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

Currently patients with diabetes comprise up to 25-30% of the census of adult wards and critical care units in our hospitals. Although evidence suggests that avoidance of hyperglycemia (>180 mg/dL) and hypoglycemia (

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.

Introduction: A post hoc analysis of the PIONEER 1-5 and 8 trials assessed the clinically relevant composite endpoints of HbA 1c (glycated haemoglobin) reduction ≥ 1% and body weight loss of ≥ 5% or ≥ 10% with orally administered semaglutide versus comparators., Methods: In the PIONEER trials, people with type 2 diabetes were randomised to orally administered semaglutide versus placebo (PIONEER 1, 4, 5 and 8), empagliflozin (PIONEER 2), sitagliptin (PIONEER 3) and liraglutide (PIONEER 4) for 26-78 weeks. This analysis assessed the proportion of people achieving an HbA 1c reduction of ≥ 1% and body weight loss of ≥ 5% at week 26 and at end of treatment, and the proportion of people achieving an HbA 1c reduction of ≥ 1% and body weight loss of ≥ 10% at end of treatment., Results: Overall, 3506 people in PIONEER 1-5 and 8 were included. At week 26 and at end of treatment, odds of achieving the composite endpoint of an HbA 1c reduction of ≥ 1% and body weight loss of ≥ 5% were significantly greater with orally administered semaglutide 14 mg than with placebo (PIONEER 1, 4, 5 and 8; all p < 0.0001), empagliflozin 25 mg (PIONEER 2, p < 0.0001), sitagliptin 100 mg (PIONEER 3, p < 0.0001) and liraglutide 1.8 mg (PIONEER 4, p < 0.0001). Odds of achieving the composite endpoint of HbA 1c reduction of ≥ 1% and body weight loss of ≥ 10% at end of treatment were also significantly greater with orally administered semaglutide versus comparators., Conclusion: In PIONEER 1-5 and 8, odds of achieving clinically relevant reductions in both HbA 1c and body weight were significantly greater with orally administered semaglutide versus comparators., (© 2023. The Author(s).)

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

The use of diabetes technology, including insulin pumps, continuous glucose monitoring devices, and automated insulin delivery systems, has increased significantly in recent years. As more people with diabetes adopt technology in the outpatient setting, we are seeing these devices more frequently in the inpatient setting. This review offers best-practice guidelines for the continuation of personal diabetes technology use in the inpatient setting. It describes policy and guideline stipulations, roles and responsibilities, and device- and brand-specific considerations. Although these devices are not approved for inpatient use by the U.S. Food and Drug Administration, there is general expert consensus that the continuation of personal diabetes devices during hospitalization is appropriate for patients who have sufficient knowledge, are not critically ill, and retain sufficient mental capacity during an acute illness. Health care systems and inpatient providers need to understand the benefits and limitations of personal diabetes technology use during hospitalization., (© 2022 by the American Diabetes Association.)

Hyperglycemia is a common phenomenon in critically ill patients, even in those without diabetes. Two landmark studies established the benefits of tight glucose control (blood glucose target 80-110 mg/dL) in surgical and medical patients. Since then, literature has consistently demonstrated that both hyperglycemia and hypoglycemia are independently associated with increased morbidity and mortality in a variety of critically ill patients. However, tight glycemic control has subsequently come into question due to risks of hypoglycemia and increased mortality. More recently, strategies targeting euglycemia (blood glucose ≤180 mg/dL) have been associated with improved outcomes, although the risk of hypoglycemia remains. More complex targets (ie, glycemic variability and time within target glucose range) and the impact of individual patient characteristics (ie, diabetic status and prehospital glucose control) have more recently been shown to influence the relationship between glycemic control and outcomes in critically ill patients. Although our understanding has increased, the optimal glycemic target is still unclear and glucose management strategies may require adjustment for individual patient characteristics. As glucose management increases in complexity, we realize that traditional means of using meters and strips and paper insulin titration algorithms are potential limitations to our success. To achieve these complex goals for glycemic control, the use of continuous or near-continuous glucose monitoring combined with computerized insulin titration algorithms may be required. The purpose of this review is to discuss the evidence surrounding the various domains of glycemic control and the emerging data supporting the need for individualized glucose targets in critically ill patients.

Current guidelines advocate the use of insulin for the management of hyperglycemia in the hospital setting. However, insulin is limited by a narrow therapeutic window, frequent errors, a need for expertise and systems-based monitoring, and lack of specificity for metabolic abnormalities that occur during critical illness. As a result, non-insulin alternatives have garnered increasing interest for managing hyperglycemia in the hospital. However, non-insulin therapies have had safety and tolerability concerns, patients may still need insulin for glycemic control, and there have been limited outcomes data supporting their use. In the study by Christiansen and colleagues in the previous issue of Critical Care, pre-admission metformin therapy was associated with reduced mortality in critically ill patients with type 2 diabetes. The mortality benefit persisted after controlling for other variables, and was particularly prominent when metformin was continued during admission. Furthermore, the reduction in mortality was observed despite a slightly increased prevalence of lactic acidosis in metformin users. The protective effects of metformin are purported to be related to pleiotropic, possibly anti-inflammatory mechanisms, raising the question of benefit in patients without diabetes. Thus, the findings warrant a re-appraisal of the risks and benefits of metformin use during critical illness. However, in order to justify the revision of multiple guidelines and changes in product labeling, clinical trials in carefully selected patient populations are indicated.

Objective: To compare a modified fixed meal dosing strategy to flexible meal dosing in hospitalized patients with type 2 diabetes., Research Design and Methods: Patients (N = 126) with refractory hyperglycemia or requiring at least 20 units of insulin per day were randomly assigned to fixed meal dosing (including withholding the dose if less than half of the meal tray was consumed) or flexible meal dosing based upon carbohydrate intake. The inpatient diabetes management team made all treatment adjustments. Outcomes included day 3 mean glucose, 72-h glucose trend analysis, hypoglycemia (<3.9 mmol/L), and inpatient diabetes treatment satisfaction., Results: The mean glucose on day 3 was 9.5 and 8.8 mmol/L in the fixed and flexible meal groups, respectively (P = 0.26). The frequency of hypoglycemia was 23 and 39% overall in the fixed and flexible meal groups (P = 0.08), with half of events occurring in the morning. There was a wide range of carbohydrate intake (median 51 g/meal, 10-90% range 26-72 g on day 3). The fixed dose group required significantly more prandial insulin overall and more correction insulin over time. There was no difference in composite treatment satisfaction or dosing miscalculations between groups., Conclusions: A fixed meal dosing strategy provided similar glucose control as flexible meal dosing, when managed by an inpatient diabetes treatment team. However, a larger sample size would be needed to definitively evaluate a treatment effect of flexible meal dosing in the hospital. Further study is needed to improve the delivery of bolus insulin in hospitalized patients.

Objective: To explore the relationship between inpatient diabetes education (IDE) and hospital readmissions in patients with poorly controlled diabetes., Research Design and Methods: Patients with a discharge diagnosis of diabetes (ICD-9 code 250.x) and HbA1c>9% who were hospitalized between 2008 and 2010 were retrospectively identified. All-cause first readmissions were determined within 30 days and 180 days after discharge. IDE was conducted by a certified diabetes educator or trainee. Relationships between IDE and hospital readmission were analyzed with stepwise backward logistic regression models., Results: In all, 2,265 patients were included in the 30-day analysis and 2,069 patients were included in the 180-day analysis. Patients who received IDE had a lower frequency of readmission within 30 days than did those who did not (11 vs. 16%; P=0.0001). This relationship persisted after adjustment for sociodemographic and illness-related factors (odds ratio 0.66 [95% CI 0.51-0.85]; P=0.001). Medicaid insurance and longer stay were also independent predictors in this model. IDE was also associated with reduced readmissions within 180 days, although the relationship was attenuated. In the final 180-day model, no IDE, African American race, Medicaid or Medicare insurance, longer stay, and lower HbA1c were independently associated with increased hospital readmission. Further analysis determined that higher HbA1c was associated with lower frequency of readmission only among patients who received a diabetes education consult., Conclusions: Formal IDE was independently associated with a lower frequency of all-cause hospital readmission within 30 days; this relationship was attenuated by 180 days. Prospective studies are needed to confirm this association.

Currently patients with diabetes comprise up to 25-30% of the census of adult wards and critical care units in our hospitals. Although evidence suggests that avoidance of hyperglycemia (>180 mg/dL) and hypoglycemia (<70 mg/dL) is beneficial for positive outcomes in the hospitalized patient, much of this evidence remains controversial and at times somewhat contradictory. We have recently formed a consortium for Planning Research in Inpatient Diabetes (PRIDE) with the goal of promoting clinical research in the area of management of hyperglycemia and diabetes in the hospital. In this article, we outline eight aspects of inpatient glucose management in which randomized clinical trials are needed. We refer to four as system-based issues and four as patient-based issues. We urge further progress in the science of inpatient diabetes management. We hope this call to action is supported by the American Diabetes Association, The Endocrine Society, the American Association of Clinical Endocrinologists, the American Heart Association, the European Association for the Study of Diabetes, the International Diabetes Federation, and the Society of Hospital Medicine. Appropriate federal research funding in this area will help ensure high-quality investigations, the results of which will advance the field. Future clinical trials will allow practitioners to develop optimal approaches for the management of hyperglycemia in the hospitalized patient and lessen the economic and human burden of poor glycemic control and its associated complications and comorbidities in the inpatient setting.

Hospital readmission is an important contributor to total medical expenditures and is an emerging indicator of quality of care. Diabetes, similar to other chronic medical conditions, is associated with increased risk of hospital readmission. Risk factors include previous hospitalization, extremes in age, and socioeconomic barriers. Preliminary studies suggest that acute and/or chronic glycemic control may be of importance when diabetes is the primary diagnosis or when it is a comorbidity. Very limited evidence from prospective randomized controlled trials aimed at improving glycemic control is available. However, whether one concludes that inpatient or outpatient glycemic control is partly responsible for reduced hospitalizations, attention to glycemic control in the hospital may facilitate sustained glycemic control post-discharge. Limited prospective and retrospective evidence suggest that the involvement of a diabetes specialist team may improve readmission rates, but attention to more generalized comprehensive approaches may also be worthwhile. Prospective interventional studies targeting interventions for improving glycemic control are needed to determine whether glycemic control impacts readmission rates., (© 2012 Diabetes Technology Society.)

Background: The accuracy of continuous glucose monitoring (CGM) in non-critically ill hospitalized patients with heart failure or severe hyperglycemia (SH) is unknown., Methods: Hospitalized patients with congestive heart failure (CHF) exacerbation (receiving IV or subcutaneous insulin) or SH requiring insulin infusion were compared to outpatients referred for retrospective CGM., Results: Forty-three patients with CHF, 15 patients with SH, and 88 outpatients yielded 470, 164, and 2150 meter-sensor pairs, respectively. Admission glucose differed (188 versus 509 mg/dl in CHF and SH, p < .001) but not the first sensor glucose (p = .35). In continuous glucose error grid analysis, 67-78% of pairs during hypoglycemia were in zones A+B (p = .63), compared with 98-100% in euglycemia (p < .001) and 98%, 92%, and 99% (p = .001) during hyperglycemia for the CHF, SH, and outpatient groups, respectively. Mean absolute relative difference (MARD) was lower in the CHF versus the SH group in glucose strata above 100 mg/dl, but there was no difference between the CHF and outpatient groups. Linear regression models showed that CHF versus outpatient, SH versus CHF, and coefficient of variation were significant predictors of higher MARD. Among subjects with CHF, MARD was not associated with brain natriuretic peptide or change in plasma volume, but it was significantly higher in subjects randomized to IV insulin (p = .04)., Conclusions: The results suggest that SH and glycemic variability are more important determinants of CGM accuracy than known CHF status alone in hospitalized patients., (© 2012 Diabetes Technology Society.)

Content: Recent studies have reported hemoglobin A(1c) (A1c) differences across racial/ethnic groups. Our diverse population allows for further investigation of potential differences in measurements of glycemia., Objectives: Our objectives were to describe and explore baseline racial/ethnic differences in self-monitored plasma glucose profiles, A1c, and 1,5-anhydroglucitol (1,5-AG) in patients with type 2 diabetes enrolled in the Assessing DURAbility of Basal vs. Lispro Mix 75/25 Insulin Efficacy trial., Design, Setting, Patients: The trial enrolled 2094 patients with type 2 diabetes, ages 30-80 yr, from 11 countries., Main Outcome Measures: Estimated mean plasma glucose (MPG), A1c, and 1,5-AG were compared among racial/ethnic groups before and after adjusting for factors affecting glycemia: age, sex, duration of diabetes, body mass index, and MPG., Results: Baseline estimated MPG +/- sd was 220.0 +/- 82.0 mg/dl, mean A1c was 9.0 +/- 1.3%, and 1,5-AG was 5.0 +/- 4.1microg/ml. Estimated MPG did not differ between Caucasian and non-Caucasian groups. A1c was higher in Hispanics (9.4 +/- 1.4%; P < 0.001), Asians (9.2 +/- 1.4%; P < 0.01), and patients of other racial/ethnic groups (9.7 +/- 1.5%; P < 0.001) compared with Caucasians (8.9 +/- 1.2%). Paradoxically, 1,5-AG was higher for Asian (5.7 +/- 4.6 microg/ml) and African patients (6.2 +/- 5.4 microg/ml) vs. Caucasians (4.9 +/- 3.9 microg/ml) (P < 0.01). After adjusting for factors affecting glycemia, A1c was higher (all P

1,5-anhydroglucitol (1,5-AG) is a validated marker of short-term glycemic control. It is a metabolically inert polyol that competes with glucose for reabsorption in the kidneys. Otherwise stable levels of 1,5-AG are rapidly depleted as blood glucose levels exceed the renal threshold for glucosuria. 1,5-AG more accurately predicts rapid changes in glycemia than hemoglobin A1C (A1C) or fructosamine. It is also more tightly associated with glucose fluctuations and postprandial glucose. Thus, 1,5-AG may offer complementary information to A1C. This review will summarize the limitations of current methods of assessing glycemic control, assess the data to support 1,5-AG as a glycemic marker and highlight the scenarios by which 1,5-AG may fill the gap in assessing glycemic control.

Objective: Postprandial hyperglycemia is often inadequately assessed in diabetes management. Serum 1,5-anhydroglucitol (1,5-AG) drops as serum glucose rises above the renal threshold for glucose and has been proposed as a marker for postprandial hyperglycemia. The objective of this study is to demonstrate the relationship between 1,5-AG and postprandial hyperglycemia, as assessed by the continuous glucose monitoring system (CGMS) in suboptimally controlled patients with diabetes., Research Design and Methods: Patients with type 1 or type 2 diabetes and an HbA(1c) (A1C) between 6.5 and 8% with stable glycemic control were recruited from two sites. A CGMS monitor was worn for two consecutive 72-h periods. Mean glucose, mean postmeal maximum glucose (MPMG), and area under the curve for glucose above 180 mg/dl (AUC-180), were compared with 1,5-AG, fructosamine (FA), and A1C at baseline, day 4, and day 7., Results: 1,5-AG varied considerably between patients (6.5 +/- 3.2 mug/ml [means +/- SD]) despite similar A1C (7.3 +/- 0.5%). Mean 1,5-AG (r = -0.45, P = 0.006) correlated with AUC-180 more robustly than A1C (r = 0.33, P = 0.057) or FA (r = 0.38, P = 0.88). MPMG correlated more strongly with 1,5-AG (r = -0.54, P = 0.004) than with A1C (r = 0.40, P = 0.03) or FA (r = 0.32, P = 0.07)., Conclusions: 1,5-AG reflects glycemic excursions, often in the postprandial state, more robustly than A1C or FA. 1,5-AG may be useful as a complementary marker to A1C to assess glycemic control in moderately controlled patients with diabetes.