Inpatient glucose control: a glycemic survey of 126 U.S. hospitals.

BACKGROUND: Despite increased awareness of the value of treating inpatient hyperglycemia, little is known about glucose control in U.S. hospitals. METHODS: The Remote Automated Laboratory System-Plus (RALS-Plus Medical Automation Systems, Charlottesville, VA) was used to extract inpatient point-of-care bedside glucose (POC-BG) tests from 126 hospitals for the period January to December 2007. Patient-day-weighted mean POC-BG and hypoglycemia/hyperglycemia rates were calculated for intensive care unit (ICU) and non-ICU areas. The relationship of POC-BG levels with hospital characteristics was determined. RESULTS: A total of 12,559,305 POC-BG measurements were analyzed: 2,935,167 from the ICU and 9,624,138 from the non-ICU. Patient-day-weighted mean POC-BG was 165 mg/dL for ICU and 166 mg/dL for non-ICU. Hospital hyperglycemia (>180 mg/dL) prevalence was 46.0% for ICU and 31.7% for non-ICU. Hospital hypoglycemia (<70 mg/dL) prevalence was low at 10.1% for ICU and 3.5% for non-ICU. For ICU and non-ICU there was a significant relationship between number of beds and patient-day-weighted mean POC-BG levels, with larger hospitals (> or = 400 beds) having lower patient-day weighted mean POC-BG per patient day than smaller hospitals (<200 beds, P < 0.001). Rural hospitals had higher POC-BG levels compared to urban and academic hospitals (P < 0.05), and hospitals in the West had the lowest values. CONCLUSIONS: POC-BG data captured through automated data management software can support hospital efforts to monitor the status of inpatient glycemic control. From these data, hospital hyperglycemia is common, hypoglycemia prevalence is low, and POC-BG levels vary by hospital characteristics. Increased hospital participation in data collection and reporting may facilitate the creation of a national benchmarking process for the development of best practices and improved inpatient hyperglycemia management.

Computerized intensive insulin dosing can mitigate hypoglycemia and achieve tight glycemic control when glucose measurement is performed frequently and on time.

INTRODUCTION: Control of blood glucose (BG) in critically ill patients is considered important, but is difficult to achieve, and often associated with increased risk of hypoglycemia. We examined the use of a computerized insulin dosing algorithm to manage hyperglycemia with particular attention to frequency and conditions surrounding hypoglycemic events. METHODS: This is a retrospective analysis of adult patients with hyperglycemia receiving intravenous (IV) insulin therapy from March 2006 to December 2007 in the intensive care units of 2 tertiary care teaching hospitals. Patients placed on a glycemic control protocol using the Clarian GlucoStabilizer IV insulin dosing calculator with a target range of 4.4-6.1 mmol/L were analyzed. Metrics included time to target, time in target, mean blood glucose +/- standard deviation, % measures in hypoglycemic ranges <3.9 mmol/L, per-patient hypoglycemia, and BG testing interval. RESULTS: 4,588 ICU patients were treated with the GlucoStabilizer to a BG target range of 4.4-6.1 mmol/L. We observed 254 severe hypoglycemia episodes (BG <2.2 mmol/L) in 195 patients, representing 0.1% of all measurements, and in 4.25% of patients or 0.6 episodes per 1000 hours on insulin infusion. The most common contributing cause for hypoglycemia was measurement delay (n = 170, 66.9%). The median (interquartile range) time to achieve the target range was 5.9 (3.8 - 8.9) hours. Nearly all (97.5%) of patients achieved target and remained in target 73.4% of the time. The mean BG (+/- SD) after achieving target was 5.4 (+/- 0.52) mmol/L. Targeted blood glucose levels were achieved at similar rates with low incidence of severe hypoglycemia in patients with and without diabetes, sepsis, renal, and cardiovascular disease. CONCLUSIONS: Glycemic control to a lower glucose target range can be achieved using a computerized insulin dosing protocol. With particular attention to timely measurement and adjustment of insulin doses the risk of hypoglycemia experienced can be minimized.

Assessing the performance of handheld glucose testing for critical care.

BACKGROUND: We assessed the performance of a point-of-care (POC) glucose meter system (GMS) with multitasking test strip by using the locally-smoothed (LS) median absolute difference (MAD) curve method in conjunction with a modified Bland-Altman difference plot and superimposed International Organization for Standardization (ISO) 15197 tolerance bands. We analyzed performance for tight glycemic control (TGC). METHODS: A modified glucose oxidase enzyme with a multilayer-gold, multielectrode, four-well test strip (StatStriptrade mark, NOVA Biomedical, Waltham, MA) was used. There was no test strip calibration code. Pragmatic comparison was done of GMS results versus paired plasma glucose measurements from chemistry analyzers in clinical laboratories. Venous samples (n = 1,703) were analyzed at 35 hospitals that used 20 types of chemistry analyzers. Erroneous results were identified using the Bland-Altman plot and ISO 15197 criteria. Discrepant values were analyzed for the TGC interval of 80-110 mg/dL. RESULTS: The GMS met ISO 15197 guidelines; 98.6% (410 of 416) of observations were within tolerance for glucose <75 mg/dL, and for > or =75 mg/dL, 100% were within tolerance. Paired differences (handheld minus reference) averaged -2.2 (SD 9.8) mg/dL; the median was -1 (range, -96 to 45) mg/dL. LS MAD curve analysis revealed satisfactory performance below 186 mg/dL; above 186 mg/dL, the recommended error tolerance limit (5 mg/dL) was not met. No discrepant values appeared. All points fell in Clarke Error Grid zone A. Linear regression showed y = 1.018x - 0.716 mg/dL, and r2 = 0.995. CONCLUSIONS: LS MAD curves draw on human ability to discriminate performance visually. LS MAD curve and ISO 15197 performance were acceptable for TGC. POC and reference glucose calibration should be harmonized and standardized.

Safety and effectiveness of a computerized subcutaneous insulin program to treat inpatient hyperglycemia.

BACKGROUND: This proof of concept study was designed to evaluate the safety and effectiveness of a computerized insulin program, the Clarian GlucoStabilizer Subcutaneous Insulin Program (CGS-SQ). This paper discusses the CGS-SQ's impact on the glycemic control of hospitalized patients with hyperglycemia. METHODS: Patients at Methodist and Indiana University Hospitals requiring subcutaneous insulin were treated using the CGS-SQ. This program calculates subcutaneous bolus insulin doses based on the current blood glucose (BG), using an insulin sensitivity factor, the number of grams of carbohydrates eaten, and an insulin-to-carbohydrate ratio, with a goal of maintaining the patient's BG in a prespecified target range. The target range, insulin sensitivity factor, and insulin-to-carbohydrate ratio are established by the physician. RESULTS: From April 2006 to September 2007, the CGS-SQ treated 1772 patients at Methodist and Indiana University Hospitals, with 46,575 BGs in its database. For these patients, the average BG was 158.3 mg/dl, 40.5% percent of BGs were in the default target range of 100-150 mg/dl, and 69.8% were in the wider range of 70-180 mg/dl. The hypoglycemia (BG <40 mg/dl) rate was 0.18%. CONCLUSIONS: The CGS-SQ provided a means to deliver insulin in a standardized manner, resulting in satisfactory BG control with a low hypoglycemia rate, thus serving as a tool for safe and effective insulin therapy for hospitalized patients.

Evaluation of point-of-care glucose testing accuracy using locally-smoothed median absolute difference curves.

BACKGROUND: We introduce locally-smoothed (LS) median absolute difference (MAD) curves for the evaluation of hospital point-of-care (POC) glucose testing accuracy. METHODS: Arterial blood samples (613) were obtained from a university hospital blood gas laboratory. Four hospital glucose meter systems (GMS) were tested against the YSI 2300 glucose analyzer for paired reference observations. We made statistical comparisons using conventional methods (e.g., linear regression, mean absolute differences). RESULTS: Difference plots with superimposed ISO 15197 tolerance bands showed bias, scatter, heteroscedasticity, and erroneous results well. LS MAD curves readily revealed GMS accuracy patterns. Performance in hypoglycemic and hyperglycemic ranges erratically exceeded the recommended LS MAD error tolerance limit (5 mg/dl). Some systems showed acceptable (within LS MAD tolerance) or nearly acceptable performance in and around a tight glycemic control (TGC) interval of 80-110 mg/dl. Performance patterns varied in this interval, creating potential for discrepant therapeutic decisions. CONCLUSIONS: Erroneous results demonstrated by ISO 15197-difference plots must be carefully considered. LS MAD curves draw on the unique human ability to recognize patterns quickly and discriminate accuracy visually. Performance standards should incorporate LS MAD curves and the recommended error tolerance limit of 5 mg/dl for hospital bedside glucose testing. Each GMS must be considered individually when assessing overall performance for therapeutic decision making in TGC.

Inpatient point-of-care bedside glucose testing: preliminary data on use of connectivity informatics to measure hospital glycemic control.

BACKGROUND: Point-of-care (POC) bedside glucose (BG) testing and timely evaluation of its effectiveness are important components of hospital inpatient glycemic control programs. We describe a new technology to evaluate inpatient POC-BG testing and report preliminary results of inpatient glycemic control from 10 U.S. hospitals. METHODS: We used the Remote Automated Laboratory System RALS-Tight Glycemic Control Module (TGCM) (Medical Automation Systems, Charlottesville, VA) connected to the RALS-Plus to extract and analyze inpatient POC-BG tests from 10 U.S. hospitals for a 3-month period. POC-BG measurements were evaluated in aggregate from all 10 facilities for intensive care unit (ICU), non-ICU, and ICU + non-ICU combined. RESULTS: A total of 742,154 POC-BGs were analyzed. The combined (ICU + non-ICU) mean POC-BG was 159 mg/dL, compared with 146 mg/dL for the ICU and 164 mg/dL for non-ICU. The proportion of hypoglycemic values (<70 mg/dL) was low at 4%, but the percentage of measurements that would be considered hyperglycemic (>180 mg/dL) was high, with more than 30% of values in the non-ICU and 20% in the ICU being elevated. CONCLUSIONS: POC-BG data can be captured through automated data management software and can support hospital efforts to evaluate and monitor the status of inpatient glycemic control. These preliminary data suggest that there is a need to conduct broad-based efforts to improve inpatient glucose management. Increasing hospital participation in data collection has the potential to create a national benchmarking process for the development of best practices and improved inpatient hyperglycemia management.