Continuous Glucose Monitors and Automated Insulin Dosing Systems in the Hospital Consensus Guideline.

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes

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

BackgroundA 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.MethodsWe 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.ResultsThe 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.ConclusionThe 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

1023-P: Current Practices in Racial Equity—Findings from the T1D Exchange Quality Improvement Collaborative (T1DX-QI) in 2022

Background: Medical racism contributes to adverse health outcomes. T1DX-QI is a large population-based cohort engaged in data sharing and quality improvement to drive system changes in type 1 diabetes care. The annual T1DX-QI survey included questions to evaluate racial equity in diabetes care and practices to promote equity. Methods: The annual T1DX-QI survey was administered to participating clinics fall 2022 and had a 94 % response rate. There were 50 responses consisting of 66% pediatric (P) and 34% adult (A) institutions. Questions concerned clinic resources, LGBTQ+ practices, racial equity, patient transition, and reproductive health. Response data were aggregated, summarized and stratified by P/A institutions. Results: Only 21% P and 33% A institutions felt all their team members can articulate how medical racism contributes to adverse diabetes outcomes. Pediatric institutions reported more strategies to address medical racism than adult (3.6 vs 3.1). Organizational strategies to decrease racial discrimination included employee trainings, DEI offices/committees, patient resources and hiring practices. Patient resources include interpreter services, transportation, insurance navigation, and housing and food assistance. Hiring practices included changing prior protocols, hiring from the community, and diversifying workforces. Majority of institutions have offered anti-racism training in the last year (P 85%, A 72%) and annually (P 64%, A 56%). Pediatric teams felt that their anti-racism training was effective more often (P 60%, A 45%) and more commonly were provided resources (67% P vs 47% A) to help address inequities. Conclusion: Despite increased offering in anti-racism training, insufficient institutional support and perceived subeffective training still represent obstacles especially in adult institutions. Sharing effective strategies to address medical racism will help institutions take steps to mitigate inequities. Disclosure J.Sanchez: None. M.Zupa: None. O.Ebekozien: Advisory Panel; Medtronic, Research Support; Eli Lilly and Company, Dexcom, Inc. T1dx-qi collaborative: n/a. J.Cases-villablanca: None. A.Addala: None. A.Mungmode: None. T.Wright: None. R.M.Wolf: Research Support; Dexcom, Inc., Boehringer Ingelheim Inc. A.Ahmann: Advisory Panel; Medtronic. N.N.Mathioudakis: None. J.Ng: Research Support; Sanofi-Aventis U.S. Funding The Leona M. and Harry B. Helmsley Charitable Trus

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

BackgroundA 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.MethodsWe 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.ResultsThe 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.ConclusionThe 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