Inpatient Glycemic Control Research Articles

Inpatient glycemic control and outcome of COVID-19 patients: A retrospective cohort.

Objectives:Although there have been associations between diabetes and mortality in COVID-19 patients, it is unclear whether this is driven by the disease itself or whether it can be attributed to an inability to exhibit effective glucose control.Methods:We conducted a retrospective cohort study of 292 patients admitted to a tertiary referral center to assess the association of mortality and glycemic control among COVID-19-positive patients. We used a logistic regression model to determine whether average fasting glycemic levels were associated with in-hospital mortality.Results:Among the diabetic and non-diabetic patients, there were no differences between mortality or length of stay. Mean glucose levels in the first 10 days of admission were higher on average among those who died (150–185 mg/dL) compared with those who survived (125–165 mg/dL). When controlling for multiple variables, there was a significant association between mean fasting glucose and mortality (odds ratio = 1.014, p < 0.001). The associations between glucose and mortality remained when controlled for comorbidities and glucocorticoid use.Conclusion:The results of this retrospective study show an association between mortality and inpatient glucose levels, suggesting that there may be some benefit to tighter glucose control in patients diagnosed with COVID-19.

Association Between Achieving Inpatient Glycemic Control and Clinical Outcomes in Hospitalized Patients With COVID-19: A Multicenter, Retrospective Hospital-Based Analysis.

OBJECTIVEDiabetes and hyperglycemia are important risk factors for poor outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19). We hypothesized that achieving glycemic control soon after admission, in both intensive care unit (ICU) and non-ICU settings, could affect outcomes in patients with COVID-19.RESEARCH DESIGN AND METHODSWe analyzed pooled data from the Glytec national database including 1,544 patients with COVID-19 from 91 hospitals in 12 states. Patients were stratified according to achieved mean glucose category in mg/dL (≤7.77, 7.83–10, 10.1–13.88, and >13.88 mmol/L; ≤140, 141–180, 181–250, and >250 mg/dL) during days 2–3 in non-ICU patients or on day 2 in ICU patients. We conducted a survival analysis to determine the association between glucose category and hospital mortality.RESULTSOverall, 18.1% (279/1,544) of patients died in the hospital. In non-ICU patients, severe hyperglycemia (blood glucose [BG] >13.88 mmol/L [250 mg/dL]) on days 2–3 was independently associated with high mortality (adjusted hazard ratio [HR] 7.17; 95% CI 2.62–19.62) compared with patients with BG <7.77 mmol/L (140 mg/dL). This relationship was not significant for admission glucose (HR 1.465; 95% CI 0.683–3.143). In patients admitted directly to the ICU, severe hyperglycemia on admission was associated with increased mortality (adjusted HR 3.14; 95% CI 1.44–6.88). This relationship was not significant on day 2 (HR 1.40; 95% CI 0.53–3.69). Hypoglycemia (BG <70 mg/dL) was also associated with increased mortality (odds ratio 2.2; 95% CI 1.35–3.60).CONCLUSIONSBoth hyperglycemia and hypoglycemia were associated with poor outcomes in patients with COVID-19. Admission glucose was a strong predictor of death among patients directly admitted to the ICU. Severe hyperglycemia after admission was a strong predictor of death among non-ICU patients.

High prevalence of Afro‐Caribbean ethnicity and hypoglycaemia in patients with diabetes and end stage renal disease hospitalized with COVID‐19

End stage renal disease (ESRD) is associated with a high mortality rate among patients hospitalized with COVID-19. To the best of our knowledge, there is limited data on the clinical features, ethnicity, inpatient glycaemic control and outcomes in patients with diabetes related ESRD in the literature. We report the clinical features and outcomes of 39 consecutive ESRD patients (28 on haemodialysis [HD] and 11 with renal transplant) secondary to diabetic kidney disease admitted to a university hospital with COVID-19. We observed a high prevalence of patients of Afro-Caribbean ethnicity hospitalized with COVID-19 with a 73% and 54% prevalence in renal transplant and HD groups respectively. The mortality rate of our cohort was 36%. Nearly a one-third of HD patients and one-fifth of transplant patients had hypoglycaemic events during COVID-19 hospitalization. Adjustment of diabetes treatment was frequently required. Our data highlight the importance of integrated multidisciplinary care of patients with diabetes related ESRD hospitalized with COVID-19.

Forecasting Inpatient Glycemic Control: Extension of Damped Trend Methods to Subpopulations

Aim:Evaluate forecasting models applied to smaller geographic locations within the hospital.Materials & methods:Damped trend models were applied to blood glucose measurements of progressively smaller inpatient geographic subpopulations. Mean absolute percentage error (MAPE) and 95% prediction intervals (PIs) assessed validity of the models to forecasts 48 weeks into the future.Results:MAPE values increased, and 95% PIs widened, when data from progressively smaller geographic areas were analyzed. MAPE values were highest and 95% PIs were broadest with the smallest geographic areas. In contrast, observations missed at larger geographical locations were more evident with smaller subpopulations.Conclusion:The utility of damped trend models to forecast inpatient glucose control diminished when applied to smaller geographic areas within the hospital.

Proposed Use of Continuous Glucose Monitoring for Care of Critically Ill COVID-19 Patients.

Coronavirus disease 2019 (COVID-19) has disproportionately affected patients with diabetes. Mounting evidence has shown that adequate inpatient glycemic control may decrease the risk of mortality. In critically ill patients, insulin drips are the most effective means of controlling blood glucose. However, resource limitations such as the availability of protective equipment and nursing time have discouraged the use of insulin drips during COVID-19. In this commentary, we review existing evidence on the importance of glycemic control in COVID-19 patients with diabetes and propose a protocol for utilizing continuous glucose monitors (CGMs) to improve glycemic control by decreasing the need for bedside management in critically ill COVID-19 patients.

Preadmission Diabetes-Specific Risk Factors for Mortality in Hospitalized Patients With Diabetes and Coronavirus Disease 2019.

OBJECTIVETo examine whether HbA1c, outpatient diabetes treatment regimen, demographics, and clinical characteristics are associated with mortality in hospitalized patients with diabetes and coronavirus disease 2019 (COVID-19).RESEARCH DESIGN AND METHODSThis was a retrospective cohort analysis of patients with diabetes hospitalized with confirmed COVID-19 infection from 11 March to 7 May 2020 at a large academic medical center in New York City. Multivariate modeling was used to assess the independent association of HbA1c levels and outpatient diabetes treatment regimen with mortality, in addition to independent effects of demographic and clinical characteristics.RESULTSWe included 1,126 hospitalized patients with diabetes and COVID-19 for analysis, among whom mean age was 68 years, 50% were male, 75% were Black, mean BMI was 30 kg/m2, 98% had type 2 diabetes, mean HbA1c was 7.5%, and 33.1% died. HbA1c levels were not associated with mortality in unadjusted or adjusted analyses, but an outpatient regimen with any insulin treatment was strongly predictive. Additionally, age, sex, and BMI interacted such that in all age categories, mortality was higher with increasing BMI in males compared with females.CONCLUSIONSIn this large U.S. cohort of hospitalized patients with diabetes and COVID-19, insulin treatment, as a possible proxy for diabetes duration, and obesity rather than long-term glycemic control were predictive of mortality. Further investigation of underlying mechanisms of mortality and inpatient glycemic control is needed.

Quality improvement project for improving inpatient glycaemic control in non-critically ill patients admitted on medical floor with type 2 diabetes mellitus

A significant number of patients admitted to the medical floor have type 2 diabetes mellitus (DM). Lack of a standardised inpatient hyperglycaemia management protocol leads to improper glycaemic control adding...

Factors Influencing Inpatient Insulin Management of Adults With Type 1 and Type 2 Diabetes by Residents and Medical Students

ObjectivesThe day-to-day management of diabetes is often the responsibility of resident physicians in teaching hospitals, yet studies have consistently shown that learners lack confidence and knowledge of inpatient insulin titration. We sought to elucidate factors that influence inpatient management of diabetes by medical learners. We predicted that there would be conscious and unconscious variables at play, and we sought to determine the process by which learners arrived at insulin titration decisions. MethodsWe administered a survey using a hypothetical case based on script concordance testing in 88 medical students and residents at the University of Calgary. We used multilevel, mixed-effects linear regression and random-effects meta-analysis to compare clerks’ and residents’ insulin prescribing, confidence in prescribing decisions and concordance with endocrinologists. ResultsResidents had higher confidence than clerks, yet were less concordant with the insulin decisions of staff endocrinologists. Both residents and clerks prescribed more insulin when the reported blood glucose was higher, or the patient had positive ketones and prescribed less insulin when the patient had hypoglycemic unawareness. Residents prescribed a higher dose of insulin when the charge nurse reported the result compared with a student nurse, but this variable did not significantly impact prescribing by clerks. ConclusionsWe propose a model in which learner factors (knowledge, confidence, experience), patient variables and perceived expectations influence insulin titration decisions via analytical and heuristic processes. We propose that educational interventions designed to improve inpatient glycemic control must, therefore, address both knowledge and systems factors.

113-LB: Home Diabetes Regimen and Admission HbA1c as Predictors of Hospital Glycemic Control: A Pooled Analysis of Inpatient Clinical Trials

Glycated hemoglobin (HbA1c) on admission has been suggested as a predictor of glycemic control in the hospital. However, no studies have examined the interaction of pre-admission patient characteristics (i.e., complexity of home regimen or diabetes duration) and HbA1c on inpatient glycemic control. We pooled data from eight randomized controlled trials of patients assigned to a standardized basal-bolus insulin regimen in the hospital. Patients were categorized according to: A) increasing home regimen complexity [group 1: diet alone or ≤1 oral antihyperglycemic drugs (OADs) (n=338, 39%); group 2: two or more OADs (n=177, 20%); and group 3: insulin based regimens (n=356, 41%) and B) admission HbA1c categories (&amp;lt;7%, 7-9%, or &amp;gt;9%). Logistic regression models adjusting for age, gender, BMI, admission BG, and hospital service (medicine vs. surgery) were constructed to determine associations between home regimen complexity, diabetes duration, HbA1c and inpatient glycemic control. Compared to patients treated at home with OADs, patients using insulin had a longer duration of diabetes and higher admission HbA1c (both p&amp;lt;0.001). Compared to group 1 (reference), patients on insulin were less likely to achieve target glycemic control (mean BG 70-180 mg/dl without hypoglycemia) regardless of admission HbA1c [HbA1c &amp;lt;7% adjusted odd ratio (aOR) 0.36 (0.16, 0.81); HbA1c 7-9% aOR 0.5 (0.30, 0.91), or HbA1c &amp;gt;9% aOR 0.44 (0.24, 0.83), and had higher risk for hypoglycemia (all p&amp;lt;0.05). Patients with a longer duration of diabetes (&amp;gt;10 years) were less likely to achieve good glycemic control compared to patients with shorter duration of diabetes irrespective of home regimen complexity. Though admission HbA1c has been proposed as a predictor of inpatient glycemic control, our data show patients requiring complex therapy with insulin at home are less likely to achieve inpatient glucose targets independent of admission HbA1c. Disclosure M.A. Urrutia: None. Y. Guo: None. M. Fayfman: None. P. Vellanki: Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc. R.J. Galindo: Advisory Panel; Self; Lilly Diabetes. Consultant; Self; Valeritas, Inc. Research Support; Self; Novo Nordisk Inc. Other Relationship; Self; UpToDate. A. Migdal: None. G. Davis: None. I. Anzola: None. B.S. Albury: None. M.F. Scioscia: None. K.W. Zamudio: None. G.E. Umpierrez: None. F.J. Pasquel: Advisory Panel; Self; AstraZeneca, Eli Lilly and Company. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Merck Sharp &amp; Dohme Corp. Research Support; Self; Dexcom, Inc., Merck Sharp &amp; Dohme Corp., National Institutes of Health.

1022-P: Efficacy of Sliding Scale Insulin for Inpatient Glycemic Control in a Multinational Propensity-Matched Cohort Study

Sliding scale insulin (SSI) continues to be widely used for glycemic management of inpatients with T2D despite condemnation in clinical guidelines. To confirm the accuracy of this clinical guideline recommendation, we determined the efficacy and safety of using SSI for the management hyperglycemia of non-critical patients with T2D in a large multinational, propensity-matched cohort study. A total of 53,516 patients with ICD 9/10 code of T2D were included in the dataset. Patients were categorized as having received SSI only vs. basal insulin, and were matched according to age, BMI, Charlson Comorbidity Index, and admission blood glucose (BG). Among patients with admission BG &amp;lt;180 mg/dL treated with SSI, 69% achieved and maintained good glycemic control (mean hospital BG &amp;lt;180 mg/dl), compared to 52% of patients treated with basal insulin (p &amp;lt;.001). Patients treated with basal insulin had higher rates of hypoglycemia than patients treated with SSI (30 vs. 16%, p &amp;lt;.001). There were no differences in rates of lactic acidosis, acute MI, pneumonia, or bacteremia (p=NS). A total of 14.93% of patients initially treated with SSI alone failed to achieve adequate glycemic control and were started on basal insulin during the hospital stay. Those who failed SSI had significantly higher admission BG (227.4±90 vs. 175.8±71 mg/dl), greater rates of CKD (31 vs. 24%), CHF (29 vs. 24%), CAD (35 vs. 31%), and higher Charlson score (4.3±2.7 vs. 3.8±2.7) than patients who remained well controlled on SSI alone (all, p&amp;lt;.001). Conclusion: In a multicenter, international, propensity-matched cohort study, 69% of patients with T2D with admission BG &amp;lt;180 mg/dL treated with SSI achieved good glycemic control. Patients admitted with higher BG and greater comorbidities are more likely to fail treatment with SSI alone. These data suggest that SSI may be appropriate for patients with mild/moderate hyperglycemia, thus simplifying recommendations for inpatient glycemic management. Disclosure A. Migdal: None. C. Fortin: None. Y. Guo: None. I. Hochberg: None. M.F. Magee: Consultant; Self; Mytonomy. Employee; Spouse/Partner; U.S. Food and Drug Administration. Research Support; Self; AstraZeneca, Eli Lilly and Company, National Institute of Diabetes and Digestive and Kidney Diseases. Speaker’s Bureau; Self; American Diabetes Association, Pri-Med LLC. Other Relationship; Self; Endocrine Society. F.J. Pasquel: Advisory Panel; Self; AstraZeneca, Eli Lilly and Company. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Merck Sharp &amp; Dohme Corp. Research Support; Self; Dexcom, Inc., Merck Sharp &amp; Dohme Corp., National Institutes of Health. G.E. Umpierrez: None. Funding Jacobs Research Foundation

23-OR: EHR-Based Blood Glucose Alerts Improve Inpatient Glycemic Control at a Large Academic Medical Center

Hospital blood glucose (BG) management is a challenge. We implemented an electronic health record (EHR)-based system at our 1541-bed academic medical center (AMC), alerting when a patient (pt) experiences significant hyperglycemia (hyper; 2 BGs ≥300 mg/dL over 48 hours) or hypoglycemia (hypo; &amp;lt;70 mg/dL). These Excursion Event Alerts (EEAs) fired to the clinician entering orders and provided links to insulin order sets and consult requests. The EEAs were launched on 14 non-ICU wards in mid-2019. We compared BG control during the 3 months prior to launch vs. the most recent 3 months. The control group consisted of 12 non-EEA wards. Our 1° outcome was the Quality Hyperglycemia Score-2 (QHS2), previously developed to assess/compare the overall BG quality on individual wards. QHS2 (scale, 0-100) incorporates 5 elements of BG management, crediting overall control as well as the minimization of significant fluctuations. After the intervention, there was a substantial 13.8% improvement in mean QHS2 (+9.36, p=0.029), comprised of more pt-days in target (+4.3%, p=0.024) and a trend for less pt-days with severe hyper (-2%, p=0.09), with no change in hypoglycemia. In contrast, there was no change in QHS2 on control wards. We conclude that a glycemic alert system was associated with improved BG control at our large AMC, suggesting that a simple EHR-based intervention can favorably impact hospital diabetes management. Disclosure L.B. Bak: None. T.M. Fandel: None. J.E. Bozzo: None. M.L. DeWitt: None. P. Chen: None. R.A. Pando: None. L. Ferro: None. R. Kaplan: None. K.C. Hendrickson: None. S.B. Amport: None. L. Sussman: None. S.E. Inzucchi: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lexicon Pharmaceuticals, Inc., Novo Nordisk A/S, Sanofi. Consultant; Self; Abbott, Merck &amp; Co., Inc., vTv Therapeutics.

The Management of Blood Glucose Should be Emphasized in the Treatment of COVID-19

Based on the higher mortality and the higher proportion of critically ill adults in coronavirus disease 2019 (COVID-19) patients with diabetes, good inpatient glycemic control is particularly important in the comprehensive treatment of COVID-19. Individualized blood glucose target goals and treatment strategies should be made according to specific circumstances of COVID-19 inpatients with diabetes. For mild patients, a strict glycemic control target (fasting plasma glucose (FPG) 4.4-6.1 mmol/L, 2-hour postprandial plasma glucose (2 h PG) 6.1-7.8 mmol/L) are recommended; a target for the glycemic control of common type patients (FPG 6.1-7.8 mmol/L, 2 h PG 7.8-10.0 mmol/L) and subcutaneous insulin deliver therapy are recommended; a target nonfasting blood glucose range of 10.0 mmol or less per liter for severe-type COVID-19 patients, a relatively Less stringent blood glucose control target (FPG 7.8-10.0 mmol/L, 2 h PG 7.8-13.9 mmol/L) for critically ill patients and intravenous insulin infusion therapy are recommended. Due to the rapid changes in the condition of some patients, the risk of diabetic ketoacidosis (DKA) or hyperglycemic hyperosmolar status (HHS) maybe occur during the treatment. Blood glucose monitoring, dynamic evaluation and timely adjustment of strategies should be strengthened to ensure patient safety and promote early recovery of patients.

An Artificial Neural Network-based Predictive Model to Support Optimization of Inpatient Glycemic Control.

Background: Achieving glycemic control in critical care patients is of paramount importance, and has been linked to reductions in mortality, intensive care unit (ICU) length of stay, and morbidities such as infection. The myriad of illnesses and patient conditions render maintenance of glycemic control very challenging in this setting. Materials and Methods: This study involved collection of continuous glucose monitoring (CGM) data, and other associated measures, from the electronic medical records of 127 patients for the first 72 h of ICU care who upon admission to the ICU had a diagnosis of type 1 (n = 8) or type 2 diabetes (n = 97) or a glucose value >150 mg/dL (n = 22). A neural network-based model was developed to predict a complete trajectory of glucose values up to 135 min ahead of time. Model accuracy was validated using data from 15 of the 127 patients who were not included in the model training set to simulate model performance in real-world health care settings. Results: Predictive models achieved an improved accuracy and performance compared with previous models that were reported by our research team. Model error, expressed as mean absolute difference percent, was 10.6% with respect to interstitial glucose values (CGM) and 15.9% with respect to serum blood glucose values collected 135 min in the future. A Clarke Error Grid Analysis of model predictions with respect to the reference CGM and blood glucose measurements revealed that >99% of model predictions could be regarded as clinically acceptable and would not lead to inaccurate insulin therapy or treatment recommendations. Conclusion: The noted clinical acceptability of these models illustrates their potential utility within a clinical decision support system to assist health care providers in the optimization of glycemic management in critical care patients.

Implementation of an Electronic Dashboard with A Remote Management System to Improve Glycemic Management Among Hospitalized Adults

Objective: Better glycemic control for hospitalized diabetic patients significantly reduces health expenditures and improves disease outcomes. We developed a dynamic dashboard with a remote management system and evaluated its impact on inpatient glycemic control. Methods: This was an observational institution-wide study; study participants were enrolled from a 1,500-bed public medical center from 2016 to 2018. We evaluated the impact of a dynamic dashboard system, which analyzed and monitored all glucose data with virtual glycemic management recommendation by a team of endocrinologists, over 3 × 1-year periods: 2016 (pre-implementation), 2017 (development), and 2018 (implementation). Results: A total of 51,641 discharges with 878,159 blood glucose measurements were obtained during the 3-year period. After implementation of the dashboard system, the proportion of patients with poor glycemic control (hyperglycemia or hypoglycemia) was reduced by 31% (from 10.2 to 7.0 per day per 100 patients with glucose monitoring; P<.001); hyperglycemia decreased by 25% (from 6.1 to 4.6 per day per 100 patients with glucose monitoring; P<.001), and hypoglycemia decreased by 45% (from 4.2 to 2.3 per day per 100 patients with glucose monitoring; P<.001). Furthermore, the trend in the proportion of patients within the treat-to-target range showed significant improvement (P<.001) during the development period, with effectiveness maintained throughout the implementation period. Conclusion: We successfully installed a dynamic, electronic medical records-based dashboard monitoring system to improve inpatient glycemic control. The system, supported by a team of endocrinologists via remote recommendations, could efficiently fill an important need for improved glycemic management among hospitalized adults. Abbreviations: CDE = certified diabetes educator; DM = diabetes mellitus; EMR = electronic medical record; POC = point-of-care; TCVGH = Taichung Veterans General Hospital; UCSF = University of California, San Francisco; U.S. = United States; vGMS = virtual glucose management service.

Glycemic control and FEV1 recovery during pulmonary exacerbations in pediatric cystic fibrosis-related diabetes

RationaleWhether short-term glucose control in cystic fibrosis-related diabetes (CFRD) is associated with FEV1 recovery during acute pulmonary exacerbations is unclear. MethodsData from all patients with CFRD ages 6–21 years hospitalized in 2010–2016 for pulmonary exacerbations at our CF Center were analyzed, including CFRD status at each encounter, all FEV1 recorded during each exacerbation, and relevant clinical covariates. Glucose control was analyzed using meter blood glucose area under the curve (AUC) indices. The primary outcome was FEV1 recovery. ResultsPatients with CFRD who finished IV antibiotics at home were treated for longer than those fully treated in the hospital (22.2 vs. 13.8 days). In those who finished treatment at home, poor inpatient glycemic control was associated with lower lung function improvement: when comparing the 75th to the 25th percentile of each glycemic index (i.e., “poorer” vs. “better” glycemic control), FEV1 recovery at discharge was 20.1% lower for glucose AUC (95%CI −0.4%, −39.9%); 20.9% lower for 48-h AUC (95%CI −2.7%, −39.1%); and 28.2% lower for AUC/day (95%CI −7.1%, −49.3%). Similar results were found at the end of IV antibiotics and at clinic follow-up. Likewise, patients with poor glycemic control had a lower slope of inpatient FEV1 recovery. Analysis in patients with normal glucose tolerance was largely non-significant. No associations were found between hemoglobin A1c and FEV1 recovery. ConclusionsIn patients with CFRD who complete IV antibiotic treatment at home, poor inpatient glycemic control is associated with worse FEV1 recovery despite longer duration of treatment.

Medical software applications for in-hospital insulin therapy: A systematic review.

BackgroundIn-hospital hyperglycemia (HH) is frequent and related to higher morbidity and mortality. Despite the benefits of HH treatment, glycemic control is often poor and neglected. The use of health applications to support diagnosis and therapy is now incorporated into medical practice. Medical applications for inpatient glycemic management have potential to standardize this handling by the nonspecialist physician. However, related studies are scarce. We aim to evaluate the efficacy in inpatient glycemic control parameters of medical software applications in non-critical care settings.MethodsThis systematic review on in-hospital insulin applications was performed according to PRISMA guidelines. Data were extracted in triplicate and methodological quality was verified. Specific outcomes of interest were glycemic control efficacy, hypoglycemia risk, length of in-hospital stay, integration with the electronic medical record and healthcare staff acceptance.ResultsAmong the 573 articles initially identified and subsequent revision of the references of each one, seven studies involving six applications were eligible for the review. A better glycemic control was reported with the use of most in-hospital insulin applications in the studies evaluated, but there was no mention of the time to reach the glycemic goal. The risk of hypoglycemia was low. Different reasons influenced the varied acceptance of the use of applications among health professionals.ConclusionThe six applications of inpatient insulin therapy in a non-critical care environment proved to be useful and safe compared to the usual management. Medical apps are tools that can help improve the quality of patient care.

A Multicenter Study Evaluating Perceptions and Knowledge of Inpatient Glycemic Control Among Resident Physicians: Analyzing Themes to Inform and Improve Care

Objective: In this descriptive study, we evaluated perceptions and knowledge of inpatient glycemic control among resident physicians. Methods: We performed this study at four academic medical centers: the University of Mississippi Medical Center, University of Virginia Health System, University of Louisville Health Sciences Center, and Emory University. We designed a questionnaire, and Institutional Review Board approval was granted at each institution prior to study initiation. We then administered the questionnaire to Internal Medicine and Medicine-Pediatric resident physicians. Results: A total of 246 of 438 (56.2%) eligible resident physicians completed the Inpatient Glycemic Control Questionnaire (IGCQ). Most respondents (85.4%) reported feeling comfortable treating and managing inpatient hyperglycemia, and a majority (66.3%) agreed they had received adequate education. Despite self-reported comfort with knowledge, only 51.2% of respondents could identify appropriate glycemic targets in critically ill patients. Only 45.5% correctly identified appropriate inpatient random glycemic target values in noncritically ill patients, and only 34.1% of respondents knew appropriate preprandial glycemic targets in noncritically ill patients. A small majority (54.1%) were able to identify the correct fingerstick glucose value that defines hypoglycemia. System issues were the most commonly cited barrier to successful inpatient glycemic control. Conclusion: Most respondents reported feeling comfortable managing inpatient hyperglycemia but had difficulty identifying appropriate inpatient glycemic target values. Future interventions could utilize the IGCQ as a pre- and postassessment tool and focus on early resident education along with improving system environments to aid in successful inpatient glycemic control. Abbreviations: DM = diabetes mellitus; Emory = Emory University Healthcare; IGC = inpatient glycemic control; IGCQ = Inpatient Glycemic Control Questionnaire; IRB = Institutional Review Board; PGY = postgraduate year; UMMC = University of Mississippi Medical Center; UVA = University of Virginia Health System; UL = University of Louisville Health Sciences Center.

Too Much Confidence, Too Little Knowledge

Multiple studies have shown an association between blood glucose (BG) levels and clinical outcomes in hospitalized patients ( 1. Murad M.H. Coburn J.A. Coto-Yglesias F. et al. Glycemic control in non-critically ill hospitalized patients: a systematic review and meta-analysis. J Clin Endocrinol Metab. 2012; 97: 49-58 Crossref PubMed Scopus (131) Google Scholar ). Both hyperglycemia and hypoglycemia are associated with increased mortality and morbidity ( 1. Murad M.H. Coburn J.A. Coto-Yglesias F. et al. Glycemic control in non-critically ill hospitalized patients: a systematic review and meta-analysis. J Clin Endocrinol Metab. 2012; 97: 49-58 Crossref PubMed Scopus (131) Google Scholar ). Experts from different professional organizations have suggested slightly different goals but mostly they are within a range of 80 to 200 mg/dL ( 2. American Diabetes Association 15. Diabetes care in the hospital: standards of medical care in diabetes-2019. Diabetes Care. 2019; 42: S173-S181 Crossref PubMed Scopus (155) Google Scholar ). There is consensus about preventing very high and very low BG levels. Besides professional society guidelines, glycemic control to prevent complications of hyperglycemia or hypoglycemia makes common sense. Therefore, many hospitals have set up inpatient glycemic control programs. However, despite a high emphasis on glycemic control in the hospital setting, overall glycemic control remains inadequate in most hospitals ( 5. Cook C.B. Kongable G.L. Potter D.J. Abad V.J. Leija D.E. Anderson M. Inpatient glucose control: a glycemic survey of 126 US hospitals. J Hos Med. 2009; 4: E7-E14 Crossref PubMed Scopus (142) Google Scholar , 6. Draznin B. Gilden J. Golden S.H. Inzucchi S.E. Pathways to quality inpatient management of hyperglycemia and diabetes: a call to action. Diabetes Care. 2013; 36: 1807-1814 Crossref PubMed Scopus (118) Google Scholar ). A study of data obtained from 575 hospitals in the United States revealed about 32% of the hospital patient days with at least 1 BG value above 180 mg/dL and about 6% hospital days with at least 1 BG value below 70 mg/dL ( 7. Swanson C.M. Potter D.J. Kongable G.L. Cook C.B. Update on inpatient glycemic control in hospitals in the United States. Endocr Pract. 2011; 17: 853-861 Abstract Full Text Full Text PDF PubMed Scopus (87) Google Scholar ). Requests for diabetes consults are rather infrequent, even in hospitals with well-established inpatient diabetes management programs. In a recent study, only 13% of the elective surgery patients with diabetes received a diabetes consult and this number increased to 32% after implementation of a major quality improvement program ( 8. Garg R. Schuman B. Bader A. et al. Effect of preoperative diabetes management on glycemic control and clinical outcomes after elective surgery. Ann Surg. 2018; 267: 858-862 Crossref PubMed Scopus (52) Google Scholar ). The number of diabetes consults at other hospitals with less established inpatient diabetes programs are likely to be much smaller.

Implementing simple algorithms to improve glucose and lipid management in people with diabetes and acute coronary syndrome.

Diabetes mellitus is associated with increased risk of adverse outcomes following acute coronary syndrome. Translating evidence-based recommendations into practice is necessary to improve outcomes. We evaluated whether implementing algorithms to guide inpatient care improved glycaemic control, and increased use of sodium-glucose co-transporter 2 (SGLT2) inhibitors and lipid-lowering medication in a tertiary cardiac unit. A 3-month audit (phase 1) was conducted to evaluate hyperglycaemia and dyslipidaemia management, and medication prescriptions. Consecutive people with diabetes admitted for acute coronary syndrome were prospectively identified. Target blood glucose level was defined as 5-10mmol/l. A multidisciplinary committee designed and implemented decision-support algorithms plus education. A 3-month post-implementation audit (phase 2) was conducted. There were 104 people in phase 1 and 101 in phase 2, with similar characteristics [HbA1c 64±20mmol/mol vs. 61±21mmol/mol (8.0±1.8% vs. 7.8±1.9%]. Post implementation, the incidence of blood glucose levels >10mmol/l was lower [phase 1: 46.4% vs. phase 2: 31.8%, rate ratio(RR)=0.77, 95% confidence intervals (CI) 0.60-0.98; P=0.031], without a difference in blood glucose levels <5mmol/l (phase 1: 4.9% vs. phase 2: 4.5%, RR=1.20, 95% CI 0.70-2.08; P=0.506). SGLT2 inhibitor prescriptions increased significantly (baseline to discharge: 12.5% to 15.4% vs. 7.9% to 24.8%; P=0.007) but high-intensity statin prescriptions did not (baseline to discharge: 35.6% to 72.1% vs. 40.6% to 85.1%; P=0.074). Prescription rates of non-statin lipid-lowering medications were not significantly increased. Implementing decision-support algorithms was associated with improved inpatient glycaemic control and increased use of cardioprotective therapies at discharge in people with diabetes and acute coronary syndrome.

Improving inpatient glycemic control by diabetes education program in internal medicine residents

AimsThe purpose of this study was to investigate the effectiveness of an inpatient diabetes care education during the first year of internal medicine residency training on inpatient glycemic control. MethodsThe program was comprised of 1-hr small group teaching per 4-week rotation and twice-a-week morning insulin round by an endocrinologist. Inpatient insulin management guideline leaflet was provided to all internal medicine residents. We retrospectively collected the point-of-care testing for glucose (POCT-glu) data in patients admitted to the general medicine wards and compared the mean of blood glucose (BG) before and after the education program. A total of 134438 POCT-glu values from 7055 patients were analyzed. ResultsAfter the initiation of the education program, mean BG levels significantly decreased during the first year and were lowest during the second year after education (Mean BG at baseline was 161.38 ± 64.10 mg/dL; 1st year, 159.48 ± 62.53 mg/dL and 2nd year, 155.60 ± 64.94 mg/dL, p-value < 0.0001). The reduction of BG levels was more pronounced in the patients with previously undiagnosed diabetes mellitus than patients with underlying diabetes mellitus. The rates of severe hypoglycemia (defined by BG < 40 mg/dL or 2.2 mmol/L) were not significantly different before and after education (baseline 0.12%, 1st year 0.14%, and 2nd year 0.14%, p-value = 0.632). ConclusionsLack of confidence and inadequate knowledge of insulin treatment in physicians were important barriers to glycemic management. Consistent education in internal medicine residents led to a significant improvement in inpatient glycemic control.