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The American Diabetes Association (ADA), European Association for the Study of Diabetes (EASD), Joint British Diabetes Societies for Inpatient Care (JBDS), American Association of Clinical Endocrinology (AACE) and Diabetes Technology Society (DTS) convened a panel of internists and diabetologists to update the ADA consensus statement on hyperglycaemic crises in adults with diabetes, published in 2001 and last updated in 2009. The objective of this consensus report is to provide up-to-date knowledge about the epidemiology, pathophysiology, clinical presentation, and recommendations for the diagnosis, treatment and prevention of diabetic ketoacidosis (DKA) and hyperglycaemic hyperosmolar state (HHS) in adults. A systematic examination of publications since 2009 informed new recommendations. The target audience is the full spectrum of diabetes healthcare professionals and individuals with diabetes., (© 2024. American Diabetes Association and European Association for the Study of Diabetes.)

Introduction: The relationship between critical care mortality and combined impact of malglycemia remains undefined., Methods: We assessed the risk-adjusted relationship (n = 4790) between hospital mortality with malglycemia, defined as hypergycemia (hours Glycemic Ratio ≥ 1.1, where GR is quotient of mean ICU blood glucose (BG) and estimated average BG), absolute hypoglycemia (hours BG < 70 mg/dL) and relative hypoglycemia (excursions GR < 0.7 in those with HbA1c ≥ 8%)., Results: Each malglycemia was independently associated with mortality - hyperglycemia (OR 1.0020/h, 95%CI 1.0009-1.0031, p = 0.0004), absolute hypoglycemia (OR 1.0616/h, 95%CI 1.0190-1.1061, p = 0.0043), and relative hypoglycemia (OR 1.2813/excursion, 95%CI 1.0704-1.5338, p = 0.0069). Absolute (7.4%) and relative hypoglycemia (6.7%) exposure dominated the first 24 h, decreasing thereafter. While hyperglycemia had lower risk association with mortality, it was persistently present across the length-of-stay (68-76% incidence daily), making it the dominant form of malglycemia. Relative contributions in the first five days from hyperglycemia, absolute hypoglycemia and relative hypoglycemia were 60%, 21% and 19% respectively., Conclusions: Absolute and relative hypoglycemia occurred largely in the first 24 h. Relative to all hypoglycemia, the associated mortality from the seemingly less potent but consistently more prevalent hyperglycemia steadily accumulated with increasing length-of-stay. This has important implications for interpretation of study results., (Copyright © 2023. Published by Elsevier Inc.)

Rationale: Maintaining glycemic control of critically ill patients may impact outcomes such as survival, infection, and neuromuscular recovery, but there is equipoise on the target blood levels, monitoring frequency, and methods., Objectives: The purpose was to update the 2012 Society of Critical Care Medicine and American College of Critical Care Medicine (ACCM) guidelines with a new systematic review of the literature and provide actionable guidance for clinicians., Panel Design: The total multiprofessional task force of 22, consisting of clinicians and patient/family advocates, and a methodologist applied the processes described in the ACCM guidelines standard operating procedure manual to develop evidence-based recommendations in alignment with the Grading of Recommendations Assessment, Development, and Evaluation Approach (GRADE) methodology. Conflict of interest policies were strictly followed in all phases of the guidelines, including panel selection and voting., Methods: We conducted a systematic review for each Population, Intervention, Comparator, and Outcomes question related to glycemic management in critically ill children (≥ 42 wk old adjusted gestational age to 18 yr old) and adults, including triggers for initiation of insulin therapy, route of administration, monitoring frequency, role of an explicit decision support tool for protocol maintenance, and methodology for glucose testing. We identified the best available evidence, statistically summarized the evidence, and then assessed the quality of evidence using the GRADE approach. We used the evidence-to-decision framework to formulate recommendations as strong or weak or as a good practice statement. In addition, "In our practice" statements were included when the available evidence was insufficient to support a recommendation, but the panel felt that describing their practice patterns may be appropriate. Additional topics were identified for future research., Results: This guideline is an update of the guidelines for the use of an insulin infusion for the management of hyperglycemia in critically ill patients. It is intended for adult and pediatric practitioners to reassess current practices and direct research into areas with inadequate literature. The panel issued seven statements related to glycemic control in unselected adults (two good practice statements, four conditional recommendations, one research statement) and seven statements for pediatric patients (two good practice statements, one strong recommendation, one conditional recommendation, two "In our practice" statements, and one research statement), with additional detail on specific subset populations where available., Conclusions: The guidelines panel achieved consensus for adults and children regarding a preference for an insulin infusion for the acute management of hyperglycemia with titration guided by an explicit clinical decision support tool and frequent (≤ 1 hr) monitoring intervals during glycemic instability to minimize hypoglycemia and against targeting intensive glucose levels. These recommendations are intended for consideration within the framework of the patient's existing clinical status. Further research is required to evaluate the role of individualized glycemic targets, continuous glucose monitoring systems, explicit decision support tools, and standardized glycemic control metrics., Competing Interests: Dr. Umpierrez’s institution received funding from Dexcom, Abbott, Bayer, and Astra Zeneca. Dr. Sirimaturos’ institution received funding from Grifols; he received funding from Astra Zeneca. Dr. Mechanick received funding from Abbott Nutrition, Aveta.Life, and Twin Health. Dr. Irving disclosed that she is a board member of the American Society for Parenteral and Enteral Nutrition and a member of American Society for Parenteral and Enteral Nutrition/Society of Critical Care Medicine Pediatric Critical Care Nutrition Guideline. Dr. Preiser received funding from Edwards, Glysure, Medtronic, and Optiscan. Dr. Krinsley received funding from Dexcom. Dr. Sands received funding from BioXcel Therapeutics. Dr. Jacobi disclosed that she was an Advisory Board Member of the Pfizer Hospital Business Unit. Dr. Agus’ institution received funding from the National Institutes of Health; he disclosed that Dexcom is providing in kind support with continuous glucose monitors for a clinical trial in children. The remaining authors have disclosed that they do not have any potential conflicts of interest. The total multiprofessional task force of 22, consisting of clinicians and patient/family advocates, and a methodologist applied the processes described in the ACCM guidelines standard operating procedure manual to develop evidence-based recommendations in alignment with the Grading of Recommendations Assessment, Development, and Evaluation Approach (GRADE) methodology. Conflict of interest policies were strictly followed in all phases of the guidelines, including panel selection and voting., (Copyright © 2024 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Background: Interventional studies investigating blood glucose (BG) management in intensive care units (ICU) have been inconclusive. New insights are needed. We assessed the ability of a new metric, the Glycemic Ratio (GR), to determine the relationship of ICU glucose control relative to preadmission glycemia and mortality., Methods: Retrospective cohort investigation (n = 4790) in an adult medical-surgical ICU included patients with minimum four BGs, hemoglobin (Hgb), and hemoglobin A1c (HbA1c). The GR is the quotient of mean ICU BGs (mBG) and estimated preadmission BG, derived from HbA1c., Results: Mortality displayed a J-shaped curve with GR (nadir GR 0.9), independent of background glycemia, consistent for HbA1c <6.5% vs >6.5%, and Hgb >10 g/dL vs <10 g/dL and medical versus surgical. An optimal range of GR 0.80 to 0.99 was associated with decreased mortality compared with GR above and below this range. The mBG displayed a linear relationship with mortality at lower HbA1c but diminished for HbA1c >6.5%, and dependent on preadmission glycemia. In adjusted analysis, GR remained associated with mortality (odds ratio = 2.61, 95% confidence interval = 1.48-4.62, P = .0012), but mBG did not (1.004, 1.000-1.009, .059). A single value on admission was not independently associated with mortality., Conclusions: The GR provided new insight into malglycemia that was not apparent using mBG, or an admission value. Mortality was associated with acute change from preadmission glycemia (GR). Further assessment of the impact of GR deviations from the nadir in mortality at GR 0.80 to 0.99, as both relative hypo- and hyperglycemia, and as duration of exposure and intensity, may further define the multifaceted nature of malglycemia., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: JSK and J-CP disclosed that they are advisory board members and consultants for Edwards Life Sciences, OptiScan Biomedical, and Roche Diagnostics. IBH’s institution received funding from Insulet and Beta Bionics; he disclosed that he is a consultant for Abbott, Roche, LifeScan, and GWave. GEU’s institution received funding from Dexcom, Abbott, and Baxter. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Objective: Patients with diabetes and end-stage kidney disease (ESKD) may experience "burnt-out diabetes," defined as having an HbA1c value <6.5% without antidiabetic therapy for >6 months. We aim to assess glycemic control by continuous glucose monitoring (Dexcom G6 CGM) metrics and glycemic markers in ESKD patients on hemodialysis with burnt-out diabetes., Research Design and Methods: In this pilot prospective study, glycemic control was assessed by continuous glucose monitoring (CGM), HbA1c measures, and glycated albumin and fructosamine measurements in patients with burnt-out diabetes (n = 20) and without a history of diabetes (n = 20)., Results: Patients with burnt-out diabetes had higher CGM-measured daily glucose levels, lower percent time in the range 70-180 mg/dL, higher percent time above range (>250 mg/dL), and longer duration of hyperglycemia >180 mg/dL (hours/day) compared with patients without diabetes (all P < 0.01). HbA1c and fructosamine levels were similar; however, patients with burnt-out diabetes had higher levels of glycated albumin than did patients without diabetes., Conclusions: The use of CGM demonstrated that patients with burnt-out diabetes have significant undiagnosed hyperglycemia. CGM and glycated albumin provide better assessment of glycemic control than do values of HbA1c and fructosamine in patients with ESKD., (© 2024 by the American Diabetes Association.)

Continuous glucose monitors (CGMs) have increasingly been used in ambulatory and inpatient or hospital settings to improve glycemic outcomes for people with diabetes. Given their capacity to aid individuals in avoiding hypo- and hyperglycemia, they may also be useful when transitioning from hospital to home by reducing rates of hospital readmissions and emergency department visits. Several types of barriers presently exist that make the deployment of CGMs at the time of hospital discharge problematic, including (1) regulatory, (2) behavioral, (3) logistical, (4) technical, (5) staffing, and (6) systemic issues. In this commentary, we review the literature, discuss these barriers, and propose possible solutions to facilitate the use of CGMs in people with diabetes at the time of hospital discharge., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: JJS is a consultant for Lifescan Diabetes Institute. CJL has received research support by the NIDDK and Helmsley Foundation and industry support paid to the Icahn School of Medicine at Mount Sinai from Abbott Diabetes, Dexcom, Insulet, Novo Nordisk, Senseonics, and Tandem. CJL has received consulting fees from Eli-Lilly, and Dexcom outside of this work. GEU is partly supported by research grants from National Institutes of Health (NIH/NATS UL 3UL1TR002378-05S2) from the Clinical and Translational Science Award program, and from National Institutes of Health and National Center for Research Resources (NIH/NIDDK 2P30DK111024-06). GEU has received research support (to Emory University) from Abbott, Bayer, Dexcom and Sanofi. DCK is a consultant for Better Therapeutics, EOFlow, Integrity, Lifecare, Nevro, Novo, Rockley Photonics, and Thirdwayv. TT, REA, RL, and IN have nothing relevant to disclose.

Introduction: Stress-induced hyperglycemia (SIH) is conventionally represented by Blood Glucose (BG) although recent evidence indicates the Glycemic Ratio (GR, quotient of mean BG and estimated preadmission BG) is a superior prognostic marker. We assessed the association between in-hospital mortality and SIH, using BG and GR in an adult medical-surgical ICU., Methods: We included patients with hemoglobin A1c (HbA1c) and minimum four BGs in a retrospective cohort investigation (n = 4790)., Results: A critical SIH threshold of GR 1.1 was identified. Mortality increased with increasing exposure to GR ≥ 1.1 (r 2  = 0.94, p = 0.0007). Duration of exposure to BG ≥ 180 mg/dL demonstrated a less robust association with mortality (r 2  = 0.75, p = 0.059). In risk-adjusted analyses, hours GR ≥ 1.1 (OR 1.0014, 95%CI (1.0003-1.0026), p = 0.0161) and hours BG ≥ 180 mg/dL (OR 1.0080, 95%CI (1.0034-1.0126), p = 0.0006) were associated with mortality. In the cohort with no exposure to hypoglycemia however, only hours GR ≥ 1.1 was associated with mortality (OR 1.0027, 95%CI (1.0012-1.0043), p = 0.0007), not BG ≥ 180 mg/dL (OR 1.0031, 95%CI (0.9949-1.0114), p = 0.50) and this relationship remained intact for those who never experienced BG outside the 70-180 mg/dL range (n = 2494)., Conclusions: Clinically significant SIH commenced above GR 1.1. Mortality was associated with hours of exposure to GR ≥ 1.1 which was a superior marker of SIH compared to BG., (Copyright © 2023 Elsevier Inc. All rights reserved.)

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: To describe the gaps in knowledge for the care of people in the hospital who have dysglycaemia or diabetes., Methods: A review of the current literature and the authors' knowledge of the subject., Results: Recent data has suggested that the prevalence of hospitalised people with diabetes is approximately three times the prevalence in the general population and is growing annually. A wealth of observational data over the last 4 decades has shown that people with hyperglycaemia, severe hypoglycaemia or diabetes, all experience more harm whilst in the hospital than those who do not have the condition. This often equates to a longer length of stay and thus higher costs. To date, the proportion of federal funding aimed at addressing the harms that people with dysglycaemia experience in hospitals has been very small compared to outpatient studies. National organisations, such as the Joint British Diabetes Societies for Inpatient Care, the American Diabetes Association and the Endocrine Society have produced guidelines or consensus statements on the management of various aspects of inpatient care. However, whilst a lot of these have been based on evidence, much remains based on expert opinion and thus low-quality evidence., Conclusions: This review highlights that inpatient diabetes is an underfunded and under-researched area., (© 2022 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.)

Randomised controlled trials and other prospective clinical studies for novel medical interventions in people with diabetes have traditionally reported HbA 1c as the measure of average blood glucose levels for the 3 months preceding the HbA 1c test date. The use of this measure highlights the long-established correlation between HbA 1c and relative risk of diabetes complications; the change in the measure, before and after the therapeutic intervention, is used by regulators for the approval of medications for diabetes. However, with the increasing use of continuous glucose monitoring (CGM) in clinical practice, prospective clinical studies are also increasingly using CGM devices to collect data and evaluate glucose profiles among study participants, complementing HbA 1c findings, and further assess the effects of therapeutic interventions on HbA 1c . Data is collected by CGM devices at 1-5 min intervals, which obtains data on glycaemic excursions and periods of asymptomatic hypoglycaemia or hyperglycaemia (ie, details of glycaemic control that are not provided by HbA 1c concentrations alone that are measured continuously and can be analysed in daily, weekly, or monthly timeframes). These CGM-derived metrics are the subject of standardised, internationally agreed reporting formats and should, therefore, be considered for use in all clinical studies in diabetes. The purpose of this consensus statement is to recommend the ways CGM data might be used in prospective clinical studies, either as a specified study endpoint or as supportive complementary glucose metrics, to provide clinical information that can be considered by investigators, regulators, companies, clinicians, and individuals with diabetes who are stakeholders in trial outcomes. In this consensus statement, we provide recommendations on how to optimise CGM-derived glucose data collection in clinical studies, including the specific glucose metrics and specific glucose metrics that should be evaluated. These recommendations have been endorsed by the American Association of Clinical Endocrinologists, the American Diabetes Association, the Association of Diabetes Care and Education Specialists, DiabetesIndia, the European Association for the Study of Diabetes, the International Society for Pediatric and Adolescent Diabetes, the Japanese Diabetes Society, and the Juvenile Diabetes Research Foundation. A standardised approach to CGM data collection and reporting in clinical trials will encourage the use of these metrics and enhance the interpretability of CGM data, which could provide useful information other than HbA 1c for informing therapeutic and treatment decisions, particularly related to hypoglycaemia, postprandial hyperglycaemia, and glucose variability., Competing Interests: Declaration of interests TB has received honoraria for participation on advisory boards for Novo Nordisk, Sanofi, Eli Lilly, Boehringer-Ingelheim, Medtronic, and Indigo Diabetes, and as a speaker for AstraZeneca, Eli Lilly, Novo Nordisk, Medtronic, Sanofi, and Roche. TB owns stocks of DreaMed Diabetes and his institution (University Medical Centre Ljubljana, University Children's Hospital) has received research grant support and travel expenses from Abbott Diabetes Care, Medtronic, Novo Nordisk, Sanofi, Sandoz, Novartis. SAA has served on advisory boards for Novo Nordisk and Medtronic and has spoken at educational events sponsored by Novo Nordisk and Sanofi in the last 12 months. She is a co-investigator on the EU IMI Hypoglycaemia Redefining Solutions for Better Lives programme, which has the industry partners Abbott Diabetes Care, Eli Lilly, Medtronic, Novo Nordisk, and Sanofi-Aventis. RWB reports that his institution (Jaeb Center for Health Research) has received funding on his behalf as follows: grant funding and study supplies from Tandem Diabetes Care, Beta Bionics, and Dexcom; study supplies from Medtronic, Ascencia, and Roche; consulting fees and study supplies from Eli Lilly and Novo Nordisk; and consulting fees from Insulet, Bigfoot Biomedical, vTv Therapeutics, and Diasome. RMB has received research support from Abbott Diabetes Care, Ascensia, Bigfoot Biomedical, CeQur, Dexcom, Eli Lilly, Hygieia, Insulet, and Medtronic. RMB has received consulting fees from Abbott Diabetes Care, Ascensia, Bigfoot Biomedical, CeQur, Dexcom, Eli Lilly, Hygieia, Insulet, Medtronic, Novo Nordisk, Onduo, Roche Diabetes Care, Sanofi, United Healthcare, Vertex Pharmaceuticals, and Zealand Pharma. RMB has participated on advisory boards for Abbott Diabetes Care, Ascensia, Bigfoot Biomedical, CeQur, Dexcom, Eli Lilly, Hygieia, Insulet, Medtronic, Novo Nordisk, Onduo, Roche Diabetes Care, Sanofi, United Healthcare, Vertex Pharmaceuticals, and Zealand Pharma. BAB has received honoraria for participating in advisory boards for Novo Nordisk, Lilly, and Arecor. TD has received honoraria from Abbott, AstraZeneca, Boehringer, Dexcom, Lilly, Medtronic, Novo Nordisk, Roche, Sanofi, and Ypsomed. TD has received research support from Abbott, AstraZeneca, Boehringer, Dexcom, Lilly, Medtronic, Novo Nordisk, Roche, Sanofi, and Ypsomed. TD has received consulting fees from Abbott, AstraZeneca, Boehringer, Dexcom, Lilly, Medtronic, Novo Nordisk, Roche, Sanofi, and Ypsomed. TD is a shareholder of DreaMed Diabetes. RG has received honoraria as an advisor to Lark, Sweetch, and Vida. AC has received honoraria from AstraZeneca, Berlin Chemie, Merck, Novo Nordisk, and Roche Diagnostics. AC has received consulting fees from AstraZeneca, Bayer, Elsevier, Roche Diagnostics, Sevier, and Theras. AC has participated on advisory boards for Eli Lilly and Roche Diagnostics. EC has received speaker fees from Sanofi and Novartis, and institutional research support from Medtronic, Sanofi, and Powder Pharmaceuticals. PC has received honoraria for speaking from Medtronic, Dexcom, Abbott, Glooko, DreaMed, Novo Nordisk, Lilly, and Sanofi, and has received research support from Medtronic, Dexcom, Abbott, and Novo Nordisk. KLC receives subscription revenue for the web blog Close Concerns from Abbott, Biolinq, Dexcom, GlySens, Medtronic, Percusense, and Senseonics. SG has received advisory board honoraria and consulting fees from Bayer, Eli Lilly, Lifescan, Medtronic, Novo Nordisk, and Zealand. He has received research grants from Dario, Dexcom, Eli Lilly, JDFR, Lexicon, Medtronic, Merck, Novo Nordisk, Sanofi, and T1D Exchange. JC, JH, and JK are employees of the diaTribe Foundation, which receives funding support from continuous glucose monitor manufacturers Abbott Diabetes Care, Dexcom, and Medtronic. IBH receives consulting fees from Abbott Diabetes Care, Roche, Lifescan, and GWave, and receives research support from Insulet and Dexcom. LL has received consulting fees from Janssen, Insulet, Boehringer Ingelheim, Medtronic, Dompe, Provention, Eli Lilly, Roche, and Dexcom. LL has participated on advisory boards for Janssen, Insulet, Boehringer Ingelheim, Medtronic, Dompe, Provention, Eli Lilly, Roche, and Dexcom. BK has received honoraria as a speaker from Dexcom and Tandem and reports research support, managed by the University of Virginia, from Dexcom, Novo Nordisk, and Tandem Diabetes Care. BK receives patent royalties, managed by the University of Virginia, for US Patent numbers; #7,815,569 B2, #8,135,548 B2, #8,718,958 B2, #9,882,660 B2, #10,194,850, and #11,289,201. TK has received honoraria from Medtronic, Abbott, Insulet, DexCom, Tandem, Sanofi, Eli Lilly, Novo Nordisk, Merck and Janssen. TK has participated in advisory boards for Medtronic, Abbott, Insulet, Dexcom, Tandem, Sanofi, Eli Lilly, Novo Nordisk, Merck, and Janssen. She has received research support from Abbott and Sanofi. DMaa has consulted for Abbott, Aditxt, Biospex, Dompe, Eli Lilly, the Helmsley Charitable Trust, Insulet, Lifescan, Mannkind, Medtronic, Novo Nordisk, and Sanofi. He has received research support from the Helmsley Charitable Trust, the Juvenile Diabetes Research Foundation, the National Institutes of Health, the National Science Foundation, and his institution (Stanford Diabetes Research Center) has had research support from Dexcom, Insulet, Medtronic, and Tandem. CM has received honoraria from Novo Nordisk, Sanofi, Merck Sharp and Dohme, Eli Lilly, Novartis, AstraZeneca, Boehringer Ingelheim, Roche, Medtronic, ActoBio Therapeutics, Pfizer, Imcyse, Insulet, Zealand Pharma, Avotres, Mannkind, Sandoz, and Vertex. CM has participated on advisory boards for Novo Nordisk, Sanofi, Merck Sharp and Dohme, Eli Lilly, Novartis, AstraZeneca, Boehringer Ingelheim, Roche, Medtronic, ActoBio Therapeutics, Pfizer, Imcyse, Insulet, Zealand Pharma, Avotres, Mannkind, Sandoz, and Vertex. Financial compensation for these activities has been received by KU Leuven. CM has received research support through KU Leuven from Medtronic, Imcyse, Novo Nordisk, Sanofi, and ActoBio Therapeutics. DMau has received honoraria as a speaker and advisory board member for Almirall, Esteve, Ferrer, Janssen, Lilly, Menarini, Merck Sharp and Dohme, Novo Nordisk, and Sanofi. ER serves on advisory boards for Abbott, Air Liquide South Africa, Dexcom, Insulet, Sanofi, Roche Diabetes Care, Novo Nordisk, and Eli Lilly, and has received research support from Dexcom and Tandem. RNim has received speaking and consulting fees from Novo Nordisk, Eli Lilly, and DreaMed Diabetes. He has received research grants from the Helmsley Charitable Trust, Dexcom, Medtronic, Abbott Diabetes Care, and Insulet. RNim owns stock in DreaMed Diabetes. RNis has received honoraria as a speaker for Abbott, Astellas Pharma, Boehringer-Ingelheim, Eli Lilly, Kissei Pharmaceutical, Merck Sharp and Dohme, Medtronic, Novartis, Novo Nordisk, Sanofi, and Takeda. He has received both research fees and research expenses from Abbott, Boehringer-Ingelheim, Ono Pharmaceuticals, Taisho Pharmaceuticals, and Takeda. MS has received honoraria as a speaker and advisory board member from Pfizer, Medtronic, Abbott, Novo Nordisk, Merck, and Sanofi. He has received research grants from Novo Nordisk and is consultant doctor to Biomm and Cristalia. SDP declares grants from AstraZeneca and Boehringer Ingelheim; consulting fees from Applied Therapeutics, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hengrui Pharmaceutical, Merck Sharpe and Dohme, Novartis Pharmaceuticals, Novo Nordisk, and Sanofi; honoraria as a speaker for AstraZeneca, Boehringer Ingelheim, Eli Lilly, Merck Sharpe and Dohme, Novartis Pharmaceuticals, Novo Nordisk, and Sanofi. JR has received consulting fees from Boehringer Ingelheim, Eli Lilly, Novo Nordisk, and Sanofi, and fees for speaking for Eli Lilly, Novo Nordisk, and Sanofi. JR has served on advisory panels for Applied Therapeutics, Boehringer Ingelheim, Eli Lilly, Intarcia, Novo Nordisk, Oramed, Hanmi, Sanofi, and Zealand and has received research support from Applied Therapeutics, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Genentech, Hanmi, Novartis, Intarcia, Merck, Novo Nordisk, Oramed, Pfizer, and Sanofi. KU declares honoraria from Takeda, Novo Nordisk, Nippon, Boehringer Ingelheim, Mitsubishi Tanabe Pharma, AstraZeneca, MSD, Ono, Sumitomo Pharma, Sanofi, Astellas. KU has received grant support from Takeda, Novo Nordisk, Eli Lilly, Nippon, Boehringer Ingelheim, Abbott Japan, Mitsubishi Tanabe Pharma, Merck Sharp and Dohme, Ono, Sumitomo Pharma, Sanofi, Kyowa Kirin, and Daiichi-Sankyo. GEU has received research support, managed by Emory University, from AstraZeneca, Bayer, and Dexcom. SAW has received honoraria from Abbott and Dexcom, has received consulting fees from Zealand, and has received research support for his institution (Sackler Faculty of Medicine, Tel Aviv University) from Abbott and Medtronic. MP has received honoraria for participation on advisory boards from AstraZeneca, Eli Lilly, Insulet, Mannkind, Medtronic Diabetes, Pfizer, Sanofi, and Dompé. MP has received consulting fees from Eli Lilly, Medtronic Diabetes, Novo Nordisk, Pfizer, Sanofi, and Qulab Medical. MP has received research grants from Dexcom, Eli Lilly, Insulet, Medtronic Diabetes, Novo Nordisk, Pfizer, Roche Diagnostics, Sanofi, DreaMed Diabetes, NG Solutions, Dompe, Lumos, GWAVE, and OPKO. MP owns stocks in DreaMed-Diabetes and NG Solutions. All other authors declare no competing interests. Support for the CGMs in Clinical Trials consensus meeting and development was provided by the Time in Range Coalition, Abbott Diabetes Care, Novo Nordisk, Dexcom, Eli Lilly, Apple Pickers Foundation, Diasome, Insulet Corporation, Lifescan, Medtronic, Sanofi, Vertex, Zealand Pharma, and Zucara. C-DISC, Embecta, and Novartis also provided funding to support the consensus meeting. The contents of this consensus statement represent the authors' views and do not constitute an official position of the National Institutes of Health or the United States Government., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: Dysglycemia influences hospital outcomes and resource utilization. Clinical decision support (CDS) holds promise for optimizing care by overcoming management barriers. This study assessed the impact on hospital length of stay (LOS) of an alert-based CDS tool in the electronic medical record that detected dysglycemia or inappropriate insulin use, coined as gaps in care (GIC)., Research Design and Methods: Using a 12-month interrupted time series among hospitalized persons aged ≥18 years, our CDS tool identified GIC and, when active, provided recommendations. We compared LOS during 6-month-long active and inactive periods using linear models for repeated measures, multiple comparison adjustment, and mediation analysis., Results: Among 4,788 admissions with GIC, average LOS was shorter during the tool's active periods. LOS reductions occurred for all admissions with GIC (-5.7 h, P = 0.057), diabetes and hyperglycemia (-6.4 h, P = 0.054), stress hyperglycemia (-31.0 h, P = 0.054), patients admitted to medical services (-8.4 h, P = 0.039), and recurrent hypoglycemia (-29.1 h, P = 0.074). Subgroup analysis showed significantly shorter LOS in recurrent hypoglycemia with three events (-82.3 h, P = 0.006) and nonsignificant in two (-5.2 h, P = 0.655) and four or more (-14.8 h, P = 0.746). Among 22,395 admissions with GIC (4,788, 21%) and without GIC (17,607, 79%), LOS reduction during the active period was 1.8 h (P = 0.053). When recommendations were provided, the active tool indirectly and significantly contributed to shortening LOS through its influence on GIC events during admissions with at least one GIC (P = 0.027), diabetes and hyperglycemia (P = 0.028), and medical services (P = 0.019)., Conclusions: Use of the alert-based CDS tool to address inpatient management of dysglycemia contributed to reducing LOS, which may reduce costs and improve patient well-being., (© 2022 by the American Diabetes Association.)

Objective: Administration of supplemental sliding scale insulin for correction of hyperglycemia in non-intensive care unit (ICU) patients with type 2 diabetes is frequently used with basal-bolus insulin regimens. In this noninferiority randomized controlled trial we tested whether glycemic control is similar with and without aggressive sliding scale insulin treatment 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 nonintensive (correction for BG >260 mg/dL, n = 107) administration of rapid-acting sliding scale insulin before meals and bedtime. The groups received the same amount of sliding scale insulin 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 nonintensive group was noninferior to BG in the intensive group with equivalence margin of 18 mg/dL (intensive 172 ± 38 mg/dL vs. nonintensive 173 ± 43 mg/dL, P = 0.001 for noninferiority). There were no differences in the proportion of target BG readings of 70-180 mg/dL, <70 or <54 mg/dL (hypoglycemia), or >350 mg/dL (severe hyperglycemia) or total, basal, or prandial insulin doses. Significantly fewer subjects received sliding scale insulin in the nonintensive (n = 36 [34%]) compared with the intensive (n = 98 [91%] [P < 0.0001]) group with no differences in sliding scale insulin doses between the groups among those who received sliding scale insulin (intensive 7 ± 4 units/day vs. nonintensive 8 ± 4 units/day, P = 0.34)., Conclusions: Among non-ICU patients with type 2 diabetes on optimal basal-bolus insulin regimen with moderate hyperglycemia (BG <260 mg/dL), a less intensive sliding scale insulin treatment did not significantly affect glycemic control., (© 2022 by the American Diabetes Association.)

Competing Interests: GEU is the current president of Medicine and Science at the American Diabetes Association; declares partial support by The National Center for Advancing Translational Sciences at the National Institutes of Health (NIH; UL1 TR002378) from the Clinical and Translational Science Award program and from the NIH National Institute of Diabetes and Digestive and Kidney Diseases (1P30DK111024-01); and has received unrestricted research support for research studies, paid to Emory University from Dexcom, AstraZeneca, and Bayer. All other authors declare no competing interests.

Background: Adult patients with diabetes or newly recognized hyperglycemia account for over 30% of noncritically ill hospitalized patients. These patients are at increased risk for adverse clinical outcomes in the absence of defined approaches to glycemic management., Objective: To review and update the 2012 Management of Hyperglycemia in Hospitalized Patients in Non-Critical Care Settings: An Endocrine Society Clinical Practice Guideline and to address emerging areas specific to the target population of noncritically ill hospitalized patients with diabetes or newly recognized or stress-induced hyperglycemia., Methods: A multidisciplinary panel of clinician experts, together with a patient representative and experts in systematic reviews and guideline development, identified and prioritized 10 clinical questions related to inpatient management of patients with diabetes and/or hyperglycemia. The systematic reviews queried electronic databases for studies relevant to the selected questions. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess the certainty of evidence and make recommendations., Results: The panel agreed on 10 frequently encountered areas specific to glycemic management in the hospital for which 15 recommendations were made. The guideline includes conditional recommendations for hospital use of emerging diabetes technologies including continuous glucose monitoring and insulin pump therapy; insulin regimens for prandial insulin dosing, glucocorticoid, and enteral nutrition-associated hyperglycemia; and use of noninsulin therapies. Recommendations were also made for issues relating to preoperative glycemic measures, appropriate use of correctional insulin, and diabetes self-management education in the hospital. A conditional recommendation was made against preoperative use of caloric beverages in patients with diabetes., Conclusion: The recommendations are based on the consideration of important outcomes, practicality, feasibility, and patient values and preferences. These recommendations can be used to inform system improvement and clinical practice for this frequently encountered inpatient population., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: Approximately 50% of obese Black patients with unprovoked diabetic ketoacidosis (DKA) or severe hyperglycemia (SH) at new-onset diabetes achieve near-normoglycemia remission with intensive insulin treatment. Despite the initial near-normoglycemia remission, most DKA/SH individuals develop hyperglycemia relapse after insulin discontinuation. Traditional biomarkers such as normal glucose tolerance at the time of remission were not predictive of hyperglycemia relapse. We tested whether 1-h plasma glucose (1-h PG) at remission predicts hyperglycemia relapse in Black patients with DKA/SH., Methods: Secondary analysis was performed of two prospective randomized controlled trials in 73 patients with DKA/SH at the safety net hospital with a median follow-up of 408 days. Patients with DKA/SH underwent a 5-point, 2-h 75-g oral glucose tolerance test after hyperglycemia remission. Hyperglycemia relapse is defined by fasting blood glucose (FBG) > 130 mg/dl, random blood glucose (BG) >180 mg/dl, or HbA1c > 7%., Results: During the median 408 (interquartile range: 110-602) days of follow-up, hyperglycemia relapse occurred in 28 (38.4%) participants. One-hour PG value ≥199 mg/dl discriminates hyperglycemia relapse (sensitivity: 64%; specificity: 71%). Elevated levels of 1-h PG (≥199 mg/dl) were independently associated with hyperglycemia relapse (adjusted hazard ratio: 2.40 [95% CI: 1.04, 5.56]). In a multivariable model with FBG, adding 1-h PG level enhanced the prediction of hyperglycemia relapse, with significant improvements in C-index (Δ: +0.05; p = 0.04), net reclassification improvement (NRI: 48.7%; p = 0.04), and integrated discrimination improvement (IDI: 7.8%; p = 0.02) as compared with the addition of 2-h PG (NRI: 20.2%; p = 0.42; IDI: 1.32%; p = 0.41) or HbA1c (NRI: 35.2%; p = 0.143; IDI: 5.8%; p = 0.04)., Conclusion: One-hour PG at the time of remission is a better predictor of hyperglycemia relapse than traditional glycemic markers among obese Black patients presenting with DKA/SH. Testing 1-h PG at insulin discontinuation identifies individuals at high risk of developing hyperglycemia relapse., Competing Interests: DDS was Speaker Bureau and Ad hoc consultant of: Novo Nordisk and Bayer. GU has received unrestricted research support for research studies (to Emory University) from Merck, Novo Nordisk, and Dexcom Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Jagannathan, Stefanovski, Smiley, Oladejo, Cotten, Umpierrez and Vellanki.)

Complications of Coronavirus Disease 2019 (COVID-19) occur with increased frequency in people admitted to the hospital with diabetes or hyperglycemia. The increased risk for COVID-19 infections in the presence of these metabolic conditions is in part due to overlapping pathophysiologic features of COVID-19, diabetes, and glucose control. Various antiviral treatments are being tested in COVID-19 patients. We believe that in these trials, it will be useful to evaluate treatment effect differences in patients stratified according to whether they have diabetes or hyperglycemia. In this way, it will be possible to better facilitate development of antiviral treatments that are most specifically beneficial for the large subset of COVID-19 patients who have diabetes or hyperglycemia.

Importance: Hyperglycemic crises (ie, diabetic ketoacidosis [DKA] and hyperglycemic hyperosmolar state [HHS]) are life-threatening acute complications of diabetes. Efforts to prevent these events at the population level have been hindered by scarce granular data and difficulty in identifying individuals at highest risk., Objective: To assess sociodemographic, clinical, and treatment-related factors associated with hyperglycemic crises in adults with type 1 or type 2 diabetes in the US from 2014 to 2020., Design, Setting, and Participants: This retrospective cohort study analyzed administrative claims and laboratory results for adults (aged ≥18 years) with type 1 or type 2 diabetes from the OptumLabs Data Warehouse from January 1, 2014, through December 31, 2020., Main Outcomes and Measures: Rates of emergency department or hospital visits with a primary diagnosis of DKA or HHS (adjusted for age, sex, race/ethnicity, and region, and for year when calculating annualized rates) were calculated separately for patients with type 1 diabetes and type 2 diabetes. The associations of sociodemographic factors (age, sex, race/ethnicity, region, and income), clinical factors (comorbidities), and treatment factors (glucose-lowering medications, hemoglobin A1c) with DKA or HHS in patients with type 1 or type 2 diabetes were assessed using negative binomial regression., Results: Among 20 156 adults with type 1 diabetes (mean [SD] age, 46.6 [16.5] years; 51.2% male; 72.6% White race/ethnicity) and 796 382 with type 2 diabetes (mean [SD] age, 65.6 [11.8] years; 50.3% female; 54.4% White race/ethnicity), adjusted rates of hyperglycemic crises were 52.69 per 1000 person-years (95% CI, 48.26-57.12 per 1000 person-years) for type 1 diabetes and 4.04 per 1000 person-years (95% CI, 3.88-4.21 per 1000 person-years) for type 2 diabetes. In both groups, factors associated with the greatest hyperglycemic crisis risk were low income (≥$200 000 vs <$40 000: type 1 diabetes incidence risk ratio [IRR], 0.61 [95% CI, 0.46-0.81]; type 2 diabetes IRR, 0.69 [95% CI, 0.56-0.86]), Black race/ethnicity (vs White race/ethnicity: type 1 diabetes IRR, 1.33 [95% CI, 1.01-1.74]; type 2 diabetes IRR, 1.18 [95% CI, 1.09-1.27]), high hemoglobin A1c level (≥10% vs 6.5%-6.9%: type 1 diabetes IRR, 7.81 [95% CI, 5.78-10.54]; type 2 diabetes IRR, 7.06 [95% CI, 6.26-7.96]), history of hyperglycemic crises (type 1 diabetes IRR, 7.88 [95% CI, 6.06-9.99]; type 2 diabetes IRR, 17.51 [95% CI, 15.07-20.34]), severe hypoglycemia (type 1 diabetes IRR, 2.77 [95% CI, 2.15-3.56]; type 2 diabetes IRR, 4.18 [95% CI, 3.58-4.87]), depression (type 1 diabetes IRR, 1.62 [95% CI, 1.37-1.92]; type 2 diabetes IRR, 1.46 [95% CI, 1.34-1.59]), neuropathy (type 1 diabetes IRR, 1.64 [95% CI, 1.39-1.93]; type 2 diabetes IRR, 1.25 [95% CI, 1.17-1.34]), and nephropathy (type 1 diabetes IRR, 1.22 [95% CI, 1.01-1.48]; type 2 diabetes IRR, 1.23 [95% CI, 1.14-1.33]). Age had a U-shaped association with hyperglycemic crisis risk in patients with type 1 diabetes (compared with patients aged 18-44 years: 45-64 years IRR, 0.72 [95% CI, 0.59-0.87]; 65-74 years IRR, 0.62 [95% CI, 0.47-0.80]; ≥75 years IRR, 0.96 [95% CI, 0.66-1.38]). In type 2 diabetes, risk of hyperglycemic crises decreased progressively with age (45-64 years IRR, 0.57 [95% CI, 0.51-0.63]; 65-74 years IRR, 0.44 [95% CI, .39-0.49]; ≥75 years IRR, 0.41 [95% CI, 0.36-0.47]). In patients with type 2 diabetes, higher risk was associated with sodium-glucose cotransporter 2 inhibitor therapy (IRR, 1.30; 95% CI, 1.14-1.49) and insulin dependency (compared with regimens with bolus insulin: regimens with basal insulin only, IRR, 0.69 [95% CI, 0.63-0.75]; and without any insulin, IRR, 0.36 [95% CI, 0.33-0.40])., Conclusions and Relevance: In this cohort study, younger age, Black race/ethnicity, low income, and poor glycemic control were associated with an increased risk of hyperglycemic crises. The findings suggest that multidisciplinary interventions focusing on groups at high risk for hyperglycemic crises are needed to prevent these dangerous events.

Hyperglycaemia in people with and without diabetes admitted to the hospital is associated with a substantial increase in morbidity, mortality, and health-care costs. Professional societies have recommended insulin therapy as the cornerstone of inpatient pharmacological management. Intravenous insulin therapy is the treatment of choice in the critical care setting. In non-intensive care settings, several insulin protocols have been proposed to manage patients with hyperglycaemia; however, meta-analyses comparing different treatment regimens have not clearly endorsed the benefits of any particular strategy. Clinical guidelines recommend stopping oral antidiabetes drugs during hospitalisation; however, in some countries continuation of oral antidiabetes drugs is commonplace in some patients with type 2 diabetes admitted to hospital, and findings from clinical trials have suggested that non-insulin drugs, alone or in combination with basal insulin, can be used to achieve appropriate glycaemic control in selected populations. Advances in diabetes technology are revolutionising day-to-day diabetes care and work is ongoing to implement these technologies (ie, continuous glucose monitoring, automated insulin delivery) for inpatient care. Additionally, transformations in care have occurred during the COVID-19 pandemic, including the use of remote inpatient diabetes management-research is needed to assess the effects of such adaptations., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background and Aims: Few studies have reported on the use of continuous glucose monitoring (CGM) during the Covid-19 pandemic. We aimed to examine glycemic control metrics using flash glucose monitoring during insulin treatment and the clinical outcome in hospitalized patients with COVID-19., Methods: Prospective, single-center cohort of adult patients diagnosed with type 2 diabetes or hyperglycemia and COVID-19 infection treated with basal bolus insulin regimen. Glycemic control was assessed with the use of intermittent Freestyle Libre flash glucose monitoring during the hospital stay. Outcome of interest were time in range [TIR], time above [TAR] and below [TBR] range, glycemic variability [coefficient of variation [% CV]), and differences in a composite of complications including ICU admission, acute respiratory distress syndrome (ARDS) and acute kidney injury., Results: A total of 60 patients were included (44 known diabetes and 16 new onset hyperglycemia). In total 190,080 data points of CGM were available, of which 72.5% of values were within the target area [TIR (70-180 mg/dL)], 22% TAR (>180 mg/dL), and 3% were TBR (<70 mg/dL). During treatment, the coefficient of variation (% CV) was 30%. There were no association with TIR, but patients with TAR >180 mg/dl had higher rates of a composite of complications (22.5% vs 16%, p = 0.04)., Conclusions: Basal bolus insulin regimen was safe and effective in achieving inpatient glycemic control in most patients with COVID-19. The association between TAR and complications indicates the need for improved inpatient glycemic control in hospitalized patients with COVID-19., Competing Interests: Declaration of competing interest AMG reports speaker fees from Novo Nordisk, Elli Lilly, Boehringer Ingelheim, Abbott and Medtronic. DCH reports speaker fees from Novo Nordisk, Medtronic and Abbott., (Copyright © 2021 Diabetes India. Published by Elsevier Ltd. All rights reserved.)

Aim: To assess whether treatment with sitagliptin, starting before surgery and continued during the hospital stay, can prevent and reduce the severity of perioperative hyperglycaemia in patients with type 2 diabetes undergoing coronary artery bypass graft (CABG) surgery., Materials and Methods: We conducted a double-blinded, placebo-controlled trial in adults with type 2 diabetes randomly assigned to receive sitagliptin or matching placebo starting 1 day prior to surgery and continued during the hospital stay. The primary outcome was difference in the proportion of patients with postoperative hyperglycaemia (blood glucose [BG] > 10 mmol/L [>180 mg/dL]) in the intensive care unit (ICU). Secondary endpoints included differences in mean daily BG in the ICU and after transition to regular wards, hypoglycaemia, hospital complications, length of stay and need of insulin therapy., Results: We included 182 participants randomized to receive sitagliptin or placebo (91 per group, age 64 ± 9 years, HbA1c 7.6% ± 1.5% and diabetes duration 10 ± 9 years). There were no differences in the number of patients with postoperative BG greater than 10 mmol/L, mean daily BG in the ICU or after transition to regular wards, hypoglycaemia, hospital complications or length of stay. There were no differences in insulin requirements in the ICU; however, sitagliptin therapy was associated with lower mean daily insulin requirements (21.1 ± 18.4 vs. 32.5 ± 26.3 units, P = .007) after transition to a regular ward compared with placebo., Conclusion: The administration of sitagliptin prior to surgery and during the hospital stay did not prevent perioperative hyperglycaemia or complications after CABG. Sitagliptin therapy was associated with lower mean daily insulin requirements after transition to regular wards., (© 2020 John Wiley & Sons Ltd.)

Glucose and insulin metabolism in patients with diabetes are profoundly altered by advanced chronic kidney disease (CKD). Risk of hypoglycemia is increased by failure of kidney gluconeogenesis, impaired insulin clearance by the kidney, defective insulin degradation due to uremia, increased erythrocyte glucose uptake during hemodialysis, impaired counterregulatory hormone responses (cortisol, growth hormone), nutritional deprivation, and variability of exposure to oral antihyperglycemic agents and exogenous insulin. Patients with end-stage kidney disease frequently experience wide glycemic excursions, with common occurrences of both hypoglycemia and hyperglycemia. Assessment of glycemia by glycated hemoglobin (HbA1c) is hampered by a variety of CKD-associated conditions that can bias the measure either to the low or high range. Alternative glycemic biomarkers, such as glycated albumin or fructosamine, are not fully validated. Therefore, HbA1c remains the preferred glycemic biomarker despite its limitations. Based on observational data for associations with mortality and risks of hypoglycemia with intensive glycemic control regimens in advanced CKD, an HbA1c range of 7% to 8% appears to be the most favorable. Emerging data on the use of continuous glucose monitoring in this population suggest promise for more precise monitoring and treatment adjustments to permit fine-tuning of glycemic management in patients with diabetes and advanced CKD., (© Endocrine Society 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Diabetes is one of the world's fastest growing health challenges. Insulin therapy remains a useful regimen for many elderly patients, such as those with moderate to severe hyperglycemia, type 1 diabetes, hyperglycemic emergencies, and those who fail to maintain glucose control on non-insulin agents alone. Recent clinical trials have shown that several non-insulin agents as monotherapy, or in combination with low doses of basal insulin, have comparable efficacy and potential safety advantages to complex insulin therapy regimens. Determining the most appropriate diabetes management plan for older hospitalized patients requires consideration of many factors to prevent poor outcomes related to dysglycemia., Competing Interests: Disclosures F.J. Pasquel has received consulting fees and research support from Merck and Dexcom, and consulting fees from Sanofi, Boehringer Ingelheim, Lilly, and AstraZeneca. G.E. Umpierrez has received unrestricted research support for inpatient studies (to Emory University) from Sanofi, Novo Nordisk, and Dexcom. A. Wallia has received research support from United Health Group, Eli Lilly, and Novo Nordisk. G.M. Davis and K. DeCarlo report no conflicts of interest., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Continuous glucose monitoring (CGM) has become a widely used tool in the ambulatory setting for monitoring glucose levels, as well as detecting uncontrolled hyperglycemia, hypoglycemia, and glycemic variability. The accuracy of some CGM systems has recently improved to the point of manufacture with factory calibration and Food and Drug Administration clearance for nonadjunctive use to dose insulin. In this commentary, we analyze the answers to six questions about what is needed to bring CGM into the hospital as a reliable, safe, and effective tool. The evidence to date indicates that CGM offers promise as an effective tool for monitoring hospitalized patients. During the current coronavirus disease 2019 crisis, we hope to provide guidance to healthcare professionals, who are seeking to reduce exposure to SARS-Cov-2, as well as preserve invaluable personal protective equipment. In this commentary, we address who, what, where, when, why, and how CGM can be adopted for inpatient use.

In past decades, a rapid evolution of diabetes technology led to increased popularity and use of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) in the ambulatory setting for diabetes management, and recently, the artificial pancreas became available. Efforts to translate this technology to the hospital setting have shown accuracy and reliability of CGM, safety of CSII in appropriate populations, improvement of inpatient glycemic control with computerized glycemic management systems, and feasibility of inpatient CGM-CSII closed-loop systems. Several ongoing studies are focusing on continued translation of this technology to improve glycemic control and outcomes in hospitalized patients., Competing Interests: Disclosure G.M. Davis and A.L. Migdal have no disclosures. R.J. Galindo is partly supported by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under Award Number P30DK11102. R.J. Galindo has received research grant support to Emory University for investigator-initiated studies from Novo Nordisk, and consulting fees from Abbott Diabetes Care, Sanofi, Valeritas, and Novo Nordisk. G.E. Umpierrez is partly supported by research grants from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378 from the Clinical and Translational Science Award program and a National Institutes of Health (NIH) grant U30, P30DK11102, and has received research grant support to Emory University for investigator-initiated studies from Sanofi, Novo Nordisk, and Dexcom., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Aims: To determine if treatment with sitagliptin, a dipeptidyl peptidase-4 inhibitor, can prevent stress hyperglycemia in patients without diabetes undergoing coronary artery bypass graft (CABG) surgery., Methods: We conducted a pilot, double-blinded, placebo-controlled randomized trial in adults (18-80 years) without history of diabetes. Participants received sitagliptin or placebo once daily, starting the day prior to surgery and continued for up to 10 days. Primary outcome was differences in the frequency of stress hyperglycemia (blood glucose (BG) >180 mg/dL) after surgery among groups., Results: We randomized 32 participants to receive sitagliptin and 28 to placebo (mean age 64±10 years and HbA1c: 5.6%±0.5%). Treatment with sitagliptin resulted in lower BG levels prior to surgery (101±mg/dL vs 107±13 mg/dL, p=0.01); however, there were no differences in the mean BG concentration, proportion of patients who developed stress hyperglycemia (21% vs 22%, p>0.99), length of hospital stay, rate of perioperative complications and need for insulin therapy in the intensive care unit or during the hospital stay., Conclusion: The use of sitagliptin during the perioperative period did not prevent the development of stress hyperglycemia or need for insulin therapy in patients without diabetes undergoing CABG surgery., Competing Interests: Competing interests: None declared.

Purpose of Review: Hyperglycemia contributes to a significant increase in morbidity, mortality, and healthcare costs in the hospital. Professional associations recommend insulin as the mainstay of diabetes therapy in the inpatient setting. The standard of care basal-bolus insulin regimen is a labor-intensive approach associated with a significant risk of iatrogenic hypoglycemia. This review summarizes recent evidence from observational studies and clinical trials suggesting that not all patients require treatment with complex insulin regimens., Recent Findings: Evidence from clinical trials shows that incretin-based agents are effective in appropriately selected hospitalized patients and may be a safe alternative to complicated insulin regimens. Observational studies also show that older agents (i.e., metformin and sulfonylureas) are commonly used in the hospital, but there are few carefully designed studies addressing their efficacy. Therapy with dipeptidyl peptidase-4 (DPP-4) inhibitors, alone or in combination with basal insulin, may effectively control glucose levels in patients with mild to moderate hyperglycemia. Further studies with glucagon-like peptide-1 (GLP-1) receptor analogs and older oral agents are needed to confirm their safety in the hospital.

Glycated hemoglobin A 1c (A1C) levels have traditionally been the gold standard for assessing glycemic control and treatment efficacy in patients with type 2 diabetes. However, A1C does not take into account fluctuations in blood glucose levels known as glycemic variability (GV). In recent years, GV has become increasingly clinically relevant, because of a better understanding of the need to reach target A1C while avoiding hypoglycemia. GV relates to both hyperglycemia and hypoglycemia, and has been associated with poorer quality of life. Diabetes treatments targeting multiple pathophysiological mechanisms are most beneficial in controlling A1C and reducing GV. In clinical trials, a number of metrics are used to measure GV, many of which are not well understood in the clinical practice. Until a gold standard metric for GV is established, the variety of measurements available may confound the choice of an optimal treatment for an individual patient., (Copyright © 2018. Published by Elsevier Inc.)

Aim: We investigated if a dipeptidyl peptidase-4 inhibitor, sitagliptin, can prevent perioperative stress hyperglycemia in patients without prior history of diabetes mellitus undergoing general surgery., Methods: This double-blind pilot trial randomized general surgery patients to receive sitagliptin (n = 44) or placebo (n = 36) once daily, starting one day prior to surgery and continued during the hospital stay. The primary outcome was occurrence of stress hyperglycemia, defined by blood glucose (BG) >140 mg/dL and >180 mg/dL after surgery. Secondary outcomes included: length-of-stay, ICU transfers, hypoglycemia, and hospital complications., Results: BG >140 mg/dL was present in 44 (55%) of subjects following surgery. There were no differences in hyperglycemia between placebo and sitagliptin (56% vs. 55%, p = 0.93). BG >180 mg/dL was observed in 19% and 11% of patients treated with placebo and sitagliptin, respectively, p = 0.36. Both treatment groups had resulted in similar postoperative BG (148.9 ± 29.4 mg/dL vs. 146.9 ± 35.2 mg/dL, p = 0.73). There were no differences in length-of-stay (4 vs. 3 days, p = 0.84), ICU transfer (3% vs. 5%, p = 1.00), hypoglycemia <70 mg/dL (6% vs. 11%, p = 0.45), and complications (14% vs. 18%, p = 0.76)., Conclusion: Preoperative treatment with sitagliptin did not prevent stress hyperglycemia or complications in individuals without diabetes undergoing surgery., (Published by Elsevier Inc.)

Purpose of Review: The intersection of tuberculosis (TB) disease and type 2 diabetes mellitus is severely hindering global efforts to reduce TB burdens. Diabetes increases the risk of developing TB disease and negatively impacts TB treatment outcomes including culture conversion time, mortality risk, and TB relapse. Recent evidence also indicates plausible mechanisms by which TB disease may influence the pathogenesis and incidence of diabetes. We review the epidemiology of stress hyperglycemia in patients with TB and the pathophysiologic responses to TB disease that are related to established mechanisms of stress hyperglycemia. We also consider clinical implications of stress hyperglycemia on TB treatment, and the role of TB disease on risk of diabetes post-TB., Recent Findings: Among patients with TB disease, the development of stress hyperglycemia may influence the clinical manifestation and treatment response of some patients and can complicate diabetes diagnosis. Research is needed to elucidate the relationship between TB disease and stress hyperglycemia and determine the extent to which stress hyperglycemia impacts TB treatment response. Currently, there is insufficient data to support clinical recommendations for glucose control among patients with TB disease, representing a major barrier for efforts to improve treatment outcomes for patients with TB and diabetes.

Aims: To determine the frequency of increasing levels of stress hyperglycemia and its associated complications in surgery patients without a history of diabetes., Methods: We reviewed hospital outcomes in 1971 general surgery patients with documented preoperative normoglycemia [blood glucose (BG) <140mg/dL] who developed stress hyperglycemia (BG >140mg/dL or >180mg/dL) within 48h after surgery between 1/1/2010 and 10/31/2015., Results: A total of 415 patients (21%) had ≥1 episode of BG between 140 and 180mg/dL and 206 patients (10.5%) had BG>180mg/dL. The median length of hospital stay (LOS) was 9days [interquartile range (IQR) 5,15] for BG between 140 and 180mg/dL and 12days (IQR 6,18) for BG>180mg/dL compared to normoglycemia at 6days (IQR 4,11), both p<0.001. Patients with BG 140-180mg/dL had higher rates of complications with an odds ratio (OR) of 1.68 [95% confidence interval (95% CI) 1.15-2.44], and those with BG>180mg/dL had more complications [OR 3.46 (95% CI 2.24-5.36)] and higher mortality [OR 6.56 (95% CI 2.12-20.27)] compared to normoglycemia., Conclusion: Increasing levels of stress hyperglycemia are associated with higher rates of perioperative complications and hospital mortality in surgical patients without diabetes., (Copyright © 2017. Published by Elsevier Inc.)

Perioperative hyperglycemia is common after cardiac surgery, reported in 60% to 90% of patients with diabetes and in approximately 60% of patients without history of diabetes. Many observational and prospective randomized trials in critically-ill cardiac surgery patients support a strong association between hyperglycemia and poor clinical outcome. Despite ongoing debate about the optimal glucose target, there is strong agreement that improved glycemic control reduces perioperative complications., (Copyright © 2017 Elsevier Inc. All rights reserved.)

A large number of patients with type 2 diabetes (T2D) on basal insulin do not reach their HbA1c goals and require additional therapy to address postprandial hyperglycemia. Guidelines from expert bodies have outlined several approaches to accomplish postprandial glucose (PPG) control, and recent literature suggests several more. This article provides strategies for primary care physicians caring for patients with T2D who do not achieve glycemic control with basal insulin alone. Current treatment guidelines and strategies for improving PPG control are reviewed, including the efficacy, safety, and cost-effectiveness of rapid-acting insulin (RAI) analogs, premixed insulin, glucagon-like peptide-1 (GLP-1) receptor agonists (RAs), dipeptidyl peptidase 4 inhibitors, sodium-glucose cotransporter 2 inhibitors, and α-glucosidase inhibitors. Other approaches, such as combinations of newer basal insulin plus RAI and a fixed-ratio combination of basal insulin and a GLP-1 RA, are also described., (© 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.)

Objective: Glargine and detemir insulin are the two most commonly prescribed basal insulin analogues for the ambulatory and inpatient management of diabetes. The efficacy and safety of basal insulin analogues in the hospital setting has not been established., Methods: This observational study compared differences in glycemic control and outcomes in non-intensive care unit patients with blood glucose (BG) >140 mg/dL who were treated with glargine or detemir, between January 1, 2012, and September 30, 2015, in two academic centers., Results: Among 6,245 medical and surgical patients with hyperglycemia, 5,749 received one or more doses of glargine, and 496 patients received detemir during the hospital stay. There were no differences in the mean daily BG (glargine, 182 ± 46 mg/dL vs. detemir, 180 ± 44 mg/dL; P = .70). There were no differences in mortality, hospital complications, or re-admissions between groups (all, P>.05). After adjusting for potential confounders, there was no statistically significant difference in hypoglycemia rates between treatment groups. Patients treated with detemir required higher total daily basal insulin doses (0.27 ± 0.16 units/kg/day vs. 0.22 ± 0.15 units/kg/day; P<.001). Glargine-treated patients had statistically longer length of stay; however, this difference may not be clinically relevant (6.8 ± 7.4 days vs. 6.0 ± 6.3 days; P<.001)., Conclusion: Our study indicates that treatment with glargine and detemir results in similar inpatient glycemic control in general medicine and surgery patients. Detemir treatment was associated with higher daily basal insulin dose and number of injections. A prospective randomized study is needed to confirm these findings., Abbreviations: BG = blood glucose BMI = body mass index CI = confidence interval eGFR = estimated glomerular filtration rate HbA1c = glycated hemoglobin ICD-9 = International Classification of Diseases, ninth revision ICU = intensive care unit IQR = interquartile range LOS = length-of-stay OR = odd ratio.

Chronic hyperglycaemia and glucose variability are associated with the development of chronic diabetes-related complications. We conducted a pooled analysis of patient-level data from three 24-week, randomized, phase III clinical trials to evaluate the impact of lixisenatide (LIXI) on glycaemic variability (GV) vs placebo as add-on to basal insulin. The main outcome GV measures were standard deviation (s.d.), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG) level, area under the curve for fasting glucose (AUC-F), and high (HBGI) and low blood glucose index (LBGI). The change in GV metrics over 24 weeks and relationships among baseline GV, patient characteristics and outcomes were assessed. Data were pooled from 1198 patients (665 LIXI, 533 placebo). Values for s.d., MAG level, MAGE, HBGI, and AUC-F significantly decreased with LIXI vs placebo, while LBGI values were unchanged. Higher baseline GV measures correlated with older age, longer type 2 diabetes duration, lower body mass index, higher baseline glycated/haemogobin, greater reduction in postprandial glucose (PPG) level, and higher rates of symptomatic hypoglycaemia. These data show that LIXI added to basal insulin significantly reduced GV and PPG excursions vs placebo, without increasing the risk of hypoglycaemia (LBGI)., (© 2017 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Adults aged 65 years and older are the fastest growing segment of the U.S. population, and their number is expected to double to 89 million between 2010 and 2050. The prevalence of diabetes in hospitalized adults aged 65-75 years and over 80 years of age has been estimated to be 20% and 40%, respectively. Similar to general populations, the presence of hyperglycemia and diabetes in elderly patients is associated with increased risk of hospital complications, longer length of stay, and increased mortality compared with subjects with normoglycemia. Clinical guidelines recommend target blood glucose between 140 and 180 mg/dL (7.8 and 10 mmol/L) for most patients in the intensive care unit (ICU). A similar blood glucose target is recommended for patients in non-ICU settings; however, glycemic targets should be individualized in older adults on the basis of a patient's clinical status, risk of hypoglycemia, and presence of diabetes complications. Insulin is the preferred agent to manage hyperglycemia and diabetes in the hospital. Continuous insulin infusion in the ICU and rational use of basal-bolus or basal plus supplement regimens in non-ICU settings are effective in achieving glycemic goals. Noninsulin regimens with the use of dipeptidyl peptidase 4 inhibitors alone or in combination with basal insulin have been shown to be safe and effective and may represent an alternative to basal-bolus regimens in elderly patients. Smooth transition of care to the outpatient setting is facilitated by providing oral and written instructions regarding timing and dosing of insulin as well as education in basic skills for home management., (© 2017 by the American Diabetes Association.)

Objective: We aimed to determine (a) longitudinal changes of inflammatory and oxidative stress markers and (b) the association between markers of inflammation and perioperative complications in coronary artery bypass surgery (CABG) patients treated with intensive vs conservative blood glucose (BG) control., Methods: Patients with diabetes (n = 152) and without diabetes with hyperglycemia (n = 150) were randomized to intensive (n = 151; BG: 100-140 mg/dL) or to conservative (n = 151; BG: 141-180 mg/dL) glycemic targets. Plasma cortisol, high-sensitivity C-reactive protein (hsCRP), tumor necrosis factor-α, interleukin-6 (IL-6), thiobarbituric acid-reactive substances, and 2'-7'-dichlorofluorescein were measured prior to and at days 3, 5, and 30 after surgery., Results: Intensive glycemic control resulted in lower mean BG (132 ± 14 mg/dL vs 154 ± 17 mg/dL, P < 0.001) in the intensive care unit. Plasma cortisol and inflammatory markers increased significantly from baseline after the third and fifth day of surgery (P < 0.001), and returned to baseline levels at 1 month of follow-up. Patients with perioperative complications had higher levels of cortisol, hsCRP, IL-6, and oxidative stress markers compared with those without complications. There were no significant differences in inflammatory and oxidative stress markers between patients, with or without diabetes or complications, treated with intensive or conventional glucose targets., Conclusion: We report no significant differences in circulating markers of acute inflammatory and oxidative stress response in cardiac surgery patients, with or without diabetes, treated with intensive (100-140 mg/dL) or conservative (141-180 mg/dL) insulin regimens., (Copyright © 2017 by the Endocrine Society)

Inpatient hyperglycemia is common and is associated with an increased risk of hospital complications, higher healthcare resource utilization, and higher in-hospital mortality rates. Appropriate glycemic control strategies can reduce these risks, although hypoglycemia is a concern. In critically ill patients, intravenous (IV) insulin is most appropriate, with a starting threshold no higher than 180 mg/dL. Once IV insulin is started, the glucose level should be maintained between 140 and 180 mg/dL. In noncritically ill patients, basal-bolus regimens with basal, prandial, and correction components are preferred for those with good nutritional intake. In contrast, a single dose of long-acting insulin plus correction insulin is preferred for patients with poor or no oral intake. Measuring hemoglobin A1c at admission is important to assess glycemic control and to tailor the treatment regimen at discharge., (Copyright © 2016 Cleveland Clinic.)

Context: A higher prevalence of diabetes-related complications is reported in minority populations; however, it is not known if there are racial disparities in diabetes care and outcomes in hospitalized patients., Objective: Our objective was to determine the association between hyperglycemia, in patients with and without diabetes mellitus (non-DM), and complications among different racial groups., Design: This observational study compared the frequency of hyperglycemia (blood glucose ≥ 180 mg/dL; 10 mmol/L) and DM and hospital complications between Black and White patients hospitalized patients between January 2012 and December 2013., Setting and Participants: Adults admitted to medical and surgery services in two academic hospitals were included in this study., Results: Among 35 866 patients, there were 14 387 Black (40.1%) and 21 479 White patients (59.9%). Blacks had a higher prevalence of hyperglycemia (42.3% vs 36.7%, P < .0001) and DM (34.5% vs 22.8%, P < .0001) and a higher admission rate and mean daily blood glucose (P < .001). Blacks also had higher rates of complications (22.2% vs 19.2%, P < .0001), both in patients with DM (24.7 vs 22.9%, P = .0413) and non-DM with hyperglycemia (41.2% vs 37.2%, P = .0019). Using sequential modelling adjusted for age, gender, body mass index, comorbidities, and insurance coverage, non-DM Blacks with normoglycemia (odds ratio, 1.22; 95% confidence interval, 1.10-1.35) and non-DM Blacks with hyperglycemia (odds ratio, 1.18; 95% confidence interval, 1.04-1.33) had higher number of complications compared to Whites., Conclusions: Black patients have higher rates of hyperglycemia and diabetes, worse inpatient glycemic control, and greater frequency of hospital complications compared to Whites. Non-DM Blacks with hyperglycemia are a particularly vulnerable group. Further investigation is needed to better understand factors contributing the racial disparities in the hospital.

Hyperglycemia is a frequent manifestation of critical and surgical illness, resulting from the acute metabolic and hormonal changes associated with the response to injury and stress (Umpierrez and Kitabchi, Curr Opin Endocrinol. 11:75-81, 2004; McCowen et al., Crit Care Clin. 17(1):107-24, 2001). The exact prevalence of hospital hyperglycemia is not known, but observational studies have reported a prevalence of hyperglycemia ranging from 32 to 60 % in community hospitals (Umpierrez et al., J Clin Endocrinol Metab. 87(3):978-82, 2002; Cook et al., J Hosp Med. 4(9):E7-14, 2009; Farrokhi et al., Best Pract Res Clin Endocrinol Metab. 25(5):813-24, 2011), and 80 % of patients after cardiac surgery (Schmeltz et al., Diabetes Care 30(4):823-8, 2007; van den Berghe et al., N Engl J Med. 345(19):1359-67, 2001). Retrospective and randomized controlled trials in surgical populations have reported that hyperglycemia and diabetes are associated with increased length of stay, hospital complications, resource utilization, and mortality (Frisch et al., Diabetes Care 33(8):1783-8, 2010; Kwon et al., Ann Surg. 257(1):8-14, 2013; Bower et al., Surgery 147(5):670-5, 2010; Noordzij et al., Eur J Endocrinol. 156(1):137-42, 2007; Mraovic et al., J Arthroplasty 25(1):64-70, 2010). Substantial evidence indicates that correction of hyperglycemia reduces complications in critically ill, as well as in general surgery patients (Umpierrez et al., J Clin Endocrinol Metab. 87(3):978-82, 2002; Clement et al., Diabetes Care 27(2):553-97, 2004; Pomposelli et al., JPEN J Parented Enteral Nutr. 22(2):77-81, 1998). This manuscript reviews the pathophysiology of stress hyperglycemia during anesthesia and the perioperative period. We provide a practical outline for the diagnosis and management of preoperative, intraoperative, and postoperative care of patients with diabetes and hyperglycemia.

Background: The impact of obesity on clinical outcomes and hospitalization costs in general surgery patients with and without diabetes (DM) is unknown., Materials and Methods: We reviewed medical records of 2451 patients who underwent gastrointestinal surgery at two university hospitals. Hyperglycemia was defined as BG ≥140 mg/dl. Overweight was defined by body mass index (BMI) between 25-29.9 kg/m(2) and obesity as a BMI ≥30 kg/m(2). Hospital cost was calculated using cost-charge ratios from Centers for Medicare and Medicaid Services. Hospital complications included a composite of major cardiovascular events, pneumonia, bacteremia, acute kidney injury (AKI), respiratory failure, and death., Results: Hyperglycemia was present in 1575 patients (74.8%). Compared to patients with normoglycemia, those with DM and non-DM with hyperglycemia had higher number of complications (8.9% vs. 35.8% vs. 30.0%, p<0.0001), longer hospital stay (5 days vs. 9 days vs. 9 days, p<0.0001), more readmissions within 30 days (9.3% vs. 18.8% vs. 17.2%, p<0.0001), and higher hospitalization costs ($20,273 vs. $79,545 vs. $72,675, p<0.0001). In contrast, compared to normal-weight subjects, overweight and obesity were not associated with increased hospitalization costs ($58,313 vs. $58,173 vs. $66,633, p=0.74) or risk of complications, except for AKI (11.9% vs. 14.8% vs. 20.5%, p<0.0001). Multivariate analysis revealed that DM (OR=4.4, 95% CI=2.8,7.0) or perioperative hyperglycemia (OR=4.1, 95% CI=2.7-6.2) were independently associated with increased risk of complications., Conclusion: Hyperglycemia but not increasing BMI, in patients with and without diabetes undergoing gastrointestinal surgery was associated with a higher number of complications and hospitalization costs., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Objective: The optimal level of glycemic control needed to improve outcomes in cardiac surgery patients remains controversial., Research Design and Methods: We randomized patients with diabetes (n = 152) and without diabetes (n = 150) with hyperglycemia to an intensive glucose target of 100-140 mg/dL (n = 151) or to a conservative target of 141-180 mg/dL (n = 151) after coronary artery bypass surgery (CABG) surgery. After the intensive care unit (ICU), patients received a single treatment regimen in the hospital and 90 days postdischarge. Primary outcome was differences in a composite of complications, including mortality, wound infection, pneumonia, bacteremia, respiratory failure, acute kidney injury, and major cardiovascular events., Results: Mean glucose in the ICU was 132 ± 14 mg/dL (interquartile range [IQR] 124-139) in the intensive and 154 ± 17 mg/dL (IQR 142-164) in the conservative group (P < 0.001). There were no significant differences in the composite of complications between intensive and conservative groups (42 vs. 52%, P = 0.08). We observed heterogeneity in treatment effect according to diabetes status, with no differences in complications among patients with diabetes treated with intensive or conservative regimens (49 vs. 48%, P = 0.87), but a significant lower rate of complications in patients without diabetes treated with intensive compared with conservative treatment regimen (34 vs. 55%, P = 0.008)., Conclusions: Intensive insulin therapy to target glucose of 100 and 140 mg/dL in the ICU did not significantly reduce perioperative complications compared with target glucose of 141 and 180 mg/dL after CABG surgery. Subgroup analysis showed a lower number of complications in patients without diabetes, but not in patients with diabetes treated with the intensive regimen. Large prospective randomized studies are needed to confirm these findings., (© 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Objective: Hyperglycemia, hypoglycemia, and glycemic variability have been associated with increased morbidity, mortality, and overall costs of care in hospitalized patients. At the Stratton VA Medical Center in Albany, New York, a process aimed to improve inpatient glycemic control by remotely assisting primary care teams in the management of hyperglycemia and diabetes was designed., Methods: An electronic query comprised of hospitalized patients with glucose values <70 mg/dL or >350 mg/dL is generated daily. Electronic medical records (EMRs) are individually reviewed by diabetes specialist providers, and management recommendations are sent to primary care teams when applicable. Glucose data was retrospectively examined before and after the establishment of the daily inpatient glycemic survey (DINGS) process, and rates of hyperglycemia and hypoglycemia were compared., Results: Patient-day mean glucose slightly but significantly decreased from 177.6 ± 64.4 to 173.2 ± 59.4 mg/dL (P<.001). The percentage of patient-days with any value >350 mg/dL also decreased from 9.69 to 7.36% (P<.001), while the percentage of patient-days with mean glucose values in the range of 90 to 180 mg/dL increased from 58.1 to 61.4% (P<.001). Glycemic variability, assessed by the SD of glucose, significantly decreased from 53.9 to 49.8 mg/dL (P<.001). Moreover, rates of hypoglycemia (<70 mg/dL) decreased significantly by 41% (P<.001)., Conclusion: Quality metrics of inpatient glycemic control improved significantly after the establishment of the DINGS process within our facility. Prospective controlled studies are needed to confirm a causal association.

Objective: Clinical guidelines recommend point-of-care glucose testing and the use of supplemental doses of rapid-acting insulin before meals and at bedtime for correction of hyperglycemia. The efficacy and safety of this recommendation, however, have not been tested in the hospital setting., Research Design and Methods: In this open-label, randomized controlled trial, 206 general medicine and surgery patients with type 2 diabetes treated with a basal-bolus regimen were randomized to receive either supplemental insulin (n = 106) at bedtime for blood glucose (BG) >7.8 mmol/L or no supplemental insulin (n = 100) except for BG >19.4 mmol/L. Point-of-care testing was performed before meals, at bedtime, and at 3:00 a.m. The primary outcome was the difference in fasting BG. In addition to the intention-to-treat analysis, an as-treated analysis was performed where the primary outcome was analyzed for only the bedtime BG levels between 7.8 and 19.4 mmol/L., Results: There were no differences in mean fasting BG for the intention-to-treat (8.8 ± 2.4 vs. 8.6 ± 2.2 mmol/L, P = 0.76) and as-treated (8.9 ± 2.4 vs. 8.8 ± 2.4 mmol/L, P = 0.92) analyses. Only 66% of patients in the supplement and 8% in the no supplement groups received bedtime supplemental insulin. Hypoglycemia (BG <3.9 mmol/L) did not differ between groups for either the intention-to-treat (30% vs. 26%, P = 0.50) or the as-treated (4% vs. 8%, P = 0.37) analysis., Conclusions: The use of insulin supplements for correction of bedtime hyperglycemia was not associated with an improvement in glycemic control. We conclude that routine use of bedtime insulin supplementation is not indicated for management of inpatients with type 2 diabetes., (© 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Hyperglycemia in patients with and without a prior history of diabetes is an independent marker of morbidity and mortality in critically and noncritically ill patients. Improvement of glycemic control with insulin therapy has been shown to reduce hospital complications in patients with diabetes, but also results in increased rates of hypoglycemia, which have been linked to poor outcomes. Thus, alternative treatment options that can normalize blood glucose levels without undue hypoglycemia are being sought. Incretin-based therapies, such as glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors, stimulate insulin secretion in a glucose-dependent fashion, thus not causing hypoglycemia. Alternative points of view exist regarding insulin versus incretin therapy for the care of these patients. We have brought together the authors on the opposite sides of this discussion with the objective of providing a rational synthesis on how to achieve the best possible control of glycemia in the hospital, using both standard insulin approaches and incretin-based therapies to improve patient outcomes. This review examines the benefits of incretin-based therapy in improving glycemic control in hospitalized patients with stress-induced diabetes and in diabetic patients in critical care and non-critical care settings.

Objective: To study the association between diabetes status, perioperative hyperglycemia, and adverse events in a statewide surgical cohort., Background: Perioperative hyperglycemia may increase the risk of adverse events more significantly in patients without diabetes (NDM) than in those with diabetes (DM)., Methods: Using data from the Surgical Care and Outcomes Assessment Program, a cohort study (2010-2012) evaluated diabetes status, perioperative hyperglycemia, and composite adverse events in abdominal, vascular, and spine surgery at 53 hospitals in Washington State., Results: Among 40,836 patients (mean age, 54 years; 53.6% women), 19% had diabetes; 47% underwent a perioperative blood glucose (BG) test, and of those, 18% had BG ≥180 mg/dL. DM patients had a higher rate of adverse events (12% vs 9%, P < 0.001) than NDM patients. After adjustment, among NDM patients, those with hyperglycemia had an increased risk of adverse events compared with those with normal BG. Among NDM patients, there was a dose-response relationship between the level of BG and composite adverse events [odds ratio (OR), 1.3 for BG 125-180 (95% confidence interval (CI), 1.1-1.5); OR, 1.6 for BG ≥180 (95% CI, 1.3-2.1)]. Conversely, hyperglycemic DM patients did not have an increased risk of adverse events, including those with a BG 180 or more (OR, 0.8; 95% CI, 0.6-1.0). NDM patients were less likely to receive insulin at each BG level., Conclusions: For NDM patients, but not DM patients, the risk of adverse events was linked to hyperglycemia. Underlying this paradoxical effect may be the underuse of insulin, but also that hyperglycemia indicates higher levels of stress in NDM patients than in DM patients.

Inpatient hyperglycemia, in patients with and without a history of diabetes, is associated with increased risk of complications, mortality, and longer hospital stay in medicine and surgical patients. Bedside capillary point of care testing is widely recommended as the preferred method for glucose monitoring and for guiding glycemic management of individual patients; however, the accuracy of most handheld glucose meters is far from optimal. Recent studies in the hospital setting have reported that the use of continuous glucose monitoring (CGM) can provide real-time information about glucose concentration, direction, and rate of change over a period of several days. Because it provides glucose values every 5-10 minutes 24 hours a day, CGM may have an advantage over point of care testing with respect to reducing the incidence of severe hypoglycemia in acute care. Real-time CGM technology may facilitate glycemic control and to reduce hypoglycemia in insulin-treated patients. Recent guidelines, however, have recommended deferring the use of CGM in the adult hospital setting until further data on accuracy and safety become available. In this study, we review the advantages and disadvantages of the use of real-time CGM in the management of dysglycemia in the hospital setting., (© 2014 Diabetes Technology Society.)