Diabetes mellitus and blood glucose variability increases the 30-day readmission rate after kidney transplantation.

INTRODUCTION: Inpatient hyperglycemia is an established independent risk factor among several patient cohorts for hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission. METHODS: Data on first-time solitary kidney transplantations were retrieved between September 2015 and December 2018. Information was linked to the electronic health records to determine diagnosis of diabetes mellitus and extract glucometric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on bootstrapped partitions of the data to ensure statistical significance. RESULTS: The cohort included 1036 patients who received kidney transplantation; 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average area under the receiver operator curve (AUC) of 78% with (76.1%, 79.9%) 95% confidence interval (CI). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia, the recipient and donor body mass index (BMI) values, presence of delayed graft function, and African American race as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed. CONCLUSIONS: Suboptimal glucose metrics during hospitalization after kidney transplantation are associated with an increased risk for 30-day hospital readmission. Optimizing hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

Automated Insulin Delivery Technology In The Hospital: Update on Safety and Efficacy Data.

BACKGROUND: Automated insulin delivery systems (AID) are a rapidly growing component in the area of continuous subcutaneous insulin infusion (CSII) therapy. As more patients use these systems in the outpatient setting, it is important to assess safety if their use is allowed to continue in the inpatient setting. METHODS: Analysis was conducted of the records of patients using AID technology upon admission to our hospital between June 2020 and December 2022. Adverse events and glycemic control of AID users were compared to patients using non-AID systems and to patients who had CSII discontinued. RESULTS: There were 185 patients analyzed: 64 on AID, 86 on non-AID, and 35 who had CSII discontinued. The number of patients on AID increased over the course of the observation period, while non-AID users decreased. Pair-wise comparisons indicated that patient-stay mean glucose levels and percentage of hypoglycemic events were similar between all groups, but the percentage of patient hyperglycemic measurements was significantly lower in the AID cohort. No adverse events (diabetic ketoacidosis, pump site complications, equipment malfunction) were reported in any either CSII cohort. CONCLUSION: The type of CSII technology encountered in the hospital is shifting from non-AID towards AID technologies. This analysis supports earlier findings that outpatient AID systems can be successfully transitioned into the inpatient setting. Further study is needed to define if AID systems offer any advantage in glycemic control.

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.

Hybrid Closed-Loop Insulin Pump Technology Can Be Safely Used in the Inpatient Setting.

BACKGROUND: Hybrid closed-loop (HCL) systems, also known as automated insulin delivery systems, are a rapidly growing technology in diabetes management. Because more patients are using these systems in the outpatient setting, it is important to also assess inpatient safety to determine whether HCL use can be continued when those patients become hospitalized. METHODS: The records of patients using HCL technology on admission to our hospital between June 1, 2020, and June 30, 2021, were analyzed. RESULTS: The final analysis included 71 patients divided into 3 categories based on their pump use as an inpatient: (1) HCL users; (2) manual pump users; and (3) pump removed. All cohorts were similar in age, sex, race, hemoglobin A1C at admission, and in Medicare Severity Diagnosis Related Group. Pairwise comparisons indicated that patient-stay mean glucose levels, frequency of patient-specific hyperglycemic measurements, and frequency of hypoglycemic events were similar between all groups. No adverse events, particularly occurrences of diabetic ketoacidosis, pump site complications or infection, or equipment malfunction, were reported. CONCLUSION: This preliminary case series review indicates that continued use of HCL technology in the hospital is safe. Moreover, glycemic control in HCL users was comparable with that in those using insulin pump with manual settings and those converted to basal-bolus insulin therapy.

Nurse adherence to post-hypoglycemic event monitoring for hospitalized patients with diabetes mellitus.

PURPOSE: The aim of this study was to investigate adherence to a posthypoglycemic event-monitoring policy for inpatients with diabetes mellitus receiving insulin therapy. METHODS: We analyzed point-of-care blood glucose data from noncritically ill inpatients receiving insulin therapy who had a hypoglycemic event (<70 mg/dL glucose) from January 3, 2017, through April 7, 2018. Blood glucose was measured until 2 sequential readings showed hypoglycemia resolution. An interval of 10 to 20 min between measurements was defined as compliant with policy. We calculated the median (IQR) time of each interval. RESULTS: We analyzed 896 episodes of hypoglycemia in 426 patients: 698 events had only 1 hypoglycemic measurement; 165 had 2 sequential hypoglycemic measurements; and 33 had 3 sequential hypoglycemic measurements. Median (IQR) times between measurements ranged from 18 (15-23) minutes to 28 (21-38) minutes. For patients with 1 hypoglycemic measurement, less than 50% of follow-up measurements were compliant. Similarly, for those with 2 sequential hypoglycemic values, less than 50% of measurements were compliant; for those with 3 sequential hypoglycemic values, less than 58%. CONCLUSION: Many instances of hypoglycemia had intervals between sequential glucose measurements that were longer than required by policy. These longer-than-expected intervals could place patients at undue risk for a prolonged hypoglycemic event. A better understanding of barriers to post-hypoglycemic event management in inpatients is required. Inpatient nurses, who are at the forefront of assessing and treating patients with hypoglycemia, should be key partners in assessing the algorithms for hypoglycemia care.

An Overview of Safety Issues on Use of Insulin Pumps and Continuous Glucose Monitoring Systems in the Hospital.

PURPOSE OF REVIEW: Summarize safety issues related to patients using insulin pump therapy and continuous glucose monitoring systems (CGMS) in the outpatient setting when they are hospitalized and to review steps that can be taken to mitigate risk associated with use or discontinuation of these devices. RECENT FINDINGS: Two recent consensus conferences were held on the topics of inpatient use of insulin pumps and CGMS devices. In addition to commonly known safety issues (e.g., device malfunction, infection), cybersecurity and the vulnerability of contemporary technology to hacking have emerged. CGMS capabilities offer the promise of advancing the goal for development of glucometry (centralized monitoring of real-time glucose data). Strategies to assuring safe use of insulin pumps and CGMS in the hospital include collaboration between the patient and staff, proper patient selection, and clear policies and procedures outlining safe use. Available data indicates few adverse events associated with these devices in the hospital. Current data suggests, with proper patient selection and a clear process in place for glycemic management, that adverse events are rare, and consensus favors allowing use of the technology in the hospital. The topic of insulin pump and CGMS in the hospital would greatly benefit from more institutions reporting on their experiences and prospective clinical trials.

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.

CO-EXISTING PROSTATE CANCER AND DIABETES MELLITUS: IMPLICATIONS FOR PATIENT OUTCOMES AND CARE.

OBJECTIVE: To investigate how diabetes mellitus (DM) impacts short-term overall survival (OS) for patients with prostate cancer and to examine how prostate cancer impacts glycemic control in DM. METHODS: Patients with DM and prostate cancer newly diagnosed from 2007 to 2014 were identified from the institutional cancer registry and matched to patients with prostate cancer but no DM according to age and year of prostate cancer diagnosis. RESULTS: The study included 276 cases and 276 controls; the mean age was 72 years, most (93%) were white, the most common Gleason score (52%) was 7, and the majority (56%) were tumor stage II. Patients with DM had a higher mean body mass index (P = .03). Alcohol use and performance status differed by group (P<.001), but the 2 groups otherwise were not significantly different. Among those with DM, the mean hemoglobin A1c (HbA1c) was 6.7%. In Kaplan-Meier survival analysis (median follow-up time, 43.7 months), the 5-year OS rates were estimated at 88% and 93% for patients with and without DM, respectively (hazard ratio, 1.64; 95% confidence interval, 0.77-3.46; P = .20). Mean glucose among patients with DM was significantly higher (P<.001) compared with non-DM patients, but mean HbA1c and glucose values did not change significantly over 1 year (P≥.13). CONCLUSION: DM did not adversely impact survival in patients with prostate cancer. In addition, prostate cancer and its treatment did not affect glycemic control. Patients and their providers can be reassured that the concurrent diagnoses do not adversely interact to worsen short-term outcomes. ABBREVIATIONS: DM = diabetes mellitus; HbA1c = hemoglobin A1c; OS = overall survival.

Perioperative Management of Patients with Diabetes and Hyperglycemia Undergoing Elective Surgery.

Diabetes mellitus (DM) and hyperglycemia are associated with increased surgical morbidity and mortality. Hyperglycemia is a determinant of risk of surgical complications and should be addressed across the continuum of surgical care. While data support the need to address hyperglycemia in patients with DM in the ambulatory setting prior to surgery and in the inpatient setting, data are less certain about hyperglycemia occurring during the perioperative period-that part of the process occurring on the day of surgery itself. The definition of "perioperative" varies in the literature. This paper proposes a standardized definition for the perioperative period as spanning the time of patient admission to the preoperative area through discharge from the recovery area. Available information about the impact of perioperative hyperglycemia on surgical outcomes within the framework of that definition is summarized, and the authors' approach to standardizing perioperative care for patients with DM is outlined, including the special case of patients receiving insulin pump therapy. The discussion is limited to adult ambulatory non-obstetric patients undergoing elective surgical procedures under general anesthesia.

RETROSPECTIVE STUDY OF GLYCEMIC CONTROL FOLLOWING TRANSITION FROM THE INTENSIVE CARE UNIT IN A NATIONAL SAMPLE OF U.S. HOSPITALS.

OBJECTIVE: Retrospective study to evaluate glycemic control outcomes after transition from the intensive care unit (ICU) to a non-ICU area in a national sample of U.S. hospitals. METHODS: Mean point-of-care blood glucose (POC-BG) data were assessed overall and at 24 hours before and up to 72 hours after the transition. Comparisons in glucose variability (standard deviation of POC-BG data) were assessed. Impact on glycemic control was evaluated after accounting for hospital characteristics through logistic regression analysis. RESULTS: POC-BG data were obtained from 576 hospitals. Overall mean (SD) POC-BG values in ICU versus non-ICU areas were 176 (24) versus 169 (21) mg/dL (P<.01). Mean (SD) of the ICU POC-BG data were 76 (16) versus 73 (16) mg/dL in the non-ICU data (P<.01). However, when comparing values of POC-BG in the last 24-hour ICU period with those from up to 72 hours posttransition, we found no differences, indicative of overall stable glycemic control and variability after transition. Any deterioration of glucose control following the transition was significantly associated with hospital size (P<.01): the smallest hospitals had the highest percentage of these cases. In addition, geographic region showed significant variability (P = .04), with hospitals in the Midwest and West having the highest proportion of cases in which glycemic control worsened following the transition. CONCLUSION: Glycemic control and variability did not change after transition from the ICU, but outcomes may depend on certain hospital characteristics. Inpatient glycemic control assessment should move beyond just cross-sectional studies and consider the impact of transitioning across inpatient areas. Other statistical approaches to studying this question should be evaluated.

GUIDELINES TO IMPROVE PERIOPERATIVE MANAGEMENT OF DIABETES MELLITUS: ASSESSMENT OF THE IMPACT OF CHANGE ACROSS TIME.

OBJECTIVE: Assess the impact of guidelines on the care of patients with diabetes undergoing elective surgery. METHODS: A multidisciplinary team developed perioperative guidelines. Overall changes in key measures were evaluated after guidelines were introduced and compared with a historical cohort. RESULTS: The historical cohort included 254 surgical procedures, and the post-guidelines implementation cohort comprised 1,387. Glucose monitoring was performed preoperatively in 93% of cases in the post-guidelines implementation cohort and in 88% in the historical cohort (P<.01), but the percentage of cases with measurements decreased over 12 months (from 95% to 91%, P = .044). Glucose was intraoperatively monitored in 67% of cases after guidelines were introduced and in 29% historically (P<.01); the post-guidelines implementation percentage decreased over 12 months from 67% to 55% (P<.01). The performance of glucose monitoring in the postanesthesia care unit (PACU) did not differ (86% vs. 87%, P = .57), but it decreased over 12 months, from 91% to 84% (P<.01). After introduction of the guidelines, insulin use increased in the preoperative, intraoperative, and PACU areas (all P≤.01) but decreased by the end of 12 months (all P<.01). Mean preoperative and PACU glucose levels in the post- guidelines implementation cohort were significantly lower than in the historical cohort (P<.01). CONCLUSION: Multidisciplinary management guidelines for diabetes patients undergoing surgery can improve the performance of key measures of care. Although adherence to recommendations generally remained higher after guideline implementation than in the historical period, the improvement in several measures began to decline over time.

Statistical transformation and the interpretation of inpatient glucose control data.

OBJECTIVE: To introduce a statistical method of assessing hospital-based non-intensive care unit (non-ICU) inpatient glucose control. METHODS: Point-of-care blood glucose (POC-BG) data from hospital non-ICUs were extracted for January 1 through December 31, 2011. Glucose data distribution was examined before and after Box-Cox transformations and compared to normality. Different subsets of data were used to establish upper and lower control limits, and exponentially weighted moving average (EWMA) control charts were constructed from June, July, and October data as examples to determine if out-of-control events were identified differently in nontransformed versus transformed data. RESULTS: A total of 36,381 POC-BG values were analyzed. In all 3 monthly test samples, glucose distributions in nontransformed data were skewed but approached a normal distribution once transformed. Interpretation of out-of-control events from EWMA control chart analyses also revealed differences. In the June test data, an out-of-control process was identified at sample 53 with nontransformed data, whereas the transformed data remained in control for the duration of the observed period. Analysis of July data demonstrated an out-of-control process sooner in the transformed (sample 55) than nontransformed (sample 111) data, whereas for October, transformed data remained in control longer than nontransformed data. CONCLUSION: Statistical transformations increase the normal behavior of inpatient non-ICU glycemic data sets. The decision to transform glucose data could influence the interpretation and conclusions about the status of inpatient glycemic control. Further study is required to determine whether transformed versus nontransformed data influence clinical decisions or evaluation of interventions.

Benchmarking glycemic control in u.s. Hospitals.

OBJECTIVE: Report data on glucose control from 635 U.S. hospitals. METHODS: Point-of-care blood glucose (POC-BG) test data from January through December 2012 from 635 facilities were extracted. Glucose control was evaluated using patient-day-weighted mean POC-BG values. We calculated hypoglycemia and hyperglycemia rates, stratified by presence or absence of intensive care unit (ICU) admission, and we evaluated the relationship between glycemic control and hospital characteristics. RESULTS: In total, 51,375,764 POC-BG measurements (non-ICU, 39,197,762; ICU, 12,178,002) from 2,612,966 patients (non-ICU, 2,415,209; ICU, 575,084) were analyzed. The mean POC-BG was 167 mg/dL for non-ICU patients and 170 mg/dL for ICU patients. The prevalence of hyperglycemia (defined as glucose value >180 mg/dL) was 32.3 and 28.2% in non-ICU and ICU patients, respectively. The prevalence of hypoglycemia (defined as glucose value <70 mg/dL) was 6.1 and 5.6% in non-ICU and ICU patients, respectively. In non-ICU and ICU settings, the patient-day-weighted mean glucose was highest in the smallest hospitals, in rural hospitals, and in hospitals located in the Northeast (all P<.01). For non-ICU patients, we observed a significant difference in the percentage of patient days with hypoglycemia by geographic region only (P<.001). In ICU patients, the prevalence of hypoglycemia varied significantly by hospital type (P<.03) and geographic region (P<.01). CONCLUSION: In this largest POC-BG data set analysis conducted to date, glycemic control varied according to hospital characteristics. Our findings remain consistent with previous reports. Among other variables, national benchmarking of inpatient glucose data will need to consider differences in hospital characteristics.

Care directed by a specialty-trained nurse practioner or physician assistant can overcome clinical inertia in management of inpatient diabetes.

OBJECTIVE: The study's objective was to determine the impact of care directed by a specialty-trained nurse practitioner (NP) or physician assistant (PA) on use of basal-bolus insulin therapy and glycemic control in a population of noncritically ill patients with diabetes. METHODS: A retrospective review of diabetes patients evaluated between July 1, 2011 and December 31, 2011 was conducted. Patients cotreated by a specialty-trained NP/PA were compared with patients who did not receive such care. RESULTS: In total, 171 patients with 222 hospitalizations were cotreated by an NP/PA and 543 patients with 665 hospitalizations were not. Patients with NP/PA involvement were younger, and had more frequent hyperglycemia, and had greater corticosteroid use than patients without NP/PA involvement (P<.01 for all). Basal-bolus insulin therapy was administered to 80% of patients with NP/PA involvement and 34% of patients without it (P<.01). After adjustment for age, sex, hyperglycemia measures, and corticosteroid use, the odds of basal-bolus insulin therapy being administered were increased significantly through NP/PA care (odds ratio, 3.66; 95% confidence interval, 2.36-5.67; P<.01). After adjustment for these variables and insulin regimen, NP/PA care was significantly correlated with lower mean point-of-care glucose levels at 24 hours before discharge (P = .042). CONCLUSION: Diabetes care assisted by an NP/PA trained in inpatient diabetes management results in greater use of recommended basal-bolus insulin therapy and is correlated with lower mean glucose levels before discharge. Adapting this model for use outside an endocrinology consult service needs to be explored so that the expertise can be brought to a broader inpatient population with diabetes.

Overcoming clinical inertia in the management of postoperative patients with diabetes.

OBJECTIVE: To assess the impact of an intervention designed to increase basal-bolus insulin therapy administration in postoperative patients with diabetes mellitus. METHODS: Educational sessions and direct support for surgical services were provided by a nurse practitioner (NP). Outcome data from the intervention were compared to data from a historical (control) period. Changes in basal-bolus insulin use were assessed according to hyperglycemia severity as defined by the percentage of glucose measurements >180 mg/dL. RESULTS: Patient characteristics were comparable for the control and intervention periods (all P≥.15). Overall, administration of basal-bolus insulin occurred in 9% (8/93) of control and in 32% (94/293) of intervention cases (P<.01). During the control period, administration of basal-bolus insulin did not increase with more frequent hyperglycemia (P = .22). During the intervention period, administration increased from 8% (8/96) in patients with the fewest number of hyperglycemic measurements to 60% (57/95) in those with the highest frequency of hyperglycemia (P<.01). The mean glucose level was lower during the intervention period compared to the control period (149 mg/dL vs. 163 mg/dL, P<.01). The proportion of glucose values >180 mg/dL was lower during the intervention period than in the control period (21% vs. 31% of measurements, respectively, P<.01), whereas the hypoglycemia (glucose >70 mg/dL) frequencies were comparable (P = .21). CONCLUSION: An intervention to overcome clinical inertia in the management of postoperative patients with diabetes led to greater utilization of basal-bolus insulin therapy and improved glucose control without increasing hypoglycemia. These efforts are ongoing to ensure the delivery of effective inpatient diabetes care by all surgical services.

Assessing outcomes of intravenous insulin therapy in non-intensive care patients.

OBJECTIVES: The aim of this study was to compare outcomes of using intravenous insulin infusion (IVII) therapy for managing hyperglycemia in a non-intensive care unit (ICU) versus an ICU setting. METHODS: We conducted a retrospective analysis on patients who received IVII for hyperglycemia. The analysis compared variables associated with hypoglycemic events while on IVII, and point-of-care blood glucose control and insulin regimens at discharge. Insulin administration errors occurring on IVII were determined. RESULTS: Between November 2020 and August 2022, 881 patients received 1,106 IVIIs (780 in ICU and 326 non-ICU). A cumulative 468 days were spent on IVII in the non-ICU setting and 1564 in the ICU (total 2,032 days). The frequency of hypoglycemia on IVII was higher when provided in the non-ICU vs ICU (1.4% vs 0.7%), p < 0.01). Non-ICU patients had significantly higher average blood glucose during the last 24 h of the hospital stay (185 mg/dL vs 160 mg/dL, non-ICU vs. ICU, Pp < 0.01) and were more likely discharged with basal-bolus insulin therapy (p < 0.01). After adjusting for other variables, the probability of having hypoglycemia (OR 2.35; 95% CI 1.62-3.42; p < 0.001) was higher for the non-ICU cohort. In addition, patients who received IVII in the non-ICU settings had mean glucose levels nearly 26 mg/dL higher (95% CI 19.40-32.9, p < 0.001) at discharge vs. ICU. Seven cases of insulin errors were reported while on IVII in the non-ICU settings, compared to one in the ICU. CONCLUSIONS: A large number (468) of ICU days were avoided by providing IVII in the non-ICU setting. Of the more than 400 days of IVII therapy provided in the non-ICU, only 7 medication errors occurred. Further studies are needed to optimize IVII strategy for non-ICU patients.

A Consensus Statement for Continuous Glucose Monitoring Metrics for Inpatient Clinical Trials.

Diabetes Technology Society organized an expert consensus panel to develop metrics for research in the use of continuous glucose monitors (CGMs) in a hospital setting. The experts met virtually in small groups both before and after an April 13, 2023 virtual meeting of the entire panel. The goal of the panel was to develop consensus definitions in anticipation of greater use of CGMs in hospital settings in the future. Establishment of consensus definitions of inpatient analytical metrics will be easier to compare outcomes between studies. Panelists defined terms related to 10 dimensions of measurements related to the use of CGMs including (1) hospital hypoglycemia, (2) hospital hyperglycemia, (3) hospital time in range, (4) hospital glycemic variability, (5) hospital glycemia risk index, (6) accuracy of CGM devices and reference methods for CGMs in the hospital, (7) meaningful time blocks for hospital glycemic goals, (8) hospital CGM data sufficiency, (9) using CGM data for insulin dosing, and (10) miscellaneous factors. The panelists voted on 51 proposed recommendations. Based on the panel vote, 51 recommendations were classified as either strong (43) or mild (8). Additional research is needed on CGM performance in the hospital. This consensus report is intended to support that type of research intended to improve outcomes for hospitalized people with diabetes.

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

BACKGROUND: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

The promise of electrochemical impedance spectroscopy as novel technology for the management of patients with diabetes mellitus.

Self-monitoring of blood glucose is the standard of care in management of hyperglycemia among patients with diabetes mellitus. To increase the sensitivity and specificity of current devices, a novel method of detecting glucose using electrochemical impedance spectroscopy (EIS) technology is explored. The enzyme glucose oxidase (GOx) was fixed to gold electrodes and a sine wave of sweeping frequencies was induced using a wide range of concentrations of glucose. Each frequency in the impedance sweep was analyzed for the highest response and R-squared value. The frequency with both factors optimized is specific for the glucose-GOx binding interaction and was determined to be 1.17 kHz in purified solutions in both higher and lower ranges of glucose. The correlation between the impedance response and concentration at the low range of detection (0-100 mg dL(-1) of glucose) was determined to be 3.53 ohm/ln (mg dL(-1)) with an R-squared value of 0.90 with a 39 mg dL(-1) lower limit of detection. The same frequency of 1.17 kHz was verified in whole blood under the same glucose range. The above data confirm that EIS offers a new method of glucose detection as an alternative to current technology in use by patients. Additionally, the unique frequency response of individual markers allows for modulation of signals so that several other markers important in the management of diabetes could be measured with a single sensor.