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

Advances in point-of-care genetic testing for personalized medicine applications

Breakthroughs within the fields of genomics and bioinformatics have enabled the identification of numerous genetic biomarkers that reflect an individual's disease susceptibility, disease progression, and therapy responsiveness. The personalized medicine paradigm capitalizes on these breakthroughs by utilizing an individual's genetic profile to guide treatment selection, dosing, and preventative care. However, integration of personalized medicine into routine clinical practice has been limited-in part-by a dearth of widely deployable, timely, and cost-effective genetic analysis tools. Fortunately, the last several decades have been characterized by tremendous progress with respect to the development of molecular point-of-care tests (POCTs). Advances in microfluidic technologies, accompanied by improvements and innovations in amplification methods, have opened new doors to health monitoring at the point-of-care. While many of these technologies were developed with rapid infectious disease diagnostics in mind, they are well-suited for deployment as genetic testing platforms for personalized medicine applications. In the coming years, we expect that these innovations in molecular POCT technology will play a critical role in enabling widespread adoption of personalized medicine methods. In this work, we review the current and emerging generations of point-of-care molecular testing platforms and assess their applicability toward accelerating the personalized medicine paradigm.

An open source tool to compute measures of inpatient glycemic control: translating from healthcare analytics research to clinical quality improvement

Objectives

The objective of this study is to facilitate monitoring of the quality of inpatient glycemic control by providing an open-source tool to compute glucometrics. To allay regulatory and privacy concerns, the tool is usable locally; no data are uploaded to the internet.

Materials and Methods

We extended code, initially developed for healthcare analytics research, to serve the clinical need for quality monitoring of diabetes. We built an application, with a graphical interface, which can be run locally without any internet connection.

Results

We verified that our code produced results identical to prior work in glucometrics. We extended the prior work by including additional metrics and by providing user customizability. The software has been used at an academic healthcare institution.

Conclusion

We successfully translated code used for research methods into an open source, user-friendly tool which hospitals may use to expedite quality measure computation for the management of inpatients with diabetes.

Investigation of Daily Glucose Profile of Inpatients in Non-endocrinology Departments in Chinese Population

Background: Inpatient hyperglycemia is associated with poor prognosis and increased hospitalization expenses. China has a large population of inpatients with hyperglycemia, but their glucose monitoring states (including preprandial, postprandial and bedtime glucose) are unknown, especially in non-endocrinology departments.

Methods: In this cross-sectional study, 5,790 patients with hyperglycemia from 31 non-endocrinology departments were enrolled, and a total of 1,22,032 point-of-care blood glucose (POC-BG) records were collected. The “patient-day” unit of measure was used as a metric for the inpatient glucose. A total of 2,763 patients from endocrinology wards were included for the comparison of the improvement of glycemic management during hospitalization in non-endocrinology wards.

Results: A total of 61.16% of patient-days had <4 POC-BG tests. Postprandial POC-BG was tested significantly less frequently than preprandial POC-BG (10.60% vs. 58.85% of all records, P < 0.001). The patient-day-weighted mean BG was higher in non-ICU wards than in the ICU (9.72 ± 3.37 vs. 9.00 ± 3.19 mmol/L, P < 0.001). The rate of hyperglycemia (BG >10 mmol/L) was 37.60% in all non-endocrinology wards (ICU vs. non-ICU: 33.19% vs. 39.17%, P < 0.001). In non-ICU wards, the rate of hyperglycemia (BG >10 mmol/L) was significantly higher in surgical wards than in medical wards (40.30% vs. 36.90%, P < 0.001). ICU had a significantly higher rate of achieving the blood glucose target than the non-ICU wards (32.50% vs. 26.38%, P < 0.001). In the non-ICU departments, medical wards had higher rate of achieving the blood glucose target than surgical wards (39.70% vs. 19.08%, P < 0.001). With increasing days of hospitalization, there was no improvement in glycemic control in non-endocrinology wards. The ICU had a significantly higher rate of hypoglycemia than non-ICU wards (4.62% vs. 3.73%, P < 0.05). In non-ICU wards, medical wards had a significantly higher rate of hypoglycemia than surgical wards (5.71% vs. 2.75%, P < 0.05).

Conclusions: Both the frequency of BG monitoring and the daily glucose profile of inpatients in Chinese non-endocrinology departments were less than ideal and need to be urgently improved.

Effect of informatization-based blood glucose team management on the control of hyperglycaemia in noncritical care units

PURPOSE

To provide a new system of in-hospital blood glucose team management combined with a network blood glucose monitoring system and analyse the effect on hyperglycaemic participants' blood glucose control in noncritical care units.

METHODS

Hyperglycaemic participants in noncritical care units were divided into two groups. They underwent active intervention by the hospital's blood glucose management team or the routine consultation group. The better method, based on a shorter length of stay (LOS) and lower hospital cost, could be selected by comparing the two blood glucose management strategies.

RESULTS

Compared with the routine consultation group, the team management group had a higher detection rate of hyperglycaemia (18.49% vs 16.17%, P<0.01) and glycosylated haemoglobin (51.53% vs 30.97%, P<0.01) and a lower incidence rate of hyperglycaemia (59.24% vs 61.59%, P<0.01), severe hyperglycaemia (3.56% vs 5.19%, P<0.01) and clinically significant hypoglycaemia (0.26% vs 0.35%, P<0.05). Simultaneously, blood glucose drift (mmol/L) (2.50 (1.83, 3.25) vs 2.76 (2.01, 3.57), P<0.01), blood glucose coefficient of variation (%) (28.86 (22.70, 34.83) vs 29.80 (23.47, 36.13), P<0.01), maximum blood glucose fluctuation (mmol/L) (9.30 (6.20, 13.10) vs 10.10 (7.00, 14.40), P<0.01) and nosocomial infection (5.42% vs 8.05%, P<0.05) were all lower among participants in the team management group. In addition, the LOS (P<0.001) and hospital costs (P<0.001) of participants were lower in the team management group.

CONCLUSION

In-hospital blood glucose team management combined with a network blood glucose monitoring system effectively improved the blood glucose control and fluctuation levels of participants who were admitted to noncritical care units, thereby reducing LOS and hospital cost.

Challenges of development and implementation of point of care pharmacogenetic testing

INTRODUCTION

Just as technology was the underlying driver of the sequencing of the human genome and subsequent generation of volumes of genome sequence data from healthy and affected individuals, animal, plant, and microbial species alike, so too will technology revolutionize diagnostic testing. One area of intense interest is the use of genetic data to inform decisions regarding drug selection and drug dosing, known as pharmacogenetic (PGx) testing, to improve likelihood of successful treatment outcomes with minimal risks.

AREAS COVERED

This commentary will provide an overview of implementation research of PGx testing, the benefits of point-of-care (POC) testing and overview of POC testing platforms, available PGx tests, and barriers and facilitators to the development and integration of POC-PGx testing into clinical settings. Sources include the published literature, and databases from the Centers for Medicaid and Medicare Services, Food and Drug Administration. Expert commentary: The utilization of POC PGx testing may enable more routine test use, but the development and implementation of such tests will face some barriers before personalized medicine is available to every patient. In particular, provider training, availability of clinical decision supports, and connectivity will be key areas to facilitate routine use.

Utilizing distributional analytics and electronic records to assess timeliness of inpatient blood glucose monitoring in non-critical care wards

Background

Regular and timely monitoring of blood glucose (BG) levels in hospitalized patients with diabetes mellitus is crucial to optimizing inpatient glycaemic control. However, methods to quantify timeliness as a measurement of quality of care are lacking. We propose an analytical approach that utilizes BG measurements from electronic records to assess adherence to an inpatient BG monitoring protocol in hospital wards.

Methods

We applied our proposed analytical approach to electronic records obtained from 24 non-critical care wards in November and December 2013 from a tertiary care hospital in Singapore. We applied distributional analytics to evaluate daily adherence to BG monitoring timings. A one-sample Kolmogorov-Smirnov (1S-KS) test was performed to test daily BG timings against non-adherence represented by the uniform distribution. This test was performed among wards with high power, determined through simulation. The 1S-KS test was coupled with visualization via the cumulative distribution function (cdf) plot and a two-sample Kolmogorov-Smirnov (2S-KS) test, enabling comparison of the BG timing distributions between two consecutive days. We also applied mixture modelling to identify the key features in daily BG timings.

Results

We found that 11 out of the 24 wards had high power. Among these wards, 1S-KS test with cdf plots indicated adherence to BG monitoring protocols. Integrating both 1S-KS and 2S-KS information within a moving window consisting of two consecutive days did not suggest frequent potential change from or towards non-adherence to protocol. From mixture modelling among wards with high power, we consistently identified four components with high concentration of BG measurements taken before mealtimes and around bedtime. This agnostic analysis provided additional evidence that the wards were adherent to BG monitoring protocols.

Conclusions

We demonstrated the utility of our proposed analytical approach as a monitoring tool. It provided information to healthcare providers regarding the timeliness of daily BG measurements. From the real data application, there were empirical evidences suggesting adherence of BG timings to protocol among wards with adequate power for assessing timeliness. Our approach is extendable to other areas of healthcare where timeliness of patient care processes is important.

Point-of-care blood glucose testing for diabetes care in hospitalized patients: an evidence-based review

Glycemic control in hospitalized patients with diabetes requires accurate near-patient glucose monitoring systems. In the past decade, point-of-care blood glucose monitoring devices have become the mainstay of near-patient glucose monitoring in hospitals across the world. In this article, we focus on its history, accuracy, clinical use, and cost-effectiveness. Point-of-care devices have evolved from 1.2 kg instruments with no informatics to handheld lightweight portable devices with advanced connectivity features. Their accuracy however remains a subject of debate, and new standards for their approval have now been issued by both the International Organization for Standardization and the Clinical and Laboratory Standards Institute. While their cost-effectiveness remains to be proved, their clinical value for managing inpatients with diabetes remains unchallenged. This evidence-based review provides an overall view of its use in the hospital setting.

Design and implementation of a web-based reporting and benchmarking center for inpatient glucometrics

Insulin is a top source of adverse drug events in the hospital, and glycemic control is a focus of improvement efforts across the country. Yet, the majority of hospitals have no data to gauge their performance on glycemic control, hypoglycemia rates, or hypoglycemic management. Current tools to outsource glucometrics reports are limited in availability or function. Society of Hospital Medicine (SHM) faculty designed and implemented a web-based data and reporting center that calculates glucometrics on blood glucose data files securely uploaded by users. Unit labels, care type (critical care, non-critical care), and unit type (eg, medical, surgical, mixed, pediatrics) are defined on upload allowing for robust, flexible reporting. Reports for any date range, care type, unit type, or any combination of units are available on demand for review or downloading into a variety of file formats. Four reports with supporting graphics depict glycemic control, hypoglycemia, and hypoglycemia management by patient day or patient stay. Benchmarking and performance ranking reports are generated periodically for all hospitals in the database. In all, 76 hospitals have uploaded at least 12 months of data for non-critical care areas and 67 sites have uploaded critical care data. Critical care benchmarking reveals wide variability in performance. Some hospitals achieve top quartile performance in both glycemic control and hypoglycemia parameters. This new web-based glucometrics data and reporting tool allows hospitals to track their performance with a flexible reporting system, and provides them with external benchmarking. Tools like this help to establish standardized glucometrics and performance standards.

Glucose control in non-critically ill inpatients with diabetes: towards closed-loop

Inpatient glycaemic control remains an important issue due to the increasing number of patients with diabetes admitted to hospital. Morbidity and mortality in hospital are associated with poor glucose control, and cost of hospitalization is higher compared to non-diabetes patients. Guidelines for inpatient glycaemic control in the non-critical care setting have been published. Current recommendations include basal-bolus insulin therapy, regular glucose monitoring, as well as enhancing healthcare provider's role and knowledge. In spite of growing focus, implementation in practice is limited, mainly due to increasing workload burden on staff and fear of hypoglycaemia. Advances in healthcare technology may contribute to an improvement of inpatient diabetes care. Integration of glucose measurements with healthcare records and computerized glycaemic control protocols are currently being used in some institutions. Recent interests in continuous glucose monitoring have led to studies assessing its utilization in inpatients. Automation of glucose monitoring and insulin delivery may provide a safe and efficacious tool for hospital staff to manage inpatient hyperglycaemia, whilst reducing staff workload. This review summarizes the evidence on current approaches to managing inpatient glycaemic control; its utility and limitations. We conclude by discussing the evidence from feasibility studies to date, on the potential use of closed loop in the non-critical care setting and its implication for future studies.

Software-guided insulin dosing: tight glycemic control and decreased glycemic derangements in critically ill patients

OBJECTIVE

To determine whether glycemic derangements are more effectively controlled using software-guided insulin dosing compared with paper-based protocols.

PATIENTS AND METHODS

We prospectively evaluated consecutive critically ill patients treated in a tertiary hospital surgical intensive care unit (ICU) between January 1 and June 30, 2008, and between January 1 and September 30, 2009. Paper-based protocol insulin dosing was evaluated as a baseline during the first period, followed by software-guided insulin dosing in the second period. We compared glycemic metrics related to hyperglycemia, hypoglycemia, and glycemic variability during the 2 periods.

RESULTS

We treated 110 patients by the paper-based protocol and 87 by the software-guided protocol during the before and after periods, respectively. The mean ICU admission blood glucose (BG) level was higher in patients receiving software-guided intensive insulin than for those receiving paper-based intensive insulin (181 vs 156 mg/dL; P=.003, mean of the per-patient mean). Patients treated with software-guided intensive insulin had lower mean BG levels (117 vs 135 mg/dL; P=.0008), sustained greater time in the desired BG target range (95-135 mg/dL; 68% vs 52%; P=.0001), had less frequent hypoglycemia (percentage of time BG level was <70 mg/dL: 0.51% vs 1.44%; P=.04), and showed decreased glycemic variability (BG level per-patient standard deviation from the mean: ±29 vs ±42 mg/dL; P=.01).

CONCLUSION

Surgical ICU patients whose intensive insulin infusions were managed using the software-guided program achieved tighter glycemic control and fewer glycemic derangements than those managed with the paper-based insulin dosing regimen.

Trends in glycemic control over a 2-year period in 126 US hospitals

BACKGROUND

Cross-sectional data on inpatient glucose control in a large sample of US hospitals are now available, but little is known about changes in glycemic control over time in these institutions.

OBJECTIVE

To evaluate trends in glycemic control in US hospitals over 2 years.

DESIGN

Retrospective analysis.

METHODS

Point-of-care blood glucose (POC-BG) test results at 126 hospitals during January to December 2007 and January to December 2009 were extracted using the Remote Automated Laboratory System-Plus (Medical Automation Systems, Charlottesville, VA), and patient-day-weighted mean glucose levels were compared.

SETTING/PATIENTS

Hospitalized patients.

RESULTS

A total of 12,541,929 POC-BG measurements from 1,010,705 patients were analyzed for 2007, and 10,659,418 POC-BG measurements from 656,206 patients were analyzed for 2009. Patient-day-weighted mean POC-BG in 2009 decreased by 5 mg/dL in the non-intensive care unit (non-ICU) data compared with that in 2007 (154 mg/dL vs 159 mg/dL, respectively; P < 0.001). However, POC-BG values were clinically unchanged in intensive care unit (ICU) data from 2009 vs 2007 (167 mg/dL vs 166 mg/dL; P < 0.001). From 2007 to 2009, the proportion of patient-day-weighted mean POC-BGs that were >180 mg/dL declined from 28% to 25% in non-ICU patients (P < 0.001), but not in ICU. Decreases in patient-day-weighted mean POC-BG values in non-ICU patients were significant regardless of hospital size, type, and geographic region (all P < 0.001), but similar decreases were not found in ICU data.

CONCLUSIONS

In this first analysis of glucose changes in US hospitals, improvements over 2 years occurred in non-ICU patients. Ongoing analysis will determine whether this trend continues.

Implementing and evaluating a multicomponent inpatient diabetes management program: putting research into practice

BACKGROUND

Strategies for successful implementation of hospitalwide glucose control efforts were addressed in a conceptual model for the development and implementation of an institutional inpatient glucose management program. CONCEPTUAL MODEL COMPONENTS: The Glucose Steering Committee incrementally developed and implemented hospitalwide glucose policies, coupled with targeted education and clinical decision support to facilitate policy acceptance and uptake by staffwhile incorporating process and outcome measures to objectively assess the effectiveness of quality improvement efforts. The model includes four components: (1) engaging staff and hospital executives in the importance of inpatient glycemic management, (2) educating staff involved in the care of patients with diabetes through structured knowledge dissemination, (3) executing evidence-based inpatient glucose management through development of policies and clinical decision aids, and (4) evaluating intervention effectiveness through assessing process measures, intermediary glucometric outcomes, and clinical and economic outcomes. An educational curriculum for nursing, provider, and pharmacist diabetes education programs and current glucometrics were also developed.

OUTCOMES

Overall the average patient-day-weighted mean blood glucose (PDWMBG) was below the currently recommended maximum of 180 mg/dL in patients with diabetes and hyperglycemia, with a significant decrease in PDWMBG of 7.8 mg/dL in patients with hyperglycemia. The program resulted in an 18.8% reduction in hypoglycemia event rates, which was sustained.

CONCLUSION

Inpatient glucose management remains an important area for patient safety, quality improvement, and clinical research, and the implementation model should guide other hospitals in their glucose management initiatives.

Update on inpatient glycemic control in hospitals in the United States

OBJECTIVE

To provide data on glucose control in hospitals in the United States, analyzing measurements from the largest number of facilities to date.

METHODS

Point-of-care bedside glucose (POC-BG) test results were extracted from 575 hospitals from January 2009 to December 2009 by using a laboratory information management system. Glycemic control for patients in the intensive care unit (ICU) and non-ICU areas was assessed by calculating patient-day-weighted mean POC-BG values and rates of hypoglycemia and hyperglycemia. The relationship between POC-BG levels and hospital characteristics was determined.

RESULTS

A total of 49,191,313 POC-BG measurements (12,176,299 ICU and 37,015,014 non-ICU values) were obtained from 3,484,795 inpatients (653,359 in the ICU and 2,831,436 in non-ICU areas). The mean POC-BG was 167 mg/dL for ICU patients and 166 mg/dL for non-ICU patients. The prevalence of hyperglycemia (>180 mg/dL) was 32.2% of patient-days for ICU patients and 32.0% of patient-days for non-ICU patients. The prevalence of hypoglycemia (<70 mg/dL) was 6.3% of patient-days for ICU patients and 5.7% of patient-days for non-ICU patients. Patient-day-weighted mean POC-BG levels varied on the basis of hospital size (P<.01), type (P<.01), and geographic location (P<.01) for ICU and non-ICU patients, with larger hospitals (≥400 beds), academic hospitals, and US hospitals in the West having the lowest mean POC-BG values. The percentage of patient-days in the ICU characterized by hypoglycemia was highest among larger and academic hospitals (P<.05) and least among hospitals in the Northeast (P<.001).

CONCLUSION

Hyperglycemia is common in hospitals in the United States, and glycemic control may vary on the basis of hospital characteristics. Increased hospital participation in data collection may support a national benchmarking process for the development of optimal practices to manage inpatient hyperglycemia.

Outpatient-to-inpatient transition of insulin pump therapy: successes and continuing challenges

BACKGROUND

Insulin pump therapy is a complex technology prone to errors when employed in the hospital setting. When patients on insulin pump therapy require hospitalization, practitioners caring for them must decide whether to allow continued pump use. We provide the largest review regarding transitioning insulin pump therapy from the outpatient to inpatient setting.

METHOD

Records of inpatient insulin pump users were retrospectively analyzed at a metropolitan Phoenix hospital between January 2006 and December 2009. Adherence to institutional procedures on insulin pump use was assessed, glycemic control was determined, and adverse events were examined.

RESULTS

We examined records on 65 patients with insulin pumps, totaling 125 hospitalizations. Mean (standard deviation) patient age was 55 (17) years, diabetes duration was 27 (14) years, pump duration was 6 (5) years, length of hospital stay was 4.7 (6.3) days, hemoglobin A1c was 7.3 (1.3)%, 85% had type 1 diabetes mellitus, 57% were women, and 97% were white. Admissions involving insulin pumps increased (23 in 2006, 17 in 2007, 40 in 2008, and 45 in 2009). Insulin pump therapy was continued in 83 (66%) hospitalizations. Among these hospitalizations, endocrinology consultations were obtained in 89%, consent agreements were found in 83%, insulin pump order sets were completed in 89%, admission glucose was checked in 100%, and nursing assessments of pump insertion sites were documented in 89%, but bedside insulin pump flow sheets were found in only 55%. Mean glucose of 175 (57) mg/dl was not significantly different than that in hospitalizations where insulin pumps were discontinued [175 (42) mg/dl] or used intermittently [177 (7) mg/dl]. There was one instance of a pump catheter kinking; however, no other adverse events (pump site infections, mechanical pump failure, diabetic ketoacidosis) were observed, and there were no use-related fatalities.

CONCLUSIONS

Most patients using insulin pumps can safely have their therapy transitioned when hospitalized. A policy on inpatient continuous subcutaneous insulin infusion use can be successfully implemented. Compliance with required procedures can be achieved, although there was room to improve adherence with some process measures. Further study is needed to determine how to optimize glycemic control when pumps are allowed during hospitalization.

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.

Institutional blood glucose monitoring system for hospitalized patients: an integral component of the inpatient glucose control program

BACKGROUND

The ability to measure patient blood glucose levels at bedside in hospitalized patients and to transmit those values to a central database enables and facilitates glucose control and follow-up and is an integral component in the care of the hospitalized diabetic patient.

OBJECTIVE

The goal of this study was to evaluate the performance of an institutional glucometer employed in the framework of the Program for the Treatment of the Hospitalized Diabetic Patient (PTHDP) at E. Wolfson Medical Center, Holon, Israel.

METHODS

As part of the program to facilitate glucose control in hospitalized diabetic patients, an institutional glucometer was employed that permits uploading of data from stands located in each inpatient department and downloading of that data to a central hospital-wide database. Blood glucose values from hospitalized diabetic patients were collected from August 2007 to October 2008. The inpatient glucose control program was introduced gradually beginning January 2008.

RESULTS

During the follow-up period, more than 150,000 blood glucose measures were taken. Mean glucose was 195.7 +/- 99.12 mg/dl during the follow-up period. Blood glucose values declined from 206 +/- 105 prior to PTHDP (August 2007-December 2007) to 186 +/- 92 after its inception (January 2008-October 2008). The decline was associated significantly with time (r = 0.11, p < 0.0001). The prevalence of blood glucose values lower than 60 mg/dl was 1.48% [95% confidence interval (CI) 0.36%] prior to vs 1.55% (95% CI 0.37%) following implementation of the PTHDP. Concomitantly, a significant increase in the proportion of blood glucose values between 80 and 200 mg/dl was observed, from 55.5% prior to program initiation vs 61.6% after program initiation (p < 0.0001).

CONCLUSIONS

The present study was designed to observe changes in institution-wide glucose values following implementation of the PTHDP. Information was extracted from the glucometer system itself. Because the aforementioned study was not a clinical trial, we cannot rule out that factors other than introduction of the program could explain some of the variability observed. With these limitations in mind, it nevertheless appears that the PTHDP, of which the institutional glucometer is an integral, essential component, was associated with improved blood glucose values in the hospitalized diabetic patient.

Continuous subcutaneous insulin infusion (insulin pump) therapy can be safely used in the hospital in select patients

OBJECTIVE

To analyze data on inpatient insulin pump use and examine staff compliance with hospital procedures, glycemic control, and safety.

METHODS

We conducted a retrospective review of charts and bedside glucose data for patients who had been receiving outpatient insulin pump therapy and were admitted to our teaching hospital between November 1, 2005, and February 8, 2008.

RESULTS

During the study period, there were 50 hospitalizations involving 35 patients who had been receiving outpatient insulin pump therapy. The mean age and duration of diabetes of the 35 patients was 55 years and 32 years, respectively. Sixty-six percent were women, and 91% had type 1 diabetes. Patients in 31 of the hospitalizations (62%) were deemed candidates for continued insulin pump therapy during their stay. Of the 31 hospitalizations, 80% had the presence of the pump documented at admission; 100% had an admission glucose value; 77% had documentation of signed patient consent; 81% had evidence of completed preprinted insulin pump orders; 77% received an endocrine consultation; and 68% had a completed bedside flow sheet. Patients continuing insulin pump therapy had mean bedside glucose levels similar to those whose pump therapy was discontinued (P = .11); however, the proportion of hypoglycemic events was lower among insulin pump users (P<.01) than among nonusers.

CONCLUSIONS

Insulin pump therapy is safe for select inpatients. Overall, staff compliance with procedures was high, although we identified areas for improvement. Continued study is needed on the effectiveness of insulin pump therapy in controlling inpatient hyperglycemia.

Temporal and geographic patterns of hypoglycemia among hospitalized patients with diabetes mellitus

BACKGROUND

Hypoglycemia is often cited as a barrier to achieving inpatient glycemic targets. We sought to characterize hypoglycemic events in our institution by work-shift cycle and by specific treatment area.

METHODS

Capillary (bedside) and blood (laboratory) glucose values of <70 mg/dl for patients with either a known diagnosis of diabetes or with evidence of hyperglycemia were abstracted from our laboratory database for hospitalizations between October 1, 2007, and February 3, 2008. Hypoglycemic events were analyzed by 12 h nursing work-shift cycles (day shift, 07:00 to 18:59; night shift, 19:00 to 06:59) and by the six medical, surgical, and intensive care areas in the hospital (designated areas 1 to 6).

RESULTS

We identified 206 individual patients with either diabetes or hyperglycemia (mean age, 67 years; 56% men; 83% white) who had 423 hypoglycemic events. There were 78% more hypoglycemic events during the night shift (n = 271 events in 128 individual patients) than during the day shift (n = 152 events in 96 individual patients). Most of the night-shift hypoglycemic measurements were detected between 04:00 and 04:59 or 06:00 and 06:59. The mean hypoglycemic level was comparable between shifts (p = .79) and across the six inpatient areas. The number of hypoglycemic events per person increased with lengths of hospital stay >5 days. The prevalence of hypoglycemia varied across patient care areas within the hospital, with most (28%) detected in one area of the hospital.

CONCLUSION

There are temporal and geographic patterns in the occurrence of hypoglycemia among patients with diabetes or hyperglycemia in our hospital. Further study should focus on the reasons underlying these variations so that specific interventions can address the risk of hypoglycemia during peak times and places.

Perceptions of resident physicians about management of inpatient hyperglycemia in an urban hospital

BACKGROUND

Information regarding practitioner beliefs about inpatient diabetes care is limited.

OBJECTIVE

To assess resident physician attitudes about inpatient hyperglycemia and determine perceived barriers to optimal glycemic control in an urban hospital setting.

DESIGN

A previously developed questionnaire was modified and administered. Residents were asked about the importance of inpatient glucose control, desirable glucose ranges, and problems encountered when managing hyperglycemia.

SETTING

Urban teaching hospital.

RESULTS

Of 85 resident physicians, 66 completed the survey (mean age, 31 years; 47% men; 33% in first residency year). Most respondents categorized glucose control as "very important" in critically-ill and perioperative patients but only "somewhat important" in non-critically-ill patients. Most residents said they would target a therapeutic glucose range within the recommended levels. Most residents (88%) also said they felt "very comfortable" or "somewhat comfortable" using subcutaneous insulin therapy, whereas some were "not at all comfortable" with either subcutaneous (11%) or intravenous (18%) administration. In general, respondents were not very familiar with existing institutional policies and preprinted order sets. The most commonly reported barrier to management of inpatient hyperglycemia was lack of knowledge about appropriate insulin regimens and their use. Anxiety about hypoglycemia was only the third most frequent concern.

CONCLUSION

Most residents acknowledged the importance of good glucose control in hospitalized patients and chose target glucose ranges consistent with existing guidelines. Lack of knowledge about insulin treatment options was the most commonly cited barrier to ideal management. Educational programs should emphasize inpatient treatment strategies for glycemic control.

Understanding and improving management of inpatient diabetes mellitus: the Mayo Clinic Arizona experience

We present an overview of strategies our institution has taken to understand the state of its inpatient diabetes management. We first describe how we utilized information systems to assess inpatient glycemic control and insulin management in noncritically ill patients and discuss our findings regarding mean bedside glucose levels, the prevalence and frequency hypoglycemic and hyperglycemic events, the patterns of insulin therapy, and evidence of inpatient clinical inertia. We also review the development of a survey to determine practitioner attitudes and beliefs about inpatient diabetes. Results of this survey study found that, in general, practitioners believed in the importance of controlling hyperglycemia but were not comfortable with many aspects of inpatient diabetes care, particularly with the use of insulin. Finally, we suggest steps to follow in developing a quality-improvement program for hospitals.

An internet service supporting quality assessment of inpatient glycemic control

BACKGROUND

Several studies have linked the maintenance of normoglycemia in acutely ill inpatients with improved clinical outcomes. We previously proposed a few standard definitions for monitoring inpatient glycemic control, or "glucometrics." In clinical practice, limited data management resources for developing and refining measurement protocols can slow quality improvement efforts. With regard to glucometrics, there are few baseline data regarding the quality of hospital glycemic management. Furthermore, there are no reliable methods for hospitals to gauge the progress of their quality improvement efforts.

METHODS

We built a novel Web application that calculates glucometrics on anonymized blood glucose data files uploaded by registered users. This Web site also collects many key characteristics of the users and institutions utilizing the service. This application will allow us to pool data from several institutions to calculate aggregate glucometrics, providing baseline data for quality improvement efforts and ongoing metrics for institutions to gauge their progress.

RESULTS

The application, accessible at http://metrics.med.yale.edu, has already drawn visitors from several countries. A number of users have registered formally, and some have begun to upload institutional glucose data. The application delivers detailed glucometrics reports to registered users, complete with visual displays. Quality improvement staff from large health systems have been the predominant users.

CONCLUSIONS

We have created an open access Web application to facilitate quality monitoring and improvement efforts-as well as clinical research-regarding inpatient glycemic management. If employed widely, this application could help establish national performance standards for glycemic control.