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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.

Traditionally, the care of critically ill patients with diabetes or stress hyperglycemia in the intensive care unit (ICU) demands the use of continuous intravenous insulin (CII) therapy to achieve narrow glycemic targets. To reduce the risk of iatrogenic hypoglycemia and to achieve glycemic targets during CII, healthcare providers (HCP) rely on hourly point-of-care (POC) arterial or capillary glucose tests obtained with glucose monitors. The burden of this approach, however, was evident during the beginning of the pandemic when the immediate reduction in close contact interactions between HCP and patients with COVID-19 was necessary to avoid potentially life-threatening exposures. Taking advantage of the advancements in current diabetes technologies, including continuous glucose monitoring (CGM) devices integrated with digital health tools for remote monitoring, HCP implemented novel protocols in the ICU to care for patients with COVID-19 and hyperglycemia. We provide an overview of research conducted in the ICU setting with the use of initial CGM technology to current devices and summarize our recent experience in the ICU.

OBJECTIVE The efficacy and safety of continuous glucose monitoring (CGM) in adjusting inpatient insulin therapy have not been evaluated. RESEARCH DESIGN AND METHODS This randomized trial included 185 general medicine and surgery patients with type 1 and type 2 diabetes treated with a basal-bolus insulin regimen. All subjects underwent point-of-care (POC) capillary glucose testing before meals and bedtime. Patients in the standard of care (POC group) wore a blinded Dexcom G6 CGM with insulin dose adjusted based on POC results, while in the CGM group, insulin adjustment was based on daily CGM profile. Primary end points were differences in time in range (TIR; 70–180 mg/dL) and hypoglycemia ( RESULTS There were no significant differences in TIR (54.51% ± 27.72 vs. 48.64% ± 24.25; P = 0.14), mean daily glucose (183.2 ± 40 vs. 186.8 ± 39 mg/dL; P = 0.36), or percent of patients with CGM values CONCLUSIONS The inpatient use of real-time Dexcom G6 CGM is safe and effective in guiding insulin therapy, resulting in a similar improvement in glycemic control and a significant reduction of recurrent hypoglycemic events compared with POC-guided insulin adjustment.

The annual Virtual Hospital Diabetes Meeting was hosted by Diabetes Technology Society on April 1 and April 2, 2022. This meeting brought together experts in diabetes technology to discuss various new developments in the field of managing diabetes in hospitalized patients. Meeting topics included (1) digital health and the hospital, (2) blood glucose targets, (3) software for inpatient diabetes, (4) surgery, (5) transitions, (6) coronavirus disease and diabetes in the hospital, (7) drugs for diabetes, (8) continuous glucose monitoring, (9) quality improvement, (10) diabetes care and educatinon, and (11) uniting people, process, and technology to achieve optimal glycemic management. This meeting covered new technology that will enable better care of people with diabetes if they are hospitalized.

Nonalcoholic steatohepatitis (NASH) is an increasingly common etiology for liver-related hospitalizations in the United States. The aim of this study was to examine the differences of disease characteristics and outcomes between hospitalized Black and White patients with NASH.We used the National Inpatient Sample (NIS) to identify all adult hospitalizations with NASH (ICD-10 code: K75.81) from 2016 to 2018. We compared demographic and clinical characteristics between Black and White patients. Multivariable models were computed to compare all-cause mortality, length of stay (LOS), and total hospital costs between the groups.There were 43,409 hospitalizations with NASH (41,143 White, 2266 Black). Black patients were less likely to have cirrhosis (33.6%) compared with Whites (56.4%), P0.0001. Black patients were less likely to have esophageal variceal bleeding (1.2% vs. 3.5%), ascites (17.1% vs. 28.8%), and acute liver failure (16.2% vs. 28.9%) compared with Whites (all P0.0001). These findings were consistent among patients with cirrhosis. Mortality was higher among Blacks compared with Whites (3.9% vs. 3.7%, adjusted odds ratio=1.34; 95% confidence interval: 1.05-1.71, P=0.018). Compared with Whites, Blacks had a longer LOS (6.3 vs. 5.6, P0.001), and higher hospital costs ($18,602 vs. $17,467; P=0.03).In this large population of inpatients with NASH, Black patients were less likely to have cirrhosis and liver disease-related complications, but had overall worse hospital mortality, longer LOS, and higher hospital costs. Further research is warranted to elaborate on factors that generate the health inequities in NASH outcomes between Black and White patients.

This article is the work product of the Continuous Ketone Monitoring Consensus Panel, which was organized by Diabetes Technology Society and met virtually on April 20, 2021. The panel consisted of 20 US-based experts in the use of diabetes technology, representing adult endocrinology, pediatric endocrinology, advanced practice nursing, diabetes care and education, clinical chemistry, and bioengineering. The panelists were from universities, hospitals, freestanding research institutes, government, and private practice. Panelists reviewed the medical literature pertaining to ten topics: (1) physiology of ketone production, (2) measurement of ketones, (3) performance of the first continuous ketone monitor (CKM) reported to be used in human trials, (4) demographics and epidemiology of diabetic ketoacidosis (DKA), (5) atypical hyperketonemia, (6) prevention of DKA, (7) non-DKA states of fasting ketonemia and ketonuria, (8) potential integration of CKMs with pumps and automated insulin delivery systems to prevent DKA, (9) clinical trials of CKMs, and (10) the future of CKMs. The panelists summarized the medical literature for each of the ten topics in this report. They also developed 30 conclusions (amounting to three conclusions for each topic) about CKMs and voted unanimously to adopt the 30 conclusions. This report is intended to support the development of safe and effective continuous ketone monitoring and to apply this technology in ways that will benefit people with diabetes.

Background: The efficacy and safety of continuous glucose monitoring (CGM) in adjusting inpatient insulin therapy has not been evaluated. Methods: This randomized trial included 185 general medicine and surgery patients with type 1 and type 2 diabetes treated with a basal bolus insulin regimen. All subjects underwent point-of-care (POC) capillary glucose testing before meals and bedtime. Patients in the standard of care (POC group) wore a blinded Dexcom G6 CGM with insulin dose adjusted based on POC results; while in the CGM group, insulin adjustment was based on daily CGM profile. Primary endpoints were differences in time in range (TIR, 70-180 mg/dL) and hypoglycemia ( Results: There were no significant differences in TIR (54.51%±27.72 vs 48.64%±24.25, p=0.14), mean daily glucose (183.2±40 mg/dL vs 186.8±39 mg/dL, p=0.36), percent of patients with CGM values Conclusion: The inpatient use of real-time Dexcom G6 CGM is safe and effective in guiding insulin therapy resulting in a similar improvement in glycemic control and a significant reduction of recurrent hypoglycemic events compared to POC-guided insulin adjustment.

Objective: Administration of supplemental sliding scale insulin (SSI) for correction of hyperglycemia in non-ICU patients with type 2 diabetes is frequently used with basal-bolus insulin regimens. This non-inferiority randomized controlled trial tested whether glycemic control is similar with and without aggressive SSI before meals and bedtime in patients treated with basal-bolus insulin regimens. Research Design and Methods: Patients with type 2 diabetes, with admission blood glucose (BG)140-400 mg/dl treated with basal-bolus insulin were randomized to Intensive (correction for BG>140 mg/dl, n=108) or to Non-Intensive (correction for BG>260 mg/dl, n=107) administration of rapid-acting SSI before meals and bedtime. Both groups received the same amount of SSI for BG>260 mg/dl. Primary outcome was difference in mean daily BG levels between the groups during hospitalization. Results: Mean daily BG in the Non-Intensive group was non-inferior to BG in the Intensive group with equivalence margin of 18 mg/dl (Intensive: 172±38 mg/dl vs Non-Intensive: 173±43 mg/dl, p=0.001 for non-inferiority). There were no differences in the proportion of target BG readings of 70-180 mg/dl, hypoglycemia (350 mg/dl), or total, basal or prandial insulin doses. Significantly fewer subjects received SSI in the Non-Intensive (n=36,34%) compared to the Intensive group (n=98,91% [p Conclusions: Among non-ICU patients with type 2 diabetes on optimal basal-bolus insulin regimen with moderate hyperglycemia (BG

IDegLira-HIGH trial was a non-inferiority, prospective, randomized clinical trial, comparing the efficacy and safety of insulin degludec/liraglutide combination (IDegLira) and a regimen of multiple daily injections (MDI) with basal-bolus insulin (BB) in adults (>18-80 years of age) , with type 2 diabetes (T2D) , and very high HbA1c (>9%-15%) , previously treated with basal insulin or multiple oral agents. Primary endpoint was change in HbA1c from baseline to week 26. Secondary endpoints included incidence of hypoglycemia, changes on weight and glycemic variability (% coefficient of variation [%CV]) . Treatment with IDegLira and BB insulin resulted in similar improvement in glycemic control: 7.7±1.7% and 7.7±1.5%, (p=0.76) , with a reduction from baseline of -3.2±2.3% and -3.0±1.8%, (p=0.39) at 26-weeks. IDegLira resulted in lower rates of hypoglycemia Disclosure R.J.Galindo: Advisory Panel; Sanofi, WW International, Inc., Research Support; Dexcom, Inc., Eli Lilly and Company, Novo Nordisk. G.E.Umpierrez: Research Support; AstraZeneca, Dexcom, Inc., Novo Nordisk. B.Moazzami: None. M.F.Scioscia: None. P.Vellanki: n/a. G.M.Davis: Consultant; Medscape, Research Support; Insulet Corporation. F.J.Pasquel: Consultant; AI Health LLC, Boehringer Ingelheim International GmbH, Dexcom, Inc., Research Support; Dexcom, Inc., Insulet Corporation, Merck & Co., Inc. M.Fayfman: None. A.L.Migdal: None. J.Saling: None. Funding Investigator-initiated study to Emory University (PI. Dr. Rodolfo J. Galindo) from Novo Nordisk.

Few studies have reported on differences in glycemic control and glucose variability by continuous glucose monitoring (CGM) in hospitalized insulin-treated older adults with type 2 diabetes (T2D) . Accordingly, we combined data from 3 inpatient randomized clinical trials using CGM in insulin-treated patients with T2D. Glycemic parameters were compared in 103 older adults (≥60 years) and 160 younger adults ( Disclosure T.Idrees: None. F.J.Pasquel: Consultant; AI Health LLC, Boehringer Ingelheim International GmbH, Dexcom, Inc., Research Support; Dexcom, Inc., Insulet Corporation, Merck & Co., Inc. L.Peng: None. G.E.Umpierrez: Research Support; AstraZeneca, Dexcom, Inc., Novo Nordisk. R.J.Galindo: Advisory Panel; Sanofi, WW International, Inc., Research Support; Dexcom, Inc., Eli Lilly and Company, Novo Nordisk. M.A.Urrutia: None. I.A.Castro-revoredo: None. E.Moreno: None. A.L.Migdal: None. G.Davis: Consultant; Medscape, Research Support; Insulet Corporation. P.Vellanki: n/a. M.Fayfman: None.

Inpatient use of CGM results in higher detection of hypoglycemic and hyperglycemic events compared to point of care testing (POC) but its efficacy and safety in adjusting insulin therapy has not been evaluated. This randomized controlled trial included 181 general medicine and surgery patients with type 1 (n= 18) and type 2 (n= 155) diabetes treated with a basal bolus insulin regimen. All patients underwent POC testing AC & HS. Patients in the POC group wore a blinded Dexcom G6 CGM with insulin dose adjusted based on POC results; while in the CGM group, insulin adjustment was based on daily Dexcom G6 CGM profile review. Hypoglycemia alarms were set at 80 mg/dl in the CGM group. Primary endpoints were differences in time in range (70-180 mg/dl) and hypoglycemia ( Results: There were no differences on admission clinical characteristics, HbA1c or diabetes type between POC and CGM groups. There were no differences in mean daily glucose (186.8±39 mg/dl vs. 183.2±40 mg/dl, p=0.36) , total daily insulin dose (36.1±28 U/day vs. 40.7±29 U/day, p=0.33) , % patients with CGM values Conclusion: Our results indicates that the inpatient use of Dexcom G6 CGM is safe and effective in guiding insulin adjustment resulting in similar improvement in glucose control and in significant reduction of recurrent hypoglycemic events compared to POC testing. Disclosure I. Spanakis: Other Relationship; Dexcom, Inc. L. G. Singh: None. C. Gothong: None. I. Marcano: None. S. Lizama: None. K. M. Munir: None. C. Chesney: None. R. D. Maguire: None. W. H. Scott: None. L. Peng: None. G. E. Umpierrez: Research Support; AstraZeneca, Dexcom, Inc., Novo Nordisk. M. A. Urrutia: None. M. F. Scioscia: None. R. J. Galindo: Advisory Panel; Sanofi, WW International, Inc., Research Support; Dexcom, Inc., Eli Lilly and Company, Novo Nordisk. P. Vellanki: n/a. A. L. Migdal: None. G. Davis: Consultant; Medscape, Research Support; Insulet Corporation. T. Idrees: None. F. J. Pasquel: Consultant; AI Health LLC, Boehringer Ingelheim International GmbH, Dexcom, Inc., Research Support; Dexcom, Inc., Insulet Corporation, Merck & Co., Inc.

Background Risk factors (RFs) for pneumococcal disease (PD) in immunocompetent individuals include comorbidities, behavioral habits, or living in a community with increased risk of PD transmission. RF stacking of comorbidities is associated with a higher incidence of PD, approaching that of immunocompromised individuals. Pneumococcal vaccination of certain adults is recommended with the 23-valent pneumococcal polysaccharide vaccine (PPSV23) alone/sequentially with pneumococcal conjugate vaccine (PCV). V114, an investigational 15-valent PCV, contains 2 epidemiologically important serotypes (STs), 22F and 33F, in addition to the 13 STs in 13-valent PCV (PCV13). Methods PNEU-DAY was a Phase 3 study evaluating V114 or PCV13 administered on Day 1, and PPSV23 given 6 months later, in adults aged 18–49 years with or without RFs. This subgroup analysis assessed safety, tolerability, and immunogenicity of V114 and PCV13 based on the number of baseline PD RFs, which included chronic liver, lung, and heart disease, diabetes mellitus, tobacco use, and alcohol consumption. Adverse events (AEs; overall and solicited) were collected after each vaccination. Immunogenicity assessment was based on ST-specific opsonophagocytic activity (OPA) at 30 days after each vaccination. Subgroup analyses were conducted by RF group (0, 1, or ≥2 RFs for PD). Results Among the 1515 participants randomized to V114 (n=1135) or PCV13 (n=380), 25.2% had no RFs, 54.7% had 1 RF and 20.1% had ≥2 RFs for PD at baseline. The proportions of participants with solicited AEs following V114/PCV13 and PPSV23 were comparable across the 3 subgroups, with injection-site pain, myalgia, and fatigue being the most common. V114 and PCV13 were immunogenic in all subgroups based on OPA geometric mean titers (GMTs) at 30 days post-vaccination for the 13 shared STs (Figure); in addition, V114 induced a robust immune response to the 2 unique STs (22F, 33F) in all subgroups. PPSV23 following PCV was immunogenic for all 15 STs contained in V114 across all subgroups. Figure. Serotype-specific OPA GMTs at baseline and 30 days post-vaccination with V114 and PCV13 by number of baseline risk factors (per-protocol population) Conclusion V114 administered alone/sequentially with PPSV23 is well tolerated and immunogenic for all 15 vaccine STs, including those not contained in PCV13, in immunocompetent adults aged 18–49 years, regardless of the number of baseline RFs. Disclosures Laura Hammitt, MD, MedImmune (Grant/Research Support, Scientific Research Study Investigator, Research Grant or Support)Merck & Co., Inc. (Grant/Research Support, Scientific Research Study Investigator, Research Grant or Support)Novavax (Grant/Research Support, Scientific Research Study Investigator, Research Grant or Support)Pfizer (Grant/Research Support, Scientific Research Study Investigator, Research Grant or Support) Francisco J. Pasquel, MD, MPH, Boehringer Ingelheim (Consultant)Dexcom (Grant/Research Support)Eli Lilly & Company (Consultant)Insulet (Grant/Research Support)Merck & Co., Inc. (Consultant, Grant/Research Support) K. Rajender Reddy, MD, BMS (Grant/Research Support)Deciphera (Advisor or Review Panel member)Gilead (Grant/Research Support)Grifols (Grant/Research Support)HCC-TARGET (Grant/Research Support)Intercept (Grant/Research Support)Mallinckrodt (Grant/Research Support, Advisor or Review Panel member)NASH-TARGET (Grant/Research Support)Pfizer (Advisor or Review Panel member)Sequana (Grant/Research Support) Ron Dagan, MD, Medimmune/AstraZeneca (Grant/Research Support, Scientific Research Study Investigator, Research Grant or Support)MSD (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker’s Bureau)Pfizer (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker’s Bureau) Rachel Dawson, D.O. MPH, Merck & Co., Inc. (Employee, Shareholder) Jennifer McCauley, BSc, Merck & Co., Inc. (Employee) Kyeongmi Cheon, Ph.D., Merck & Co., Inc. (Employee, Shareholder) Alison Pedley, PhD, Merck & Co., Inc. (Employee) Tina Sterling, BS, Merck & Co., Inc. (Employee, Shareholder) Gretchen Tamms, B.S., Merck Sharp and Dohme (Employee, Shareholder) Luwy Musey, MD, Merck & Co., Inc. (Employee) Ulrike K. Buchwald, MD, MS, Merck & Co., Inc. (Employee)TB Alliance (Employee)

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.

AIMS: Differences in risk profiles for individuals with early- (< 40 years old) vs. later-onset (≥ 40 years old) diabetes were examined. METHODS: A nested case-control study design using 30-year longitudinal data from the Coronary Artery Risk Development in Young Adults (CARDIA) study was used. Survey data (socio-demographics, family history, medical records, and lifestyle behaviors), obesity-related measures (body mass index, weight), blood pressure, and laboratory data (insulin, fasting glucose, 2-h glucose, and lipids) were used to examine progression patterns of diabetes development in those with early-onset vs. later-onset diabetes. RESULTS: Of 605 participants, 120 were in early-onset group while 485 were in later-onset group. Early-onset group had a lower A Priori Diet Quality Score, but not statistically significant at baseline; however, the between-group difference became significant at the time that diabetes was first detected (p=.026). The physical activity intensity score consistently decreased from baseline to the development of diabetes in both the early- and later-onset groups. Early-onset group showed more dyslipidemia at baseline and at the time that diabetes was first detected, and rapid weight gain from baseline to the development of diabetes. CONCLUSIONS: Emphases on lifestyle modification and risk-based diabetes screening in asymptomatic young adults are necessary for early detection and prevention.