, reporting of estimated GFR did result in a significantly higher rate of recognition, but the overall rate was still very low in both groups (3.9%‐12.6%).

Although physician recognition of CKD did increase with the reporting of estimated GFR, this study found no significant impact on prescribing behaviors. Previous studies have shown an association between documentation of specific diagnoses and appropriate physician management.15, 18 However, the current data suggest that simply reporting GFR and increasing physician recognition of CKD may not lead to a significant decrease in medication dosing errors and that more extensive educational measures may be required.

Hospitalist physicians are increasingly serving as the primary caregivers for an aging population of hospitalized patients, and it is imperative that physicians recognize decreased GFR in elderly patients. Clearly, medication dosing errors are occurring in these patients, increasing the risk of adverse drug reactions.19 Elderly patients with renal impairment are also at increased risk of ARF while hospitalized.9, 10 Recognition of CKD by inpatient physicians identifies those patients who require preventive measures including maintenance of adequate hydration and avoidance of hypotension and nephrotoxic agents. Prevention of ARF in these patients has important clinical implications, as the mortality of patients is higher for elderly patients who develop hospital‐acquired ARF than for those presenting with community‐acquired ARF.20 Development of ARF has also been shown to increase length of hospitalization.21 Hospitalist physicians can also use the period of hospitalization as an opportunity to identify patients at risk of progressive CKD and in need of close follow‐up and possible referral to a nephrologist.

This study had several limitations. It was performed at a single institution, and therefore results may not be generalizable to all medical centers. The primary outcome of CKD documentation is an imperfect measure of recognition. The fact that chart documentation of CKD increased following the intervention suggests that documentation is associated with recognition, although it may be an underestimate. The effects of reporting estimated GFR on other secondary outcomes, including dosing of other medications, prevention of ARF, and length of hospital stay were not examined and deserve further investigation. The C‐G equation was chosen to calculate estimated GFR. There may be some advantage to using the Modification of Diet in Renal Disease equation as an alternative, but it is unclear if this is true in elderly female patients, who made up most of our study population.2225 Although using a prediction equation is clearly superior to using creatinine measurement solely to assess renal function in patients, further study is needed to identify the most accurate and effective formula for calculating estimated GFR in elderly patients.

The low rate of recognition of CKD by physicians found in this and other studies demonstrates the strong need for improvement in this area. Low recognition of CKD and a high rate of medication dosing errors despite reporting of the estimated GFR suggest that simply reporting GFR in addition to creatinine level is not sufficient. Further research is indicated to identify pragmatic educational tools and feedback mechanisms that effectively improve inpatient physician recognition of CKD and decrease medication dosing errors in elderly hospitalized patients.

Chronic kidney disease is increasingly recognized as a significant public health issue, especially as our population ages. In the United States, it is estimated that 19.2 million individuals have chronic kidney disease (CKD), with an increasing prevalence in the elderly.1 CKD is associated with a higher mortality rate, as well as an increased risk of having several comorbidities, including anemia, coronary artery disease, and congestive heart failure.24 Early recognition, intervention, and management of patients with CKD by physicians has been shown to slow progression of disease and decrease complications.57 In the hospital setting, patients with CKD are at increased risk of medication dosing errors and acute renal failure (ARF).810

Serum creatinine is the most commonly used laboratory marker for assessing renal function. However, creatinine level is an imprecise measure of overall renal function, especially in older patients. The most recent National Kidney Foundation/ Kidney Disease Outcomes Quality Initiative (K/DOQI) guidelines recommend laboratory reporting of a calculated estimate of GFR.11 Equations used to calculate estimated GFR in adults, including the Cockcroft‐Gault (C‐G) equation, have been shown to provide an estimate of renal function, which can be used to clinically stratify varying levels of impaired renal function.11 Several studies have demonstrated that recognition of CKD by physicians is low in various clinical settings, especially in elderly patients.1215 Compliance with renal‐dose medication guidelines has also frequently been noted to be poor.16, 17

The investigators conducted a chart review study before and after reporting of estimated GFR to physicians in a hospital setting to assess the effect on physician recognition of CKD, the primary outcome. Secondary outcomes included the effect of reporting GFR on physician prescribing behaviors at the time of hospital discharge, including dosing of renal‐dosed antibiotics and use of nonsteroidal anti‐inflammatory (NSAID) and cyclooxygenase type 2 inhibitor (COX‐2) medications.

METHODS

This study was a retrospective chart review, with a prospective chart review as a comparison. Patients selected were admitted to a general medical floor in a 900‐bed academic medical center over the 2 years from 2002 to 2004. Computerized databases of laboratory values and weights obtained during hospitalization were used to select patients who fulfilled the following criteria: age > 65 years, all creatinine values during hospitalization < 1.6 mg/dL, and calculated estimated creatinine clearance (CrCl) < 60 mL/min using the Cockcroft‐Gault (C‐G) formula. The C‐G equation was developed for estimating CrCl and has also been extensively tested as a predictor of GFR. K/DOQI guidelines identify the C‐G equation as the most frequently used equation to estimate GFR in adults.11 To ensure steady‐state renal function, patients were excluded if creatinine varied by more than 0.4 mg/dL during their hospitalization. Based on an anticipated CKD recognition rate of 24%,13 our study sample size was selected to detect a 13% difference in the primary end point between the pre‐ and postintervention groups with 80% power. The study was approved by the institutional review board of the medical school.

Patient charts were reviewed with data obtained from the medical record, including physician notes, discharge summaries, orders, medication lists, and discharge prescriptions. Physician recognition was defined by documentation of CKD, calculated CrCl, or GFR in the physician notes or discharge summary. Charts were reviewed for diagnosis of hypertension (HTN) or diabetes (DM), and discharge medications including NSAID and COX‐2 medications and use and correct dosing of antibiotics requiring dose adjustment in patients with decreased GFR. Aspirin was not included as an NSAID.

For the prospective chart review portion, patients were selected at the time of admission on the basis of the same criteria. A notification was placed in the chart prominently listing the patient's estimated GFR calculated using the C‐G equation. Also included was a list of the stages of chronic renal disease based on the most recent K/DOQI guidelines11 and recommendations on dosing of select renal‐dosed antibiotics. Patients were again excluded if creatinine varied more than 0.4 mg/dL during their hospitalization.

RESULTS

DISCUSSION

This study has confirmed the findings of other investigators that significant CKD is underdiagnosed by physicians, especially in elderly patients with creatinine values within the normal laboratory range.13, 14 Investigators have demonstrated improved documentation of CKD with reporting of creatinine clearance and other simple educational interventions in an outpatient setting.13 In this study, reporting of estimated GFR did result in a significantly higher rate of recognition, but the overall rate was still very low in both groups (3.9%‐12.6%).

Although physician recognition of CKD did increase with the reporting of estimated GFR, this study found no significant impact on prescribing behaviors. Previous studies have shown an association between documentation of specific diagnoses and appropriate physician management.15, 18 However, the current data suggest that simply reporting GFR and increasing physician recognition of CKD may not lead to a significant decrease in medication dosing errors and that more extensive educational measures may be required.

Hospitalist physicians are increasingly serving as the primary caregivers for an aging population of hospitalized patients, and it is imperative that physicians recognize decreased GFR in elderly patients. Clearly, medication dosing errors are occurring in these patients, increasing the risk of adverse drug reactions.19 Elderly patients with renal impairment are also at increased risk of ARF while hospitalized.9, 10 Recognition of CKD by inpatient physicians identifies those patients who require preventive measures including maintenance of adequate hydration and avoidance of hypotension and nephrotoxic agents. Prevention of ARF in these patients has important clinical implications, as the mortality of patients is higher for elderly patients who develop hospital‐acquired ARF than for those presenting with community‐acquired ARF.20 Development of ARF has also been shown to increase length of hospitalization.21 Hospitalist physicians can also use the period of hospitalization as an opportunity to identify patients at risk of progressive CKD and in need of close follow‐up and possible referral to a nephrologist.

This study had several limitations. It was performed at a single institution, and therefore results may not be generalizable to all medical centers. The primary outcome of CKD documentation is an imperfect measure of recognition. The fact that chart documentation of CKD increased following the intervention suggests that documentation is associated with recognition, although it may be an underestimate. The effects of reporting estimated GFR on other secondary outcomes, including dosing of other medications, prevention of ARF, and length of hospital stay were not examined and deserve further investigation. The C‐G equation was chosen to calculate estimated GFR. There may be some advantage to using the Modification of Diet in Renal Disease equation as an alternative, but it is unclear if this is true in elderly female patients, who made up most of our study population.2225 Although using a prediction equation is clearly superior to using creatinine measurement solely to assess renal function in patients, further study is needed to identify the most accurate and effective formula for calculating estimated GFR in elderly patients.

The low rate of recognition of CKD by physicians found in this and other studies demonstrates the strong need for improvement in this area. Low recognition of CKD and a high rate of medication dosing errors despite reporting of the estimated GFR suggest that simply reporting GFR in addition to creatinine level is not sufficient. Further research is indicated to identify pragmatic educational tools and feedback mechanisms that effectively improve inpatient physician recognition of CKD and decrease medication dosing errors in elderly hospitalized patients.

Chronic kidney disease is increasingly recognized as a significant public health issue, especially as our population ages. In the United States, it is estimated that 19.2 million individuals have chronic kidney disease (CKD), with an increasing prevalence in the elderly.1 CKD is associated with a higher mortality rate, as well as an increased risk of having several comorbidities, including anemia, coronary artery disease, and congestive heart failure.24 Early recognition, intervention, and management of patients with CKD by physicians has been shown to slow progression of disease and decrease complications.57 In the hospital setting, patients with CKD are at increased risk of medication dosing errors and acute renal failure (ARF).810

Serum creatinine is the most commonly used laboratory marker for assessing renal function. However, creatinine level is an imprecise measure of overall renal function, especially in older patients. The most recent National Kidney Foundation/ Kidney Disease Outcomes Quality Initiative (K/DOQI) guidelines recommend laboratory reporting of a calculated estimate of GFR.11 Equations used to calculate estimated GFR in adults, including the Cockcroft‐Gault (C‐G) equation, have been shown to provide an estimate of renal function, which can be used to clinically stratify varying levels of impaired renal function.11 Several studies have demonstrated that recognition of CKD by physicians is low in various clinical settings, especially in elderly patients.1215 Compliance with renal‐dose medication guidelines has also frequently been noted to be poor.16, 17

The investigators conducted a chart review study before and after reporting of estimated GFR to physicians in a hospital setting to assess the effect on physician recognition of CKD, the primary outcome. Secondary outcomes included the effect of reporting GFR on physician prescribing behaviors at the time of hospital discharge, including dosing of renal‐dosed antibiotics and use of nonsteroidal anti‐inflammatory (NSAID) and cyclooxygenase type 2 inhibitor (COX‐2) medications.

METHODS

This study was a retrospective chart review, with a prospective chart review as a comparison. Patients selected were admitted to a general medical floor in a 900‐bed academic medical center over the 2 years from 2002 to 2004. Computerized databases of laboratory values and weights obtained during hospitalization were used to select patients who fulfilled the following criteria: age > 65 years, all creatinine values during hospitalization < 1.6 mg/dL, and calculated estimated creatinine clearance (CrCl) < 60 mL/min using the Cockcroft‐Gault (C‐G) formula. The C‐G equation was developed for estimating CrCl and has also been extensively tested as a predictor of GFR. K/DOQI guidelines identify the C‐G equation as the most frequently used equation to estimate GFR in adults.11 To ensure steady‐state renal function, patients were excluded if creatinine varied by more than 0.4 mg/dL during their hospitalization. Based on an anticipated CKD recognition rate of 24%,13 our study sample size was selected to detect a 13% difference in the primary end point between the pre‐ and postintervention groups with 80% power. The study was approved by the institutional review board of the medical school.

Patient charts were reviewed with data obtained from the medical record, including physician notes, discharge summaries, orders, medication lists, and discharge prescriptions. Physician recognition was defined by documentation of CKD, calculated CrCl, or GFR in the physician notes or discharge summary. Charts were reviewed for diagnosis of hypertension (HTN) or diabetes (DM), and discharge medications including NSAID and COX‐2 medications and use and correct dosing of antibiotics requiring dose adjustment in patients with decreased GFR. Aspirin was not included as an NSAID.

For the prospective chart review portion, patients were selected at the time of admission on the basis of the same criteria. A notification was placed in the chart prominently listing the patient's estimated GFR calculated using the C‐G equation. Also included was a list of the stages of chronic renal disease based on the most recent K/DOQI guidelines11 and recommendations on dosing of select renal‐dosed antibiotics. Patients were again excluded if creatinine varied more than 0.4 mg/dL during their hospitalization.

RESULTS

DISCUSSION

This study has confirmed the findings of other investigators that significant CKD is underdiagnosed by physicians, especially in elderly patients with creatinine values within the normal laboratory range.13, 14 Investigators have demonstrated improved documentation of CKD with reporting of creatinine clearance and other simple educational interventions in an outpatient setting.13 In this study, reporting of estimated GFR did result in a significantly higher rate of recognition, but the overall rate was still very low in both groups (3.9%‐12.6%).

Although physician recognition of CKD did increase with the reporting of estimated GFR, this study found no significant impact on prescribing behaviors. Previous studies have shown an association between documentation of specific diagnoses and appropriate physician management.15, 18 However, the current data suggest that simply reporting GFR and increasing physician recognition of CKD may not lead to a significant decrease in medication dosing errors and that more extensive educational measures may be required.

Hospitalist physicians are increasingly serving as the primary caregivers for an aging population of hospitalized patients, and it is imperative that physicians recognize decreased GFR in elderly patients. Clearly, medication dosing errors are occurring in these patients, increasing the risk of adverse drug reactions.19 Elderly patients with renal impairment are also at increased risk of ARF while hospitalized.9, 10 Recognition of CKD by inpatient physicians identifies those patients who require preventive measures including maintenance of adequate hydration and avoidance of hypotension and nephrotoxic agents. Prevention of ARF in these patients has important clinical implications, as the mortality of patients is higher for elderly patients who develop hospital‐acquired ARF than for those presenting with community‐acquired ARF.20 Development of ARF has also been shown to increase length of hospitalization.21 Hospitalist physicians can also use the period of hospitalization as an opportunity to identify patients at risk of progressive CKD and in need of close follow‐up and possible referral to a nephrologist.

This study had several limitations. It was performed at a single institution, and therefore results may not be generalizable to all medical centers. The primary outcome of CKD documentation is an imperfect measure of recognition. The fact that chart documentation of CKD increased following the intervention suggests that documentation is associated with recognition, although it may be an underestimate. The effects of reporting estimated GFR on other secondary outcomes, including dosing of other medications, prevention of ARF, and length of hospital stay were not examined and deserve further investigation. The C‐G equation was chosen to calculate estimated GFR. There may be some advantage to using the Modification of Diet in Renal Disease equation as an alternative, but it is unclear if this is true in elderly female patients, who made up most of our study population.2225 Although using a prediction equation is clearly superior to using creatinine measurement solely to assess renal function in patients, further study is needed to identify the most accurate and effective formula for calculating estimated GFR in elderly patients.

The low rate of recognition of CKD by physicians found in this and other studies demonstrates the strong need for improvement in this area. Low recognition of CKD and a high rate of medication dosing errors despite reporting of the estimated GFR suggest that simply reporting GFR in addition to creatinine level is not sufficient. Further research is indicated to identify pragmatic educational tools and feedback mechanisms that effectively improve inpatient physician recognition of CKD and decrease medication dosing errors in elderly hospitalized patients.