Glucometrics: Where Are We Now?

PURPOSE OF REVIEW: Inpatient glucose data analysis, or glucometrics, has developed alongside the growing emphasis on glycemic control in the hospital. Shortcomings in the initial capabilities for glucometrics have pushed advancements in defining meaningful units of measurement and methods for capturing glucose data. This review addresses the growth in glucometrics and ends with its promising new state. RECENT FINDINGS: Standardization, allowing for benchmarking and purposeful comparison, has been a goal of the field. The National Quality Foundation glycemic measures and recently enacted Center for Medicare and Medicaid Services (CMS) electronic quality measures for hypo- and hyperglycemia have allowed for improved integration and consistency. Prior systems have culminated in an upcoming measure from the Center for Disease Control and Prevention's National Healthcare Safety Network. It is poised to create a new gold standard for glucometrics by expanding and refining the CMS metrics, which should empower both local improvement and benchmarking as the program matures.

Role of continuous glucose monitoring in diabetic patients at high cardiovascular risk: an expert-based multidisciplinary Delphi consensus.

BACKGROUND: Continuous glucose monitoring (CGM) shows in more detail the glycaemic pattern of diabetic subjects and provides several new parameters ("glucometrics") to assess patients' glycaemia and consensually guide treatment. A better control of glucose levels might result in improvement of clinical outcome and reduce disease complications. This study aimed to gather an expert consensus on the clinical and prognostic use of CGM in diabetic patients at high cardiovascular risk or with heart disease. METHODS: A list of 22 statements concerning type of patients who can benefit from CGM, prognostic impact of CGM in diabetic patients with heart disease, CGM use during acute cardiovascular events and educational issues of CGM were developed. Using a two-round Delphi methodology, the survey was distributed online to 42 Italian experts (21 diabetologists and 21 cardiologists) who rated their level of agreement with each statement on a 5-point Likert scale. Consensus was predefined as more than 66% of the panel agreeing/disagreeing with any given statement. RESULTS: Forty experts (95%) answered the survey. Every statement achieved a positive consensus. In particular, the panel expressed the feeling that CGM can be prognostically relevant for every diabetic patient (70%) and that is clinically useful also in the management of those with type 2 diabetes not treated with insulin (87.5%). The assessment of time in range (TIR), glycaemic variability (GV) and hypoglycaemic/hyperglycaemic episodes were considered relevant in the management of diabetic patients with heart disease (92.5% for TIR, 95% for GV, 97.5% for time spent in hypoglycaemia) and can improve the prognosis of those with ischaemic heart disease (100% for hypoglycaemia, 90% for hyperglycaemia) or with heart failure (87.5% for hypoglycaemia, 85% for TIR, 87.5% for GV). The experts retained that CGM can be used and can impact the short- and long-term prognosis during an acute cardiovascular event. Lastly, CGM has a recognized educational role for diabetic subjects. CONCLUSIONS: According to this Delphi consensus, the clinical and prognostic use of CGM in diabetic patients at high cardiovascular risk is promising and deserves dedicated studies to confirm the experts' feelings.

Glucometrics and Insulinometrics.

PURPOSE OF REVIEW: Glucometrics is the systematic analysis of inpatient glucose data and is of key interest as hospitals strive to improve inpatient glycemic control. Insulinometrics is the systematic analysis and reporting of inpatient insulin therapy. This paper reviews some of the questions to be resolved before a national benchmarking process can be developed that will allow institutions to track and compare inpatient glucose control performance against established guidelines. RECENT FINDINGS: There remains a lack of standardization on how glucometircs should be measured and reported. Before hospitals can commit resources to compiling and extracting data, consensus must be reached on such questions as which measures to report, definitions of glycemic targets, and how data should be obtained. Examples are provided on how insulin administration can be measured and reported. Hospitals should begin assessment of glucometrics and insulinometrics. However, consensus and standardization must first occur to allow for a national benchmarking process.

Impact of Continuous Glucose Monitoring and its Glucometrics in Clinical Practice in Spain and Future Perspectives: A Narrative Review.

INTRODUCTION: Continuous glucose monitoring (CGM) devices allow for 24-h real-time measurement of interstitial glucose levels and have changed the interaction between people with diabetes and their health care providers. The large amount of data generated by CGM can be analyzed and evaluated using a set of standardized parameters, collectively named glucometrics. This review aims to provide a summary of the existing evidence on the use of glucometrics data and its impact on clinical practice based on published studies involving adults and children with type 1 diabetes (T1D) in Spain. METHODS: The PubMed and MEDES (Spanish Medical literature) databases were searched covering the years 2018-2022 and including clinical and observational studies, consensus guidelines, and meta-analyses on CGM and glucometrics conducted in Spain. RESULTS: A total of 16 observational studies were found on the use of CGM in Spain, which have shown that cases of severe hypoglycemia in children with T1D were greatly reduced after the introduction of CGM, resulting in a significant reduction in costs. Real-world data from Spain shows that CGM is associated with improved glycemic markers (increased time in range, reduced time below and above range, and glycemic variability), and that there is a relationship between glycemic variability and hypoglycemia. Also, CGM and analysis of glucometrics proved highly useful during the COVID-19 pandemic. New glucometrics, such as the glycemic risk index, or new mathematical approaches to the analysis of CGM-derived glucose data, such as "glucodensities," could help patients to achieve better glycemic control in the future. CONCLUSION: By using glucometrics in clinical practice, clinicians can better assess glycemic control and a patient's individual response to treatment.

Continuous glucose monitoring (CGM) devices are used to monitor glucose levels in real time over 24 h. This has changed the way people with diabetes and their health care providers interact. These devices produce a large amount of data that can be analyzed and evaluated using standardized parameters called glucometrics, which include the time a patient's glucose is in range, below range, and above range, and how much it varies over 24 h. Clinicians can use these data to better assess glycemic control and a patient's individual response to treatment. In this article, we summarize evidence from published studies involving adults and children with type 1 diabetes in Spain to look at how the use of these data has affected clinical practice. Studies have shown that cases of severe low blood glucose in children with diabetes were greatly reduced after the introduction of CGM, resulting in a significant reduction in costs. Data from clinical practice in Spain show that CGM is associated with improved blood glucose markers. Many studies analyzed these data during the COVID-19 pandemic and showed that CGM and analysis of glucometrics were highly useful during this time. New glucometrics and approaches to the analysis of data from CGM could help patients achieve better blood glucose control.

Hyperglycemia in hospital: an independent marker of infection, acute kidney injury & stroke for hospital inpatients.

CONTEXT: Hyperglycemia in hospital inpatients without pre-existing diabetes is associated with increased mortality. However, the independent contribution of hyperglycemia to healthcare-associated infection (HAI), acute kidney injury (AKI), and stroke is unclear. OBJECTIVE: To investigate the relationship between hyperglycemia and adverse clinical outcomes in hospital for patients with and without diabetes. DESIGN: Diabetes IN-hospital: Glucose and Outcomes (DINGO) was a 26-week (October 2019 - March 2020) prospective cohort study. Clinical and glucose data were collected up to the 14th day of admission. Primary stratification was by hyperglycemia, defined as ≥2 random capillary blood glucose (BG) measurements ≥11.1 mmol/L (≥200 mg/dL). Propensity weighting for nine clinical characteristics, was performed to allow interrogation of causality. To maintain the positivity assumption, patients with HbA1c > 12.0% were excluded and pre-hospital treatment not adjusted for. SETTING: The Royal Melbourne Hospital, a quaternary referral hospital in Melbourne, Australia. PATIENTS: Admissions with at least two capillary glucose values and length of stay >24 hours were eligible, with half randomly sampled. OUTCOME MEASURES: HAI, AKI, stroke, and mortality. RESULTS: Of 2,558 included admissions, 1,147 (45%) experienced hyperglycemia in hospital. Following propensity-weighting and adjustment, hyperglycemia in hospital was found to, independently of nine covariables, contribute an increased risk of in-hospital HAI (130 [11.3%] vs.100 [7.1%], adjusted odds ratio [aOR] 1.03, 95% confidence interval [95%CI] 1.01-1.05, p = 0.003), AKI (120 [10.5%] vs. 59 [4.2%], aOR 1.07, 95%CI 1.05-1.09, p < 0.001), and stroke (10 [0.9%] vs. 1 [0.1%], aOR 1.05, 95%CI 1.04-1.06, p < 0.001). CONCLUSIONS: In hospital inpatients (HbA1c ≤ 12.0%), irrespective of diabetes status and pre-hospital glycaemia, hyperglycemia increases the risk of in-hospital HAI, AKI, and stroke compared with those not experiencing hyperglycemia.

Changes in glucometric parameters in people living with diabetes users of the free-style libre 2 system before and after the update possibility to real-time glucose readings in real world practice.

In Spain, from October 10th, 2023, the FreeStyle Libre 2 system offers the possibility to automatically changed from isCGM to rtCGM with a system update. Our study aimed to evaluate the glucometric before and after that date. We didn't find significant changes in TIR, however time of use increased and TBR decreased.

Longitudinal Digital Glucometric Benchmarking to Evaluate the Impact of Institutional Diabetes Care Initiatives in Adults With Diabetes Mellitus Over the 2016-2020 Period.

BACKGROUND: While glucometric benchmarking has been used to compare glucose management between institutions, the value of longitudinal intra-institution benchmarking to assess quality improvement changes is not established. METHODS: A prospective six-month observational study (October 2019-March 2020 inclusive) of inpatients with diabetes or newly detected hyperglycemia admitted to eight medical and surgical wards at the Royal Melbourne Hospital. Networked blood glucose (BG) meters were used to collect capillary BG levels. Outcomes were measures of glycemic control assessed by mean and threshold glucometric measures and comparison with published glucometric benchmarks. Intra-institution comparison was over the 2016-2020 period. RESULTS: In all, 620 admissions (588 unique individuals) met the inclusion criteria, contributing 15 164 BG results over 4023 admission-days. Compared with the 2016 cohort from the same institution, there was increased BG testing (3.8 [SD = 2.2) vs 3.3 [SD = 1.7] BG measurements per patient-day, P < .001), lower mean patient-day mean glucose (PDMG; 8.9 mmol/L [SD = 3.2] vs 9.5 mmol/L [SD = 3.3], P < .001), and reduced mean and threshold measures of hyperglycemia (P < .001 for all). Comparison with institutions across the United States revealed lower incidence of mean PDMG >13.9 or >16.7 mmol/L, and reduced hypoglycemia (<3.9, <2.8, and <2.2 mmol/L), when compared with published benchmarks from an earlier period (2009-2014). CONCLUSIONS: Comprehensive digital-based glucometric benchmarking confirmed institutional quality improvement changes were followed by reduced hyperglycemia and hypoglycemia in a five-year comparison. Longitudinal glucometric benchmarking enables evaluation and validation of changes to institutional diabetes care management practices.

Longitudinal Analysis of Real-World Performance of an Implantable Continuous Glucose Sensor over Multiple Sensor Insertion and Removal Cycles.

The Eversense® Continuous Glucose Monitoring (CGM) System, with the first long-term, implantable glucose sensor, has been commercially available in Europe and South Africa since 2016 for adults with diabetes. The performance of the sensor over multiple, sequential 90- or 180-day cycles from either real-world experience or clinical studies has not been previously published. The Eversense Data Management System (DMS) was used to evaluate the accuracy of General Data Protection Regulation (GDPR)-compliant sensor glucose (SG) values against self-monitoring of blood glucose (SMBG) from June 2016 through August 2019 among patients with at least four sensor cycles from European and South African health care practices. Mean SG and associated measures of variability, glucose management indicator (GMI), and percent and time in various hypoglycemic, euglycemic, and hyperglycemic ranges were calculated for the 24-h time period over each cycle. In addition, transmitter wear time was evaluated across each sensor wear cycle. Among the 945 users included in the analysis, the mean absolute relative difference (standard deviation [SD]) using 152,206, 174,645, 206,024, and 172,587 calibration matched pairs against SMBG was 11.9% (3.6%), 11.5% (4.0%), 11.8% (4.7%), and 11.5% (4.1%) during the first four sensor cycles, respectively. Mean values of the CGM metrics over the first sensor cycle were 156.5 mg/dL for SG, 54.7 mg/dL for SD, 0.35 for coefficient of variation, and 7.04% for GMI. Percent SG at different glycemic ranges was as follows: <54 mg/dL was 1.1% (16 min), <70 mg/dL was 4.6% (66 min), ≥70-180 mg/dL (time in range) was 64.5% (929 min), >180-250 mg/dL was 22.8% (328 min), and >250 mg/dL was 8.1% (117 min). The median transmitter wear time over the first cycle was 83.2%. CGM metrics and wear time were similar over the subsequent three cycles. This real-world evaluation of adult patients with diabetes using the Eversense CGM System in the home setting demonstrated that the implantable sensor provides consistent stable accuracy and CGM metrics over multiple, sequential sensor cycles with no indication of degradation of sensor performance.

Benchmarking blood sugar control in the small rural intensive care unit.

Objective: We sought to determine a benchmark for our blood glucose monitoring and compare our data to published data.Methods: Natividad Medical Center is a 172-bed rural hospital located in Salinas, California.Point of care blood glucose (POC-BG) data was extracted from our EMR for all ICU patients greater than 18 years of age between January 2014 and May 2018. Patient day-weighted mean POC-BGs were calculated for each patient by calculating the average POC-BG per day for each patient. Proportion measurements for each of our measurements groups were recorded (>180 mg/dL, <70 mg/dL, >250 mg/dL and <50 mg/dL). Monthly averages were plotted for visual comparison. Benchmarks were calculated by using 2x Standard Deviation for each measurement group.Results: A total of 3164 patients were found with 21,006 POC-BG measurements. The average POC-BG was 136 mg/dL and median 119 mg/dL. Proportion measurements of monthly day-weighted mean POC-BGs ranged from 0-1.2%, 5.3-44.8%, 0-0.3% and 0.6-16.5%, respectively for less than 70 mg/dL, greater than 180 mg/dL, less than 50 mg/dL and greater than 250 mg/dL. A 2x Standard Deviation was used to calculate our benchmark cut offs which provides a 95% confidence interval and includes 97.5% when neglecting the lower range. Our calculated benchmark values are 1.2, 38.2, 0.19, and 13.1% respectively for measurement groups less than 70 mg/dL, greater than 180 mg/dL, less than 50 mg/dL and greater than 250 mg/dL.Conclusion: Here we present data from a small rural hospital in the Western United States. We calculated benchmarks that could be used to track our ongoing hyper/hypoglycemia improvement projects. We found that when compared to published data, our hyper/hypoglycemia data was comparable to national data.

Real-World Data from the First U.S. Commercial Users of an Implantable Continuous Glucose Sensor.

Background: The Eversense® Continuous Glucose Monitoring (CGM) System, with the first 90-day implantable sensor, received FDA (Food and Drug Administration) approval in June 2018. No real-world experience has been published. Methods: Deidentified sensor glucose (SG) data from August 1, 2018 to May 11, 2019 in the Eversense Data Management System (DMS) were analyzed for the first 205 patients who reached a 90-day wear period on the Eversense CGM system. The mean SG, standard deviation (SD), median interquartile range, coefficient of variation (CV), glucose measurement index (GMI), and percent and time in minutes across glucose ranges were computed for the 24-h time period, the nighttime (00:00-06:00), and by 30-day wear periods. Sensor accuracy, sensor reinsertion rate, transmitter wear time, and safety data were assessed. Results: Of the 205 patients, 129 identified as type 1, 18 as type 2, and 58 were unreported. Fifty were CGM naive, 112 had prior CGM experience, and 43 were unreported. The mean SG was 161.8 mg/dL, SD was 57.4 mg/dL, CV was 0.35, and GMI was 7.18%. Percent SG at <54 mg/dL was 1.2% (18 min), <70 mg/dL was 4.1% (59.7 min), time in range (≥70-180 mg/dL) was 62.3% (897.7 min), >180-250 mg/dL was 21.9% (315.8 min), and >250 mg/dL was 11.6% (166.7 min). Nighttime values were similar. The glucometric values were similar over 30-day time periods of the sensor wear. The mean absolute relative difference (SD) using 27,708 calibration paired points against home blood glucose meters was 11.2% (11.3%). The sensor reinsertion rate was 78.5%. The median transmitter wear time was 83.6%. There were no related serious adverse events. Conclusion: The Eversense real-world data showed promising glycemic results, sensor accuracy, and safety. These data suggest that the Eversense CGM system is a valuable tool for diabetes management.

A stratification approach using logit-based models for confounder adjustment in the study of continuous outcomes.

The control of confounding is an area of extensive epidemiological research, especially in the field of causal inference for observational studies. Matched cohort and case-control study designs are commonly implemented to control for confounding effects without specifying the functional form of the relationship between the outcome and confounders. This paper extends the commonly used regression models in matched designs for binary and survival outcomes (i.e. conditional logistic and stratified Cox proportional hazards) to studies of continuous outcomes through a novel interpretation and application of logit-based regression models from the econometrics and marketing research literature. We compare the performance of the maximum likelihood estimators using simulated data and propose a heuristic argument for obtaining the residuals for model diagnostics. We illustrate our proposed approach with two real data applications. Our simulation studies demonstrate that our stratification approach is robust to model misspecification and that the distribution of the estimated residuals provides a useful diagnostic when the strata are of moderate size. In our applications to real data, we demonstrate that parity and menopausal status are associated with percent mammographic density, and that the mean level and variability of inpatient blood glucose readings vary between medical and surgical wards within a national tertiary hospital. Our work highlights how the same class of regression models, available in most statistical software, can be used to adjust for confounding in the study of binary, time-to-event and continuous outcomes.

Computerized Insulin Order Sets and Glycemic Control in Hospitalized Patients.

BACKGROUND: The purpose of this study was to evaluate the impact of computerized provider order entry subcutaneous insulin order sets on inpatient glycemic control and ordering behavior. METHODS: This was an interrupted time series study of non-intensive care patients at an urban teaching hospital. The primary outcome was proportion of capillary blood glucose in optimal range (4.0-10.0 mmol/L [72-180 mg/dL]) during the 6 months before and after a change to a computerized provider order entry-integrated insulin order set. Secondary outcomes included other measures of glycemia (hyperglycemia [>13.9mmol/L (250 mg/dL)], hypoglycemia [<4.0 mmol/L (72 mg/dL)], severe hypoglycemia [<2.2 mmol/L (40 mg/dL)]) and ordering behavior (use of basal-bolus-correctional insulin regimens). Comparisons of sensitivity-based versus generic correctional scale were also conducted. RESULTS: A total of 63,393 measurements were obtained from June 2011 to June 2012. Order set usage was limited (51.5%). The weekly proportion of capillary blood glucose within the optimal range was not significantly different after the switch to computerized provider order entry order sets (pre-period: 64.9% vs post-period: 65.3%, P = .996). There were no differences in the proportions of moderate or severe hyperglycemia (pre-period: 10.9% vs post-period: 12.0%, P = .061) and hypoglycemia (pre-period: 1.9% vs post-period: 1.6%, P = .144). However, an increased proportion within the optimal range was seen in those with an order set featuring a sensitivity-based correctional scale versus orders without (65.3% vs 55.0%, P <.001). Increased basal-bolus-correctional ordering was observed after protocol implementation (20.3% vs 23.6%, P <.0001). CONCLUSIONS: With low institutional uptake, computerized insulin order sets did not improve inpatient glycemic control.

Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric.

BACKGROUND: Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates and thereby decrease health care expenditures. To evaluate what constitutes effective glucose control, typically several metrics are reported, including time in range, time in mild and severe hypoglycemia, coefficient of variation, and others. To date, there is no one metric that combines all of these individual metrics to give a number indicative of overall performance. We proposed a composite metric that combines 5 commonly reported metrics, and we used this composite metric to compare 6 glucose controllers. METHODS: We evaluated the following controllers: Ideal Medical Technologies (IMT) artificial-intelligence-based controller, Yale protocol, Glucommander, Wintergerst et al PID controller, GRIP, and NICE-SUGAR. We evaluated each controller across 80 simulated patients, 4 clinically relevant exogenous dextrose infusions, and one nonclinical infusion as a test of the controller's ability to handle difficult situations. This gave a total of 2400 5-day simulations, and 585 604 individual glucose values for analysis. We used a random walk sensor error model that gave a 10% MARD. For each controller, we calculated severe hypoglycemia (<40 mg/dL), mild hypoglycemia (40-69 mg/dL), normoglycemia (70-140 mg/dL), hyperglycemia (>140 mg/dL), and coefficient of variation (CV), as well as our novel controller metric. RESULTS: For the controllers tested, we achieved the following median values for our novel controller scoring metric: IMT: 88.1, YALE: 46.7, GLUC: 47.2, PID: 50, GRIP: 48.2, NICE: 46.4. CONCLUSION: The novel scoring metric employed in this study shows promise as a means for evaluating new and existing ICU-based glucose controllers, and it could be used in the future to compare results of glucose control studies in critical care. The IMT AI-based glucose controller demonstrated the most consistent performance results based on this new metric.

Tides of change: improving glucometrics in a large multihospital health care system.

BACKGROUND: This study explores the relationship between education for inpatient diabetes providers and the utilization of insulin order sets, inpatient glucometrics, and length of stay in a large health care system. METHODS: The study included patients with and without the diagnosis of diabetes. An education campaign included provider-directed diabetes education administered via online learning modules and in-person presentations by trained individuals. Relationships among provider-attended diabetes education, order set usage, and inpatient glucometrics (hypo- and hyperglycemia) were analyzed, as well as length of stay. RESULTS: Insulin use knowledge scores for all providers averaged 52%, and improved significantly to 93% (P < .001) by the end of the education intervention period. Likewise utilization of electronic basal-bolus order sets increased from a baseline of 20% for patients receiving insulin to 86% within 6 weeks (P < .01) of introduction of order sets. During the study, the incidence of hypoglycemia and hyperglycemia declined from 1.47% to 1.27% and from 23.21% to 17.80%, respectively. However, these improvements were not sustained beyond the completion of the education campaign. CONCLUSIONS: Education of diabetes health care providers was provided in a large, multihospital system through the use of online learning modules. Adoption of standardized insulin order sets was associated with an improvement in glucometrics. This educational and quality initiative resulted in overall improvements in insulin knowledge, adherence to recommended order sets, inpatient glucometrics, and patient length of stay. These improvements were not sustained, reinforcing the need for repeated educational interventions for those involved in providing inpatient diabetes care.

Novel approach to inpatient glucometric monitoring and variability in a community hospital setting.

Hyperglycemia and glucose variability in the hospital environment are associated with higher rates of complications, longer lengths of stay, and mortality. Standardized metrics are needed to assess the efficacy and safety of glucose management interventions. Glucometric data were collected from 2024 inpatients in a San Diego hospital between 2009 and 2011. As a complementary measure of glucose control, individual patient excursion rates were calculated using counts of distinct excursions from normal to critical glucose ranges >180 or <70 mg/dL. Prediction models for excursion rates were devised, based on patient demographic and clinical characteristics. Patients were predominantly male (51.2%), Caucasian (86.0%), and elderly (median age 72 years). Obesity was prevalent: 32% were overweight and 33% were obese. Median length of hospitalization was 5.0 days (range, 0.8-139.4 days). Unadjusted rate of excursions >180 mg/dL was 0.456 per 24 hours. The proportion of zero excursions decreased as severity of illness decreased, but was unrelated to age. Excursion rates were slightly smaller for major and extreme severity of illness compared to mild or moderate illness severity. Excursion rates did not vary in a monotone fashion with age, although the general pattern reflected a reduction in excursion rates from the first age quartile (19 to 59) through the last age quartile (83 to 100). Using the Akaike information criterion, zero-inflated negative binomial models were identified as appropriate for analyzing glucose excursion rates. Systematic approaches to glucose reporting and management in the hospital environment offer "windows of opportunity" to improve diabetes care.

Using the glucometrics website to benchmark ICU glucose control before and after the NICE-SUGAR study.

Prior to 2009, intensive glycemic control was the standard in main intensive care units (ICUs). Glucose targets have been recalibrated after publication of the NICE-SUGAR study in that year, followed by updated guidelines that endorsed more moderated control. We sought to determine if the prevalence of hyperglycemia in US ICUs had increased after the NICE-SUGAR study's results were reported. We used data from hospitals submitted to the Yale Glucometrics™ website to assess mean blood glucose values, percentage of blood glucose within various ranges, and the prevalence of hypo- and hyperglycemic excursions, based on the patient-day method, comparing the pre- to post-NICE-SUGAR time period. Among more than a total of 2 million blood glucose determinations, comprising 408 790 patient-days, median patient-day blood glucose decreased from 144 mg/dL to 141 mg/dL (P < .001) in the pre- versus post-NICE-SUGAR time period. The percentage of patient days with a mean blood glucose of 110-179 mg/dl increased from 58.3 to 63.6%. The percentage of patient-days with either hypoglycemia (<70 mg/dl) or severe hyperglycemia (≥300 mg/dl) decreased during this time. Our results suggest that glycemic control in US ICUs has improved when comparing time periods before versus after publication of the NICE-SUGAR study. We found no evidence that fewer hypoglycemic events were achieved at the expense of more hyperglycemia.

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.