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

OBJECTIVE: Diabetes 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 METHODS: We 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. RESULTS: Overall, 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). CONCLUSIONS: Both 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.

Glycemic Characteristics and Clinical Outcomes of COVID-19 Patients Hospitalized in the United States.

INTRODUCTION: Diabetes has emerged as an important risk factor for severe illness and death from COVID-19. There is a paucity of information on glycemic control among hospitalized COVID-19 patients with diabetes and acute hyperglycemia. METHODS: This retrospective observational study of laboratory-confirmed COVID-19 adults evaluated glycemic and clinical outcomes in patients with and without diabetes and/or acutely uncontrolled hyperglycemia hospitalized March 1 to April 6, 2020. Diabetes was defined as A1C ≥6.5%. Uncontrolled hyperglycemia was defined as ≥2 blood glucoses (BGs) > 180 mg/dL within any 24-hour period. Data were abstracted from Glytec's data warehouse. RESULTS: Among 1122 patients in 88 U.S. hospitals, 451 patients with diabetes and/or uncontrolled hyperglycemia spent 37.8% of patient days having a mean BG > 180 mg/dL. Among 570 patients who died or were discharged, the mortality rate was 28.8% in 184 diabetes and/or uncontrolled hyperglycemia patients, compared with 6.2% of 386 patients without diabetes or hyperglycemia (P < .001). Among the 184 patients with diabetes and/or hyperglycemia who died or were discharged, 40 of 96 uncontrolled hyperglycemia patients (41.7%) died compared with 13 of 88 patients with diabetes (14.8%, P < .001). Among 493 discharged survivors, median length of stay (LOS) was longer in 184 patients with diabetes and/or uncontrolled hyperglycemia compared with 386 patients without diabetes or hyperglycemia (5.7 vs 4.3 days, P < .001). CONCLUSION: Among hospitalized patients with COVID-19, diabetes and/or uncontrolled hyperglycemia occurred frequently. These COVID-19 patients with diabetes and/or uncontrolled hyperglycemia had a longer LOS and markedly higher mortality than patients without diabetes or uncontrolled hyperglycemia. Patients with uncontrolled hyperglycemia had a particularly high mortality rate. We recommend health systems which ensure that inpatient hyperglycemia is safely and effectively treated.

Risk of Hypoglycemia During Insulin Infusion Directed by Paper Protocol Versus Electronic Glycemic Management System in Critically Ill Patients at a Large Academic Medical Center.

BACKGROUND: Insulin infusions are commonly utilized to control hyperglycemia in critically ill patients and decrease hyperglycemia associated complications. Safety concerns have been raised in trials evaluating methods of glycemic control regarding the incidence of hypoglycemia and its relationship to increased mortality. Electronic glycemic management systems (eGMS) may result in less variable blood glucose (BG) control and less hypoglycemia. This study aimed to compare BG control, time in target BG range, and the rate of hypoglycemia when critically ill patients were managed with an insulin infusion guided by paper-based protocol (PBP) versus eGMS. METHODS: This retrospective review compared critically ill patients ≥ 18 years old that received insulin infusion from March to May 2015 (PBP group) and October to January 2017 (eGMS group). The primary outcome was the incidence of hypoglycemia. Secondary outcomes included frequency and severity of hypoglycemia, duration in glycemic target, length of insulin therapy, as well as ICU and hospital length of stay. RESULTS: Fifty-four patients were evaluated, 27 in each group. Percentage of days with BG <70 mg/dL was significantly reduced after eGMS implementation (21.5% v 1.3%, P < .0001) including the frequency of severe hypoglycemia (BG < 40 mg/dL) (5.4% v 0.01%, P < .0001). Patients in the eGMS group spent a greater amount of time in target BG range (31.5% v 63.7%, P < .0001). CONCLUSIONS: An eGMS has the potential to address many of the unmet needs of an optimal glycemic control strategy, minimizing hypoglycemia, and glycemic variability in a heterogeneous critically ill population.

Safely Converting an Entire Academic Medical Center From Sliding Scale to Basal Bolus Insulin via Implementation of the eGlycemic Management System.

OBJECTIVE: Hyperglycemia is common in the inpatient setting and providers frequently rely on sliding scale insulin. This case study reviews the experience of one hospital moving from high utilization of sliding scale to basal bolus insulin therapy. METHOD: This Retrospective Quality Improvement Study describes the journey of clinicians at a 580-bed hospital to convert from high usage of SSI to BBI. Hyperglycemic adult patients prescribed insulin, with/without a diagnosis of diabetes, were included. RESULTS: Data over the first year showed that patients treated with Glucommander (GM) spent more time in the target range of 70-180 mg/dL than patients treated with non-Glucommander (non-GM), with 2,434 fewer hypoglycemic events and 40,589 fewer hyperglycemic events. Prior to implementation of GM, SSI was close to 95%, BBI at 5%. Within the first month of use, 96% usage of BBI was achieved. Reduction of hypoglycemic events (% of BG < 70 mg/dL) by 21% with 2.16% non-GM compared to GM at 1.74% and severe Hypoglycemia (% of BG < 50 mg/dL) by 50% in the ICU 3% non-GM compared to GM at 1.5%. In addition, patients treated with GM had a shorter LOS than patients treated with non-GM by 3.18 days and used 47.4% less point of care tests per patient. CONCLUSION: Glycemic management improved with use of eGMS. The conversion from SSI to BBI enhanced overall patient safety, eliminated the time and effort otherwise required when manually titrating insulin and reduced overall cost of care for patients on insulin therapy.

Comparison of Computer-Guided Versus Standard Insulin Infusion Regimens in Patients With Diabetic Ketoacidosis.

BACKGROUND: This study was performed to investigate the efficacy of Glucommander (GM) (Glytec®), a computer-based algorithm versus standard (paper form-based) continuous insulin infusion (CII) in the treatment of patients with diabetic ketoacidosis (DKA). METHODS: This was a retrospective multicenter study involving 2665 patients with DKA treated with either GM (n = 1750) or standard protocols (n = 915) across 34 institutions in the United States. GM estimates the rate of CII using an insulin sensitivity factor referred to as a "multiplier" that ranges between 0.01 and 0.03. Outcomes of interest were differences in time to resolve DKA (blood glucose [BG] <200 mg/dL and bicarbonate < 18 mmol/L) and number of hypoglycemic events defined as a BG <70 mg/dl. RESULTS: Treatment with GM was associated with lower rates of hypoglycemia during the time of the insulin drip (12.9% vs 35%, P = .001), faster time to normalization of blood glucose (9.7 ± 8.9 vs 10.97 ± 10.2 hours, P = .0001) and resolution of metabolic acidosis (13.6 ± 11.8 vs 17.3 ± 19.6 hours, P = .0001), and shorter hospital length of stay (3.2 ± 2.9 vs 4.5 ± 4.8 days, P = .01) compared to standard care. Best treatment outcomes were achieved with an initial multiplier of 0.01 and a glucose target range between 120 and 180 mg/dl. CONCLUSION: The GM algorithm in DKA treatment resulted in lower rates of hypoglycemia and faster DKA resolution over standard paper-based algorithms. Prospective randomized clinical trials comparing the efficacy and cost of computer-based algorithms versus standard CII regimens are warranted.

Comparison of an Electronic Glycemic Management System Versus Provider-Managed Subcutaneous Basal Bolus Insulin Therapy in the Hospital Setting.

BACKGROUND: American Diabetes Association (ADA) guidelines recommend a basal bolus correction insulin regimen as the preferred method of treatment for non-critically ill hospitalized patients. However, achieving ADA glucose targets safely, without hypoglycemia, is challenging. In this study we evaluated the safety and efficacy of basal bolus subcutaneous (SubQ) insulin therapy managed by providers compared to a nurse-directed Electronic Glycemic Management System (eGMS). METHOD: This retrospective crossover study evaluated 993 non-ICU patients treated with subcutaneous basal bolus insulin therapy managed by a provider compared to an eGMS. Analysis compared therapy outcomes before Glucommander (BGM), during Glucommander (DGM), and after Glucommander (AGM) for all patients. The blood glucose (BG) target was set at 140-180 mg/dL for all groups. The safety of each was evaluated by the following: (1) BG averages, (2) hypoglycemic events <40 and <70 mg/dL, and (3) percentage of BG in target. RESULT: Percentage of BG in target was BGM 47%, DGM 62%, and AGM 36%. Patients' BGM BG average was 195 mg/dL, DGM BG average was 169 mg/dL, and AGM BG average was 174 mg/dL. Percentage of hypoglycemic events <70 mg/dL was 2.6% BGM, 1.9% DGM, and 2.8% AGM treatment. CONCLUSION: Patients using eGMS in the DGM group achieved improved glycemic control with lower incidence of hypoglycemia (<40 mg/dL and <70 mg/dl) compared to both BGM and AGM management with standard treatment. These results suggest that an eGMS can safely maintain glucose control with less hypoglycemia than basal bolus treatment managed by a provider.

Use of a Computer-Based Insulin Infusion Algorithm to Treat Diabetic Ketoacidosis in the Emergency Department.

BACKGROUND: Efforts at improving quality metrics in diabetes focus on minimizing adverse events and avoiding re-admissions to the hospital. Our experience with Glucommander™ (Glytec, Greenville, SC), a cloud-based insulin management software system, suggested that its use in the emergency department (ED) would be useful in treating patients with mild diabetic ketoacidosis (DKA). MATERIALS AND METHODS: Thirty-five patients seen in the ED with hyperglycemic crises and diagnosed with DKA during one calendar year were reviewed. A retrospective chart review was performed on patients who were placed on Glucommander™ for DKA management. We excluded patients with significant acidosis or concomitant medical illnesses. RESULTS: Initial average capillary glucose level was 487 ± 68 mg/dL, average time to target glucose was 5 h 11 min, and rate of hypoglycemia (blood glucose level <70 mg/dL) was less than 0.3%. Sixteen patients treated with the protocol were discharged from the ED directly, and 19 were admitted. Patients were maintained for an average of 14 ± 1 h on the Glucommander™ protocol. There was a significantly higher anion gap (P = 0.002) and lower serum bicarbonate level (P = 0.006) in the admitted group. We found very low evidence of re-admission (6%) within 30 days of discharge from the ED for DKA patients. No significant glucose-related adverse events were noted. CONCLUSIONS: Use of Glucommander™ for guiding the insulin treatment of mild DKA in the ED can decrease admissions to the hospital for DKA by 45%. Low rates of hypoglycemia make this an option to improve efficiency of utilization of inpatient hospital beds. The cost savings for nonadmissions were estimated at $78,000 over the 12 months of the study. Our results suggest that Glucommander™ is a safe and efficient tool for use in the ED to manage mild to moderate DKA.

Tides of change: improving glucometrics in a large multihospital health care system.

BACKGROUND: This study explores the relationship between education for inpatient diabetes providers and the utilization of insulin order sets, inpatient glucometrics, and length of stay in a large health care system. METHODS: The study included patients with and without the diagnosis of diabetes. An education campaign included provider-directed diabetes education administered via online learning modules and in-person presentations by trained individuals. Relationships among provider-attended diabetes education, order set usage, and inpatient glucometrics (hypo- and hyperglycemia) were analyzed, as well as length of stay. RESULTS: Insulin use knowledge scores for all providers averaged 52%, and improved significantly to 93% (P < .001) by the end of the education intervention period. Likewise utilization of electronic basal-bolus order sets increased from a baseline of 20% for patients receiving insulin to 86% within 6 weeks (P < .01) of introduction of order sets. During the study, the incidence of hypoglycemia and hyperglycemia declined from 1.47% to 1.27% and from 23.21% to 17.80%, respectively. However, these improvements were not sustained beyond the completion of the education campaign. CONCLUSIONS: Education of diabetes health care providers was provided in a large, multihospital system through the use of online learning modules. Adoption of standardized insulin order sets was associated with an improvement in glucometrics. This educational and quality initiative resulted in overall improvements in insulin knowledge, adherence to recommended order sets, inpatient glucometrics, and patient length of stay. These improvements were not sustained, reinforcing the need for repeated educational interventions for those involved in providing inpatient diabetes care.

Effectively identifying the inpatient with hyperglycemia to increase patient care and lower costs.

Recent years have seen an increased focus on merging quality care and financial results. This focus not only extends to the inpatient setting but also is of major importance in assuring effective transitions of care from hospital to home. Inducements to meld the 2 factors include tying payment to quality standards, investing in patient safety, and offering new incentives for providers who deliver high-quality and coordinated care. Once seen as the purview of primary care or specific surgical screening programs, identification of patients with hyperglycemia or undiagnosed diabetes mellitus now presents providers with opportunities to improve care. Part of the new focus will need to address the length of stay for patients with diabetes mellitus. These patients are proven to require longer hospital stays regardless of the admission diagnosis. With reducing length of stay as a major objective, efficiency combined with improved quality is the desired outcome. Even with the mounting evidence supporting the benefits of improving glycemic control in the hospital setting, institutions continue to struggle with inpatient glycemic control. Multiple national groups have provided recommendations for blood glucose assessment and glycated hemoglobin testing. This article identifies the key benefits in identifying patients with hyperglycemia and reviews possible ways to identify, monitor, and treat this potential problem area and thereby increase the level of patient care and cost-effectiveness.