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Background: The COVID-19 pandemic necessitated rapid implementation of continuous glucose monitoring (CGM) in the intensive care unit (ICU). Although rarely reported, perceptions from nursing staff who used the systems are critical for successful implementation and future expanded use of CGM in the inpatient setting. Methods: A 22-item survey focused on CGM use was distributed to ICU nurses at two large academic medical centers in the United States in 2022. Both institutions initiated inpatient CGM in the spring of 2020 using the same CGM+point of care (POC) hybrid protocol. The survey employed a 1- to 5-point Likert scale regarding CGM sensor insertion, accuracy, acceptability, usability, training, and perceptions on workload. Results: Of the 71 surveys completed, 68 (96%) nurses reported they cared for an ICU patient on CGM and 53% reported they had independently performed CGM sensor insertion. The ICU nurses overwhelmingly reported that CGM was accurate, reduced their workload, provided safer patient care, and was preferred over POC glucose testing alone. Interestingly, nearly half of nurses (49%) reported that they considered trend arrows in dosing decisions although trends were not included in the CGM+POC hybrid protocol. Nurses received training through multiple modalities, with the majority (80%) of nurses reporting that CGM training was sufficient and prepared them for its use. Conclusion: These results confirm nursing acceptance and preference for CGM use within a hybrid glucose monitoring protocol in the ICU setting. These data lay a blueprint for successful implementation and training strategies for future widespread use.

The use of diabetes technology, including insulin pumps, continuous glucose monitoring devices, and automated insulin delivery systems, has increased significantly in recent years. As more people with diabetes adopt technology in the outpatient setting, we are seeing these devices more frequently in the inpatient setting. This review offers best-practice guidelines for the continuation of personal diabetes technology use in the inpatient setting. It describes policy and guideline stipulations, roles and responsibilities, and device- and brand-specific considerations. Although these devices are not approved for inpatient use by the U.S. Food and Drug Administration, there is general expert consensus that the continuation of personal diabetes devices during hospitalization is appropriate for patients who have sufficient knowledge, are not critically ill, and retain sufficient mental capacity during an acute illness. Health care systems and inpatient providers need to understand the benefits and limitations of personal diabetes technology use during hospitalization.

Audrey White,1 Elizabeth Buschur,2 Cara Harris,2 Michael L Pennell,3 Adam Soliman,2 Kathleen Wyne,2 Kathleen M Dungan2 1Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA; 2Division of Endocrinology, Diabetes & Metabolism, The Ohio State University, Columbus, OH, 43220, USA; 3The Ohio State University College of Public Health, Division of Biostatistics, Columbus, OH, 43210, USACorrespondence: Kathleen M Dungan, The Ohio State University, Division of Endocrinology, Diabetes and Metabolism, 5th Floor McCampbell Hall, 1581 Dodd Drive, Columbus, OH, 43210-1296, USA, Email kathleen.dungan@osumc.eduObjective: To evaluate the relationship between health literacy, social support, and self-efficacy as predictors of change in A1c and readmission among hospitalized patients with type 2 diabetes (T2D).Methods: This is a secondary analysis of patients with T2D (A1c > 8.5%) enrolled in a randomized trial in which health literacy (Newest Vital Sign), social support (Multidimensional Scale of Perceived Social Support), and empowerment (Diabetes Empowerment Scale-Short Form) was assessed at baseline. Multivariable models evaluated whether these concepts were associated with A1c reduction at 12 weeks (absolute change, % with > 1% reduction, % reaching individualized target) and readmission (14 and 30 days).Results: A1c (N=108) decreased > 1% in 60%, while individualized A1c target was achieved in 31%. After adjustment for baseline A1c and potential confounders, health literacy was associated with significant reduction in A1c (Estimate − 0.21, 95% CI − 0.40, − 0.01, p=0.041) and > 1% decrease in A1c (OR 1.37, 95% CI 1.08, 1.73, p=0.009). However, higher social support was associated with greater adjusted odds of reaching the individualized A1c target (OR 1.63, 95% CI 1.04, 2.55, p=0.32). Both higher empowerment (OR 0.23, 95% CI 0.08, 0.64, p=0.005) and social support (OR 0.57, 95% CI 0.36, 0.91, p=0.018) were associated with fewer readmissions by 14 days, but not 30 days.Conclusion: The study indicates that health literacy and social support may be important predictors of A1c reduction post-discharge among hospitalized patients with T2D. Social support and diabetes self-management skills should be addressed and early follow-up may be critical for avoiding readmissions.Clinical Trial: NCT03455985.Keywords: type 2 diabetes, hospital discharge, basal insulin, A1c, readmission, social support, self-efficacy, literacy

Introduction A post hoc analysis of the PIONEER 1–5 and 8 trials assessed the clinically relevant composite endpoints of HbA1c (glycated haemoglobin) reduction ≥ 1% and body weight loss of ≥ 5% or ≥ 10% with orally administered semaglutide versus comparators. Methods In the PIONEER trials, people with type 2 diabetes were randomised to orally administered semaglutide versus placebo (PIONEER 1, 4, 5 and 8), empagliflozin (PIONEER 2), sitagliptin (PIONEER 3) and liraglutide (PIONEER 4) for 26–78 weeks. This analysis assessed the proportion of people achieving an HbA1c reduction of ≥ 1% and body weight loss of ≥ 5% at week 26 and at end of treatment, and the proportion of people achieving an HbA1c reduction of ≥ 1% and body weight loss of ≥ 10% at end of treatment. Results Overall, 3506 people in PIONEER 1–5 and 8 were included. At week 26 and at end of treatment, odds of achieving the composite endpoint of an HbA1c reduction of ≥ 1% and body weight loss of ≥ 5% were significantly greater with orally administered semaglutide 14 mg than with placebo (PIONEER 1, 4, 5 and 8; all p

OBJECTIVES: The metabolic abnormalities that lead to diabetes mellitus (DM) following an episode of acute pancreatitis (AP) have not been extensively studied. This manuscript describes the objectives, hypotheses, and methods of mechanistic studies of glucose metabolism that comprise secondary outcomes of the Diabetes RElated to Acute pancreatitis and its Mechanisms (DREAM) Study. METHODS: Three months after an index episode of AP, participants without pre-existing DM will undergo baseline testing with an oral glucose tolerance test. Participants will be followed longitudinally in three sub-cohorts with distinct metabolic tests. In the first and largest subcohort, oral glucose tolerance tests will be repeated 12 months after AP and annually to assess changes in β-cell function, insulin secretion, and insulin sensitivity. In the second, mixed meal tolerance tests will be performed at 3 and 12 months, then annually, and following incident DM to assess incretin and pancreatic polypeptide responses. In the third, frequently-sampled intravenous glucose tolerance tests will be performed at 3 months and 12 months to assess the first-phase insulin response and more precisely measure β-cell function and insulin sensitivity. CONCLUSIONS: The DREAM study will comprehensively assess the metabolic and endocrine changes that precede and lead to the development of DM after AP.

Acute pancreatitis (AP) is a disease characterized by an acute inflammatory phase followed by a convalescent phase. Diabetes mellitus (DM) was historically felt to be a transient phenomenon related to acute inflammation; however, it is increasingly recognized as an important late and chronic complication. There are several challenges that have prevented precisely determining the incidence rate of DM following AP and understanding the underlying mechanisms. The Diabetes RElated to Acute Pancreatitis and its Mechanisms (DREAM) Study is a prospective cohort study designed to address these and other knowledge gaps to provide the evidence needed to screen for, prevent, and treat DM following AP. In the following article, we summarize literature regarding the epidemiology of DM following AP, and provide the rationale and an overview of the DREAM study.

The coronavirus disease 2019 pandemic resulted in the need for remote glucose monitoring in intensive care unit (ICU) patients requiring insulin infusions. While rarely reported, perceptions from nursing staff are critical for successful implementation and expanded use. A hybrid continuous glucose monitoring (CGM) and intermittent point of care (POC) glucose monitoring was developed at the study institution. After 1.5 years of protocol use, an anonymous survey was distributed electronically to ICU nurses to obtain their perspectives on CGM utilization. The survey employed a 1-5 Likert scale regarding CGM sensor insertion, accuracy, acceptability, and usability as well as perceptions on reduced room entry and reduced workload. Of the 51 surveys completed, 50 (98%) nurses reported they cared for an ICU patient enrolled in the CGM protocol and 31 (62%) started or replaced a CGM sensor. A representative sample of questions and responses is shown in table 1. ICU nurses overwhelmingly reported CGM was accurate, reduced their workload, provided safer patient care, and was preferred over POC glucose testing alone. These results confirm nursing acceptance and preference for CGM use within a hybrid glucose monitoring protocol in the ICU setting. Disclosure J. Pattison: None. K. M. Dungan: Advisory Panel; Dexcom, Inc., Consultant; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Elsevier, Nova Biomedical, Novo Nordisk, Tolerion, Inc., Other Relationship; UpToDate, Research Support; Abbott, Dexcom, Inc., Novo Nordisk, Sanofi, ViaCyte, Inc., Speaker’s Bureau; Academy for Continued Healthcare Learning, Cardiometabolic Health Congress, Medscape. E. Buschur: Advisory Panel; Beta Bionics, Inc., Research Support; Dexcom, Inc. M. C. Exline: Other Relationship; Abbott. L. G. Jones: None. C. May: None. M. Mcnett: None. K. Smetana: None. E. R. Faulds: Advisory Panel; Dexcom, Inc., Research Support; Dexcom, Inc., Speaker’s Bureau; Medscape.

Background: Funded by the Ohio Department of Medicaid, the Ohio Cardiovascular and Diabetes Health Collaborative (Cardi-OH) unites the 7 medical schools in Ohio to improve outcomes and reduce disparities in hypertension and type 2 diabetes (T2D). The purpose of this needs assessment was to identify high priority educational topics for the dissemination of evidenced-based best practices. Methods: Questions surveyed collaborative members’ familiarity with guidelines, educational topics, and barriers to care. Results: A total of 111 out of 123 members completed the needs assessment (90.2% response rate). Half (49.5%, n=55) were familiar with the 2017 ACC/AHA guidelines for hypertension, whereas only 36.9% (n=41) were familiar with the 2020 ADA guidelines. The 3 topics of most interest were: 1) social determinants of health (SDOH, 55.9%, n=62), 2) lifestyle approaches to T2D (40.5%, n=45), and 3) new therapies for T2D (32.4%, n=36). Fifty-nine members (53.2%) provided direct clinical care; the others worked in research and public health. Providers estimated 25% of their T2D patients had A1C values >9.0%. Providers listed the 3 most common patient self-care barriers as: 1) lack of patient motivation (65.5%, n=38), 2) lack of patient education (60.3%, n=35), and 3) limited formularies and prior authorizations (41.4%, n=24). The 3 most common reasons for clinical inertia were: 1) lack of patient education (50.0%, n=29), 2) competing priorities (46.6%, n=27), and 3) lack of patient motivation (44.8%, n=26). Providers felt payers could help them with: 1) transportation (61.0%, n=68), 2) coverage of diabetes self-management education and support (DSMES, 59.4%, n=66), and 3) obtaining glucose monitors (45.9%, n=51). Conclusions: Top priorities for future evidence-based best practices are SDOH, patient engagement, and addressing complexity of care. Payers should prioritize increased coverage for DSMES and simplification of ordering monitoring supplies. Disclosure E. A. Beverly: None. G. D. Solomon: None. M. B. Holliday: None. J. Lewis: None. A. Nevar: None. S. M. Swiatkowski: None. M. W. Konstan: None. S. D. Bolen: None. K. M. Dungan: Advisory Panel; Self; Eli Lilly and Company, Tolerion, Inc., Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Elsevier, Novo Nordisk, Other Relationship; Self; UpToDate, Research Support; Self; Abbott, Sanofi-Aventis, Viacyte, Inc. J. T. Wright: None. A. Caron: None. M. Clemons: None. S. Aldrich renner: None. K. Gawlik: None. M. Sajatovic: Consultant; Self; Alkermes, Janssen Pharmaceuticals, Inc., Otsuka America Pharmaceutical, Inc., Other Relationship; Self; Springer Press, Johns Hopkins University Press, Oxford Press, UpToDate, Research Support; Self; Alkermes, Nuromate, Otsuka America Pharmaceutical, Inc. G. Rao: None. Funding Ohio Medicaid Technical Assistance and Policy Program (G-2021-05-0069)

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 PIONEER phase 3 program investigated glycemic response and other efficacy endpoints in patients (pts) with type 2 diabetes randomized to the glucagon-like peptide-1 analog oral semaglutide (sema; 3, 7 or 14 mg once daily), placebo (pbo) or an active comparator (empagliflozin [empa] 25 mg, sitagliptin [sita] 100 mg or liraglutide [lira] 1.8 mg once daily). This post-hoc analysis of the PIONEER 1-5 and 8 trials evaluated the response of any reduction in HbA1c (%) and/or body weight (%), and a clinically relevant composite endpoint of HbA1c reduction ≥1% and body weight loss ≥5%, with oral sema 14 mg vs. comparators at the end of treatment (26-78 weeks). Across trials, any reduction in HbA1c was observed in higher proportions of pts with oral sema (89-95%) than with pbo (51-64%) or active comparators (82-88%). A simultaneous reduction in both HbA1c and body weight was seen in 72-86% of pts treated with oral sema (Figure). The composite of HbA1c reduction ≥1% and body weight loss ≥5% was achieved by 27-41% of pts with oral sema, 1-8% of pts with pbo, 11% with sita 100 mg, 18% with lira 1.8 mg, and 20% with empa 25 mg. Within each trial, the odds of achieving HbA1c reduction of ≥1% and body weight loss of ≥5% with oral sema 14 mg were significantly greater vs. all comparators (p Disclosure K.M. Dungan: Advisory Panel; Self; Eli Lilly and Company, Elsevier. Research Support; Self; Novo Nordisk Inc., Sanofi-Aventis. Speaker’s Bureau; Self; Nova Biomedical. Other Relationship; Self; UpToDate, Vizient Inc. C.L. Hertz: Employee; Self; Novo Nordisk A/S. L. Mellbin: Advisory Panel; Self; Amgen, AstraZeneca, Novo Nordisk A/S, Sanofi-Aventis. Consultant; Self; AstraZeneca, Bayer AG, Novo Nordisk A/S. Research Support; Self; Bayer AG. Speaker’s Bureau; Self; Novo Nordisk A/S. Other Relationship; Self; Novo Nordisk Foundation. M. Abildlund Nielsen: Employee; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. Stock/Shareholder; Spouse/Partner; Novo Nordisk A/S. R. Sørrig: Employee; Self; Novo Nordisk A/S. V.C. Woo: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc. Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Inc., Sanofi-Aventis. T. Vilsbøll: Advisory Panel; Self; AstraZeneca, Mundipharma International, Novo Nordisk A/S, Sun Pharmaceutical Industries Ltd. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Medscape, Merck Sharp & Dohme Corp., Sanofi. Funding Novo Nordisk A/S

The study aims to determine whether the route of insulin administration influences glycaemic variability and inflammatory or neurohormonal markers in patients with type 2 diabetes and congestive heart failure (CHF) exacerbation.Patients (n = 65) were randomized to intravenous (IV) insulin (duration 48 h) or subcutaneous (SQ) insulin. Inflammatory cytokines and markers of lipid oxidation, high-frequency heart rate variability (n = 27) and cardiac impedance (pre-ejection period, n = 28) were used to estimate parasympathetic and sympathetic tone in patients with valid cardiac data. Glycaemic variability was measured using a continuous glucose monitor.Mean glucose was lower (7.7 ± 1.2 vs 9.4 ± 2.7 mmol/L, p = 0.004), coefficient of variation was higher (p = 0.03) and glycaemic lability index was similar on day 1 in the IV group compared with the SQ group, but groups were similar by day 2. The IV group had more confirmed hypoglycaemia (p = 0.005). There were no differences in hospital readmission or hospital length of stay between groups. There were no differences in CHF biomarkers, heart rate variability or pre-ejection period between groups. Increasing log glycaemic lability index was associated with lower on-treatment pre-ejection period (p = 0.03) while increasing coefficient of variation was associated with increasing brain natriuretic peptide (p = 0.004) and paroxonase-1 (p = 0.02). Other univariable analyses were not significant.There were modest, transient differences in glucose control between IV and SQ insulin in hospitalized CHF patients. However, the analyses do not support a link between insulin route and inflammatory markers or autonomic tone. Further study is needed to assess outcomes in hospitalized CHF patients.

Results of randomised controlled trials of tight glycaemic control in hospital inpatients might vary with population and disease state. Individualised therapy for different hospital inpatient populations and identification of patients at risk of hyperglycaemia might be needed. One risk factor that has received much attention is the presence of pre-existing diabetes. So-called stress hyperglycaemia is usually defined as hyperglycaemia resolving spontaneously after dissipation of acute illness. The term generally refers to patients without known diabetes, although patients with diabetes might also develop stress hyperglycaemia—a fact overlooked in many studies comparing hospital inpatients with or without diabetes. Investigators of several studies have suggested that patients with stress hyperglycaemia are at higher risk of adverse consequences than are those with pre-existing diabetes. We describe classification of stress hyperglycaemia, mechanisms of harm, and management strategies.