Daily Inpatient Glycemic Survey (DINGS): A Process to Remotely Identify and Assist in the Management of Hospitalized Patients with Diabetes and Hyperglycemia

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.Abbreviations: DINGS = daily inpatient glycemic survey EMR = electronic medical record HbA1c = glycated hemoglobin ICU = intensive care unit VA = Veterans Affairs     

Safety and Efficacy of Glucostabilizer in the Management of Diabetic Ketoacidosis

Objective: To evaluate the safety and efficacy of GlucoStabilizer software intravenous insulin (IV) dosing in comparison to American Diabetes Association protocol-directed provider-guided insulin dose adjustment (PGIA).Methods: GlucoStabilizer calculates the dose of IV insulin required to reach a prescribed target glucose range. GlucoStabilizer has not been fully studied in DKA. This retrospective study compared outcomes in patients with DKA before and after the implementation of GlucoStabilizer. Insulin doses were administered based on GlucoStabilizer calculations or PGIA. The analysis evaluated before-after changes in the amount of insulin used, time to target, hypoglycemia or hypokalemia events, and the time to DKA resolution.Results: We studied 77 patients with insulin doses calculated by GlucoStabilizer and 69 patients with PGIA dosing. GlucoStabilizer was superior to PGIA. Patients treated with GlucoStabilizer-calculated doses did not experience hypoglycemia (N = 0 versus N = 10; P<.001). The 10 unique PGIA patients had a total of 18 episodes with 17 between 55 to 69 mg/dL; 1 <54 mg/dL, and no episodes <40 mg/dL. The GlucoStabilizer group required less insulin to reach DKA resolution (59.2 versus 101.2 units; P<.001). Time to glycemic target and DKA resolution were similar (6.7 versus 4.6 hours; P = .132) and (9.8 versus 9.9 hours; P = .803), respectively. No difference in the incidence of hypokalemia was seen (N = 9 versus N = 11; P = .48).Conclusion: This study demonstrates the Gluco Stabilizer settings that can be successfully used in the management of DKA with the avoidance of hypoglycemia. Patients treated with GlucoStabilizer-calculated doses experienced no hypoglycemia and required less insulin as compared to those managed with PGIA.Abbreviations: ADA = American Diabetes Association; DKA = diabetic ketoacidosis; ED = emergency department; eGMS = electronic glycemic management systems; ICU = intensive care unit; IV = intravenous; PGIA = protocol-directed provider-guided insulin dose adjustment     

Hypoglycemia Rates After Restriction of High-Dose Glargine in Hospitalized Patients

Objective: Hypoglycemia remains one of the main challenges of insulin therapy. To reduce insulin-related hypoglycemia at our institution, we restricted inpatient ordering of high glargine doses (≥0.5 U/kg/day) to endocrine staff in May 2013. This retrospective cohort study assesses its effect on hypoglycemia and glycemic control within 48 hours of admission (ADM).Methods: We identified 692 adult patients hospitalized at Boston Medical Center who received glargine upon ADM from November 1, 2012 through April 30, 2013 as the pre-intervention group, and 651 adult patients admitted between November 1, 2013 and April 30, 2014 as the postintervention group. Demographics, medical history, home insulin regimen, concurrent oral diabetes medications or glucocorticoid administration, ADM serum creatinine, all blood glucose levels (BG) ≤48 hours of ADM, and hemoglobin A1c values ≤3 months were assessed. Hypoglycemia was defined as BG ≤70 mg/dL, and hyperglycemia as BG ≥200 mg/dL. Multivariable regression models assessed potential associations between covariates and incidence of hypoglycemia and average BG ≤48 hours of ADM.Results: Demographics were similar between groups. Significantly less patients received high-dose glargine in the post-intervention group (5.2% vs. 0.3%, P<.001). Incidences of hypoglycemia were significantly lower in the postintervention group (20.9% vs. 17.8%, P<.001 per ADM; 3.4% vs. 2.3%, P = .001 per BG measurements [BGM]). Mean BG levels ≤48 hours of ADM and incidence of hyperglycemia were not significantly different. The adjusted incident rate ratio of hypoglycemia was 0.63 per ADM and 0.74 per BGM in the postintervention group compared to the pre-intervention group (P = .001 and P = .063, respectively).Conclusion: We found that implementation of a restriction on high doses of glargine resulted in lower rates of hypoglycemia without worsening glycemic control.Abbreviations:ADM = admissionBG = blood glucoseBGM = blood glucose measurementsBMC = Boston Medical CenterBMI = body mass indexEMR = electronic medical recordHgbA1c = hemoglobin A1cIRR = incidence rate ratioNPH = neutral protamine HagedornTDD = total daily doseT2D = type 2 diabetes     

Impact of a Hypoglycemia Reduction Bundle and a Systems Approach to Inpatient Glycemic Management

Objective: Uncontrolled hyperglycemia and iatrogenic hypoglycemia represent common and frequently preventable quality and safety issues. We sought to demonstrate the effectiveness of a hypoglycemia reduction bundle, proactive surveillance of glycemic outliers, and an interdisciplinary data-driven approach to glycemic management.Methods: Population: all hospitalized adult non–intensive care unit (non-ICU) patients with hyperglycemia and/or a diagnosis of diabetes admitted to our 550-bed academic center across 5 calendar years (CYs). Interventions: hypoglycemia reduction bundle targeting most common remediable contributors to iatrogenic hypoglycemia; clinical decision support in standardized order sets and glucose management pages; measure-vention (daily measurement of glycemic outliers with concurrent intervention by the inpatient diabetes team); educational programs. Measures and analysis: Pearson chi-square value with relative risks (RRs) and 95% confidence intervals (CIs) were calculated to compare glycemic control, hypoglycemia, and hypoglycemia management parameters across the baseline time period (TP1, CY 2009–2010), transitional (TP2, CY 2011–2012), and mature postintervention phase (TP3, CY 2013). Hypoglycemia defined as blood glucose <70 mg/dL, severe hypoglycemia as <40 mg/dL, and severe hyperglycemia >299 mg/dL.Results: A total of 22,990 non-ICU patients, representing 94,900 patient-days of observation were included over the 5-year study. The RR TP3:TP1 for glycemic excursions was reduced significantly: hypoglycemic stay, 0.71 (95% CI, 0.65 to 0.79); severe hypoglycemic stay, 0.44 (95% CI, 0.34 to 0.58); recurrent hypoglycemic day during stay, 0.78 (95% CI, 0.64 to 0.94); severe hypoglycemic day, 0.48 (95% CI, 0.37 to 0.62); severe hyperglycemic day (>299 mg/dL), 0.76 (95% CI, 0.73 to 0.80).Conclusion: Hyperglycemia and hypoglycemia event rates were both improved, with the most marked effect on severe hypoglycemic events. Most of these interventions should be portable to other hospitals.Abbreviations: BG = blood glucose CDS = clinical decision support CI = confidence interval CY = calendar year DIG = diabetes initiative group EHR = electronic health record ICU = intensive care unit RR = relative risk SHM = Society of Hospital Medicine TP = time period     

Impaired Awareness of Hypoglycemia Continues to be a Risk Factor for Severe Hypoglycemia Despite the Use of Continuous Glucose Monitoring System in Type 1 Diabetes

Objective: Impaired awareness of hypoglycemia (IAH) is a risk factor for severe hypoglycemia in patients with type 1 diabetes (T1D) not using a continuous glucose monitoring (CGM) system. The current study investigated the prevalence of IAH and its relationship with severe hypoglycemia in T1D patients using CGM systems.Methods: This cross-sectional observational study enrolled 135 patients with T1D and ongoing real-time CGM use. A survey was conducted to assess hypoglycemia awareness with the Gold, Clarke, and Pedersen-Bjergaard questionnaires and the 6-month history of severe hypoglycemia. Other diabetes histories and the CGM glucose data were collected.Results: The Gold, Clarke, and Pedersen-Bjergaard questionnaires demonstrated the overall prevalence of IAH/abnormal awareness to be 33.3%, 43.7%, and 77.0%, respectively. Participant age and duration of T1D were consistently related to IAH or hypoglycemia unawareness with all three questionnaires (P<.05). Amongst the patients using CGM for >6 months, 24.5% were found to have at least one episode of severe hypoglycemia in the preceding 6 months. IAH identified by the Gold and Clarke questionnaires and hypoglycemia unawareness identified by the Pedersen-Bjergaard questionnaire were related to 6-, 4.63-, and 5.83-fold increased risk of severe hypoglycemia (P = .001, .004, and .013), respectively. IAH identified by the Gold/Clarke questionnaires was associated with a longer duration of CGM glucose <54 mg/dL and higher glucose coefficients of variation (P<.05).Conclusion: IAH is highly prevalent and related to a higher risk for severe hypoglycemia in T1D patients using CGM.Abbreviations: CGM = continuous glucose monitoring; CI = confidence interval; HAAF = hypoglycemia-associated autonomic failure; HbA1c = hemoglobin A1C; IAH = impaired awareness of hypoglycemia; T1D = type 1 diabetes     

Effects of a Computerized Order Set on the Inpatient Management of Hyperglycemia: A Cluster-Randomized Controlled Trial

ObjectiveTo determine the effects of a computerized order set on the inpatient management of diabetes and hyperglycemia.MethodsWe conducted a cluster-randomized controlled trial on the general medical service of an academic medical center staffed by residents and hospitalists. Consecutively enrolled patients with diabetes mellitus or inpatient hyperglycemia were randomized on the basis of their medical team to usual care (control group) or an admission order set built into the hospital’s computer provider order entry (CPOE) system (intervention group). All teams received a detailed subcutaneous insulin protocol and case-based education. The primary outcome was the mean percent of glucose readings per patient between 60 and 180 mg/dL.ResultsBetween April 5 and June 22, 2006, we identified 179 eligible study subjects. The mean percent of glucose readings per patient between 60 and 180 mg/dL was 75% in the intervention group and 71% in the usual care group (adjusted relative risk, 1.36; 95% confidence interval, 1.03 to 1.80). In comparison with usual care, the intervention group also had a lower patient-day weighted mean glucose (148 mg/dL versus 158 mg/dL, P = .04), less use of sliding-scale insulin by itself (25% versus 58%, P = .01), and no significant difference in the rate of severe hypoglycemia (glucose < 40 mg/dL; 0.5% versus 0.3% of patient-days, P = .58).ConclusionThe use of an order set built into a hospital’s CPOE system led to improvements in glycemic control and insulin ordering without causing a significant increase in hypoglycemia. Other institutions with CPOE should consider adopting similar order sets as part of a comprehensive inpatient glycemic management program. (Endocr Pract. 2010;16:209-218)     

Proactive Protocol-Based Management of Hyperglycemia and Diabetes in Colorectal Surgery Patients

Objective: The management of diabetic patients undergoing elective abdominal surgery continues to be unsystematic, despite evidence that standardized perioperative glycemic control is associated with fewer postoperative surgical complications. We examined the efficacy of a pre-operative diabetes optimization protocol implemented at a single institution in improving perioperative glycemic control with a target blood glucose of 80 to 180 mg/dL.Methods: Patients with established and newly diagnosed diabetes who underwent elective colorectal surgery were included. The control group comprised 103 patients from January 1, 2011, through December 31, 2013, before protocol implementation. The glycemic-optimized group included 96 patients following protocol implementation from January 1, 2014, through July 31, 2016. Data included demographic information, blood glucose levels, insulin doses, hypoglycemic events, and clinical outcomes (length of stay, re-admissions, complications, and mortality).Results: Patients enrolled in the glycemic optimization protocol had significantly lower glucose levels intra-operatively (145.0 mg/dL vs. 158.1 mg/dL; P = .03) and postoperatively (135.6 mg/dL vs. 145.2 mg/dL; P = .005). A higher proportion of patients enrolled in the protocol received insulin than patients in the control group (0.63 vs. 0.48; P = .01), but the insulin was administered less frequently (median [interquartile range] number of times, 6.0 [2.0 to 11.0] vs. 7.0 [5.0 to 11.0]; P = .04). Two episodes of symptomatic hypoglycemia occurred in the control group. There was no difference in clinical outcomes.Conclusion: Improved peri-operative glycemic control was observed following implementation of a standardized institutional protocol for managing diabetic patients undergoing elective colorectal surgery.Abbreviations: HbA1c = glycated hemoglobin A1c; IQR = interquartile range     

Characterizing Glucose Changes Antecedent to Hypoglycemic Events in the Intensive Care Unit

ObjectiveTo determine whether patterns of glucose changes before hypoglycemia vary according to the severity of the event.MethodsIn this retrospective analysis, point-ofcare blood glucose (POC-BG) data were obtained from the intensive care units (ICUs) of a convenience sample of hospitals that responded to a survey on inpatient diabetes management quality improvement initiatives. To evaluate POC-BG levels before hypoglycemic events, data from patients who experienced hypoglycemia during their time in the ICU were examined, and their glucose changes were assessed against a comparison group of patients who achieved a glycemic range of 80 to 110 mg/dL without ever experiencing hypoglycemia. Absolute glucose decrease, glucose rate of change, and glucose variability before hypoglycemic events (< 40, 40-49, 50-59, and 60-69 mg/ dL) were calculated.ResultsA total of 128 419 POC-BG measurements from 2942 patients in 89 ICUs were analyzed. Patients who experienced the most severe hypoglycemic episodes had the largest absolute drop in their glucose levels before the event (P < .001). The glucose rate of change before a hypoglycemic event increased with worsening hypoglycemia: mean (± standard deviation) glucose rate of change was-1.69 (± 2.98) mg/dL per min before an episode with glucose values less than 40 mg/dL, -0.56 (± 2.65) mg/dL per min before an episode with glucose values 60 to 69 mg/dL, but only -0.39 (± 0.70) for patients who attained a glucose range of 80 to 110 mg/dL without hypoglycemia (P < .001). Glucose variability before an event progressively increased with worsening biochemical hypoglycemia and was least among patients achieving glucose concentrations in the 80 to 110-mg/dL range without hypoglycemia (P < .001).ConclusionsAntecedent glucose change and variability were greater for patients who experienced hypoglycemia. If monitored, these patterns could potentially alert clinicians and help them take preventive measures. Further examination of how these parameters interact with other predisposing risk factors for hypoglycemia is warranted. (Endocr Pract. 2012;18:317-324)     

Comparison of Basal Insulin Regimens on Glycemic Variability in Noncritically Ill Patients with Type 2 Diabetes

Objective: To evaluate the impact of different subcutaneous basal insulin regimens on glycemic variability (GV) and hospital complications in non-intensive care unit (ICU) patients with type 2 diabetes (T2D).Methods: This study is a post hoc analysis of 279 general medicine and surgery patients treated with either a “Basal Bolus” insulin regimen using glargine once daily and glulisine before meals or a “Basal Plus” regimen using glargine once daily plus correction doses of glulisine before meals for glucose >140 mg/dL. GV was calculated as mean delta (Δ) daily glucose, mean SD, and mean amplitude of glycemic excursions (MAGE).Results: Treatment with Basal Bolus and Basal Plus regimens resulted in similar mean daily glucose, hypoglycemia, length of stay (LOS), and hospital complications (all P>.05). There were no differences in GV between treatment groups by Δ change (72.5 ± 36 vs. 69.3 ± 34 mg/dL), SD (38.5 ± 18 vs. 37.1 ± 16 mg/dL) and MAGE (67.5 ± 34 vs. 66.1 ± 39 mg/dL) (all P>.05). Surgery patients treated with Basal Bolus had higher GV compared to those treated with Basal Plus (Δ daily glucose and SD: P = .02, MAGE: P = .009), but no difference in GV was found between treatment groups for the general medicine patients (P>.05). Patients with hypoglycemia events had higher GV compared to subjects without hypoglycemia (P<.05), but no association was found between GV and hospital complications (P>.05).Conclusion: Treating hospitalized, non-ICU, diabetic patients with Basal Plus insulin regimen resulted in similar glucose control and GV compared to the standard Basal Bolus insulin regimen. Higher GV was not associated with hospital complications.Abbreviations:BG = blood glucoseCV= coefficient of variationGV= glycemic variabilityICU = intensive care unitLOS = length of stayMAGE = mean amplitude of glycemic excursionsSSI = sliding scale insulinT2D = type 2 diabetesTDD =total daily dose     

Effectiveness of Inpatient Insulin Order Sets Using Human Insulins in Noncritically Ill Patients in A Rural Hospital

Objective: Recent guidelines recommend a physiologic approach to non–intensive care unit (ICU) inpatient glucose management utilizing basal-bolus with correctional (BBC) insulin over traditional sliding-scale insulin monotherapy. Unfortunately, few studies exist using a BBC approach restricted to human insulins (regular and neutral protamine Hagedorn [NPH]). This study evaluated changes in provider prescribing patterns, effects on blood glucose, and safety with implementation of hospital order sets for BBC using human insulins.Methods: Order sets were developed for non-ICU inpatients, consisting of basal, prandial, and correctional insulin using NPH and regular human insulins. Evaluation compared a 4-month period before (admissions, n = 274) with a 4-month period after order set availability (n = 302). Primary outcome was change in insulin prescribing patterns. Secondary outcomes included use of nonpreferred diabetes treatments, hemoglobin A_1c testing, mean daily blood glucose, and incidence of hypoglycemia.Results: Use of BBC insulin regimen increased from 10.6 to 27.5% after order set implementation (P<.001). Use of oral antihyperglycemic agents decreased from 24.1 to 14.9% after implementation (P = .006). Hemoglobin A_1c testing rose from 50.0 to 62.3% after (P = .003). Mean daily blood glucose improved, with an estimated mean difference of 14.4 mg/dL (95% confidence interval, 2.2 to 26.5 mg/dL) over hospital days 3 through 9 (P = .02). There was no significant change in the incidence of moderate or severe hypoglycemia.Conclusion: Implementation of hospital-wide human insulin order sets led to improvements in prescribing practices and blood glucose control, without increasing the incidence of hypoglycemia. These order sets may be useful for facilities limited by formulary and cost considerations to the use of older human insulins.Abbreviations: BBC = basal-bolus with correctional insulin ICU = intensive care unit NPH = neutral protamine Hagedorn NPO = nil per os     

Guidelines to Improve Perioperative Management of Diabetes Mellitus: Assessment of the Impact of Change Across Time

Objective: Assess the impact of guidelines on the care of patients with diabetes undergoing elective surgery.Methods: A multidisciplinary team developed perioperative guidelines. Overall changes in key measures were evaluated after guidelines were introduced and compared with a historical cohort.Results: The historical cohort included 254 surgical procedures, and the post–guidelines implementation cohort comprised 1,387. Glucose monitoring was performed preoperatively in 93% of cases in the post–guidelines implementation cohort and in 88% in the historical cohort (P<.01), but the percentage of cases with measurements decreased over 12 months (from 95% to 91%, P = .044). Glucose was intraoperatively monitored in 67% of cases after guidelines were introduced and in 29% historically (P<.01); the post–guidelines implementation percentage decreased over 12 months from 67% to 55% (P<.01). The performance of glucose monitoring in the postanesthesia care unit (PACU) did not differ (86% vs. 87%, P = .57), but it decreased over 12 months, from 91% to 84% (P<.01). After introduction of the guidelines, insulin use increased in the preoperative, intraoperative, and PACU areas (all P≤.01) but decreased by the end of 12 months (all P<.01). Mean preoperative and PACU glucose levels in the post– guidelines implementation cohort were significantly lower than in the historical cohort (P<.01).Conclusion: Multidisciplinary management guidelines for diabetes patients undergoing surgery can improve the performance of key measures of care. Although adherence to recommendations generally remained higher after guideline implementation than in the historical period, the improvement in several measures began to decline over time.Abbreviations: DM = diabetes mellitus HbA_1c = hemoglobin A_1c PACU = postanesthesia care unit POC-BG = pointof-care blood glucose POME = preoperative medical evaluation     

The Synergy to Enable Glycemic Control Following Emergency Department Discharge Program for Adults with Type 2 Diabetes: Step-Diabetes

Objective: To evaluate a diabetes (DM) care delivery model among hyperglycemic adults with type 2 DM being discharged from the emergency department (ED) to home. The primary hypothesis was that a focused education and medication management intervention would lead to a greater short-term improvement in glycemic control compared to controls.Methods: A 4-week, randomized controlled trial provided antihyperglycemic medications management using an evidence-based algorithm plus survival skills diabetes self-management education (DSME) for ED patients with blood glucose (BG) levels ≥200 mg/dL. The intervention was delivered by endocrinologist-supervised certified diabetes educators. Controls received usual ED care.Results: Among 101 participants (96% Black, 54% female, 62.3% Medicaid and/or Medicare insurance), 77% completed the week 4 visit. Glycated hemoglobin A1C (A1C) went from 11.8 ± 2.4 to 10.5 ± 1.9% (P<.001) and 11.5 ± 2.0 to 11.1 ± 2.1% in the intervention and control groups, respectively (P = .012). At 4 weeks, the difference in A1C reduction between groups was 0.9% (P = .01). Mean BG decreased for both groups (P<.001), with a higher percentage of intervention patients (65%) reaching a BG <180 mg/dL compared to 29% of controls (P = .002). Hypoglycemia rates did not differ by group, and no severe hypoglycemia was reported. Medication adherence (Modified Morisky Score©) improved from low to medium (P<.001) among intervention patients and did not improve among controls.Conclusions: This study provides evidence that a focused diabetes care delivery intervention can be initiated in the ED among adults with type 2 diabetes and hyperglycemia and safely and effectively completed in the ambulatory setting. Improvement in short-term glycemic outcomes and medication adherence were observed.Abbreviations: A1C = glycated hemoglobin A1C BG = blood glucose BMI = body mass index CDE = certified diabetes educator CI = confidence interval DM = diabetes mellitus DSME = diabetes self-management education ED = emergency departmentMMAS-8 = Modified Morisky Medication Scale PCP = primary care provider POC = point of care SQ = subcutaneous     

Management of Hyperglycemia with the Administration of Intravenous Exenatide to Patients in the Cardiac Intensive Care Unit

ObjectiveTo evaluate the feasibility, effectiveness, and safety of intravenous exenatide to control hyperglycemia in the cardiac intensive care unit (CICU).MethodsA prospective, single-center, open-label, nonrandomized pilot study. Forty patients admitted to the CICU with glucose levels of 140 to 400 mg/dL received intravenous exenatide as a bolus followed by a fixed dose infusion for up to 48 hours. Exenatide effectiveness was benchmarked to two historical insulin infusion cohorts, one (INT) with a target glucose of 90 to 119 mg/dL (n = 84) and the other (MOD) with a target of 100 to 140 mg/dL (n = 71).ResultsMedian admission glucose values were 185.5 mg/dL (161.0, 215.5), 259.0 mg/dL (206.0, 343.0), and 189.5 mg/dL (163.5, 245.0) in the exenatide, MOD, and INT groups, respectively (P<.001). Steady state glucose values were similar between the exenatide (132.0 mg/dL [110.0, 157.0]) and the MOD groups (127.0 mg/dL [105.0, P = .15), but lower in the INT group (105.0 mg/dL [92.0, 128.0], P<.001 for exenatide versus INT). Median (IQR) time to steady state was 2.0 hours (1.5, 5.0) in the exenatide group compared to 12.0 hours (7.0, 15.0) in the MOD group (P<.001) and 3.0 hours (1.0, 5.0) in the INT group (P = .80 for exenatide versus INT). Exenatide was discontinued in 3 patients after failure to achieve glycemic control. No episodes of severe hypoglycemia (<50 mg/dL) occurred in patients who received exenatide. Nausea was reported by 16 patients and vomiting by 2 patients.ConclusionIntravenous exenatide is effective in lowering glucose levels in CICU patients, but its use may be limited by nausea. (Endocr Pract. 2013;19:81-90)     

De-Intensification of Diabetes Treatment in Elderly Patients with Type 2 Diabetes Mellitus

Objective: De-intensification of diabetes treatment is recommended in elderly patients with tight glycemic control at high risk of hypoglycemia. However, rates of de-intensification in endocrine practice are unknown. We conducted a retrospective study to evaluate the rate of de-intensification of antidiabetic treatment in elderly patients with type 2 diabetes mellitus (T2DM) and tight glycemic control.Methods: All patients with ≥2 clinic visits over a 1-year period at a major academic diabetes center were included. De-intensification of diabetes treatment was defined as a decrease or discontinuation of any antidiabetic drug without adding another drug, or a reduction in the total daily dose of insulin or a sulfonylurea drug with or without adding a drug without risk of hypoglycemia.Results: Out of 3,186 unique patients, 492 were ≥65 years old with T2DM and hemoglobin A1c (HbA1c) <7.5% (<58 mmol/mol). We found 308 patients treated with a sulfonylurea drug or insulin, 102 of whom had hypoglycemia as per physician note. Among these 102 patients, 38 (37%) were advised to de-intensify therapy. In a subgroup analysis of patients ≥75 years old with HbA1c <7% (<53 mmol/mol), we found that out of 23 patients treated with a sulfonylurea drug or insulin and reporting hypoglycemia, 11 (43%) were advised de-intensification of therapy. There were no significant predictors of de-intensification of treatment.Conclusion: Our study suggests that de-intensification of antidiabetic medications is uncommon in elderly patients with T2DM. Strategies may need to be developed to prevent the potential harm of overtreatment in this population.Abbreviations: ADA = American Diabetes Association; CGM = continuous glucose monitoring; HbA1c = hemoglobin A1c; T2DM = type 2 diabetes mellitus; UKPDS = United Kingdom Prospective Diabetes Study     

Impact of Multidisciplinary Process Improvement Interventions on Glucometrics in a Noncritically Ill Setting

Objective: This study aimed to assess the impact of multidisciplinary process improvement interventions on glycemic control in the inpatient setting of an urban community hospital, utilizing the daily simple average as the primary glucometric measure.Methods: From 2010–2014, five process of care interventions were implemented in the noncritical care inpatient units of the study hospital. Interventions included education of medical staff, implementation of hyperglycemia and hypoglycemia protocols, computerized insulin order entry, and coordination of meal tray delivery with finger stick and insulin administration. Unpaired t tests compared pre- and postintervention process measures. Simple average daily glucose measure was the primary glucometric outcome. Secondary outcome measures included frequency of hyperglycemia and hypoglycemia. Glucose outcomes were compared with an in-network hospital that did not implement the respective interventions.Results: A total of 180,431 glucose measurements were reported from 4,705 and 4,238 patients from the intervention and comparison hospitals, respectively. The time between bolus-insulin administration and breakfast tray delivery was significantly reduced by 81.7 minutes (P<.00005). The use of sliding scale insulin was sustainably reduced. Average daily glucose was reduced at both hospitals, and overall rates of hypoglycemia were low.Conclusion: A multidisciplinary approach at an urban community hospital with limited resources was effective in improving and sustaining processes of care for improved glycemic control in the noncritical care, inpatient setting.Abbreviations: IQR = interquartile range; JMC = Jacobi Medical Center; NCBH = North Central Bronx Hospital     

Temporal Occurrences and Recurrence Patterns of Hypoglycemia During Hospitalization

Objective: To describe the temporal distribution of hypoglycemia and its rate of recurrence during hospitalization to aid in the development of strategies to prevent hypoglycemia in hospitalized patients.Methods: Retrospective review of hypoglycemia (blood glucose <50 mg/dL) audit data in adult hospitalized patients at 2 academic hospitals. Demographics, timing, and blood glucose values were recorded. Antihyperglycemic medications, number of recurrent events, and change in basal insulin dose following the hypoglycemic event were also extracted.Results: A total of 274 index occurrences of hypoglycemia were analyzed. The mean age of the patients was 53.8 years, with roughly equal gender distributions. Twenty-eight percent of the events occurred in the absence of antihyperglycemic therapy. The incidence of hypoglycemia peaked between midnight and 6 AM. There were 36 instances of recurrent hypoglycemia associated with antihyperglycemic therapy, with 78% (n = 28) cases involving basal insulin. Patients on basal insulin who developed hypoglycemia did not have their dose changed prior to the time of the next administration in 75% of the cases.Conclusion: Hypoglycemia in hospitalized patients may occur with greater frequency overnight. Although cumbersome, routine nocturnal glycemic testing should be considered. Education regarding insulin management in the hospital and improved communication between night and day staff may aid in decreasing subsequent hypoglycemic events.Abbreviations: BG = blood glucose EHR = electronic health record ICU = intensive care unit IV = intravenous     

How Patients With Type 1 Diabetes Translate Continuous Glucose Monitoring Data into Diabetes Management Decisions

Objective: To understand how patients use continuous glucose monitoring (CGM) data in their diabetes management.Methods: We surveyed patients who regularly used CGM (>6 days per week), using 70 questions, many scenario-based. The survey had 6 sections: patient characteristics, general CGM use, hypoglycemia prevention and management, hyperglycemia prevention and management, insulin dosing adjustments (both for incidental hyperglycemia not at meals and at mealtimes), and real-time use versus retrospective analysis.Results: The survey was completed by 222 patients with type 1 diabetes. In response to a glucose of 220 mg/dL, the average correction dose adjustment based on rate of change arrows varied dramatically. Specifically, when the CGM device showed 2 arrows up (glucose increasing >3 mg/dL/minute), respondents stated they would increase their correction bolus, on average, by 140% (range, 0 to 600%). Conversely, 2 arrows down (glucose decreasing >3 mg/dL/minute) caused respondents to reduce their dose by 42%, with 24% omitting their dose entirely. Furthermore, 59% of respondents stated they would delay a meal in response to rapidly rising glucose, whereas 60% would wait until after a meal to bolus in response to falling glucose levels. With a glucose value of 120 mg/dL and a falling glucose trend, 70% of respondents would prophylactically consume carbohydrates to avoid hypoglycemia.Conclusion: CGM users utilize CGM data to alter multiple aspects of their diabetes care, including insulin dose timing, dose adjustments, and in hypoglycemia prevention. The insulin adjustments are much larger than common recommendations. Additional studies are needed to determine appropriate insulin adjustments based on glucose trend data.Abbreviations: A_1c = hemoglobin A_1c CGM = continuous glucose monitoring ROC = rate of change SMBG = self-monitored blood glucose     

Impact of Glucose Management Team on Outcomes of Hospitalization in Patients With Type 2 Diabetes Admitted to the Medical Service

Objective: To improve glycemic control of hospitalized patients with diabetes and hyperglycemia, many medical centers have established dedicated glucose management teams (GMTs). However, the impact of these specialized teams on clinical outcomes has not been evaluated.Methods: We conducted a retrospective study of 440 patients with type 2 diabetes admitted to the medical service for cardiac or infection-related diagnosis. The primary endpoint was a composite outcome of several well-recognized markers of morbidity, consisting of: death during hospitalization, transfer to intensive care unit, initiation of enteral or parenteral nutrition, line infection, new in-hospital infection or infection lasting more than 20 days of hospitalization, deep venous thrombosis or pulmonary embolism, rise in plasma creatinine, and hospital re-admissions.Results: Medical housestaff managed the glycemia in 79% of patients (usual care group), while the GMT managed the glycemia in 21% of patients (GMT group). The primary outcome was similar between cohorts (0.95 events per patient versus 0.99 events per patient in the GMT and usual care cohorts, respectively). For subanalysis, the subjects in both groups were stratified into those with average glycemia of <180 mg/dL versus those with glycemia >180 mg/dL. We found a significant beneficial impact of glycemic management by the GMT on the composite outcome in patients with average glycemia >180 mg/dL during their hospital stay. The number of patients who met primary outcome was significantly higher in the usual care group (40 of 83 patients, 48%) than in the GMT-treated cohort (8 of 33 patients, 25.7%) (P<.02).Conclusion: Our data suggest that GMTs may have an important role in managing difficult-to-control hyperglycemia in the inpatient setting.Abbreviations:BG = blood glucoseGMT = glucose management teamHbA1c = hemoglobin A1cICU = intensive care unitPOC = point of careT2D = type 2 diabetes     

The Added and Interpretative Value of CGM-Derived Parameters in Type 1 Diabetes Depends on the Level of Glycemic Control

ObjectiveIn type 1 diabetes mellitus (T1DM) management, continuous glucose monitoring (CGM)-derived parameters can provide additional insights, with time in range (TIR) and other parameters reflecting glycemic control and variability being put forward. This study aimed to examine the added and interpretative value of the CGM-derived indices TIR and coefficient of variation (CV%) in T1DM patients stratified according to their level of glycemic control by means of HbA1C.MethodsT1DM patients with a minimum disease duration of 10 years and without known macrovascular disease were enrolled. Patients were equipped with a blinded CGM device for 7 days. TIR and time spent in hypoglycemia and hyperglycemia were determined, and CV% was used as a parameter for glycemic variability. Pearson (r) and Spearman correlations (r_s) and a regression analysis were used to examine associations.ResultsNinety-five patients (age: 45 ± 10 years; HbA1C level: 7.7% ± 0.8% [61 ± 7 mmol/mol]) were included (mean blood glucose [MBG]: 159 ± 31 mg/dL; TIR: 55.8% ± 14.9%; CV%: 43.5% ± 7.8%) and labeled as having good (HbA1C level ≤7% [≤53 mmol/mol]; n = 20), moderate (7%-8%; n = 44), or poor (>8% [>64 mmol/mol]; n = 31) glycemic control. HbA1C was significantly associated with MBG (r_s = 0.48, P < .001) and time spent in hyperglycemia (total: r_s = 0.52; level 2: r = 0.46; P < .001) but not with time spent in hypoglycemia and CV%, even after an analysis of the HbA1C subgroups. Similarly, TIR was negatively associated with HbA1C (r = −0.53; P < .001), MBG (r_s = −0.81; P < .001), and time spent in hyperglycemia (total: r_s = −0.90; level 2: r_s = −0.84; P < .001) but not with time in hypoglycemia. The subgroup analyses, however, showed that TIR was associated with shorter time spent in level-2 hypoglycemia in patients with good (r_s = −0.60; P = .007) and moderate (r_s = −0.25; P = .047) glycemic control. In contrast, CV% was strongly positively associated with time in hypoglycemia (total: r_s = 0.78; level 2: r_s = 0.76; P < .001) but not with TIR or time in hyperglycemia in the entire cohort, although the subgroup analyses showed that TIR was negatively associated with CV% in patients with good glycemic control (r = −0.81, P < .001) and positively associated in patients with poor glycemic control (r = +0.47; P < .01).ConclusionThe CGM-derived metrics TIR and CV% are related to clinically important situations, TIR being strongly dependent on hyperglycemia and CV% being reflective of hypoglycemic risk. However, the interpretation and applicability of TIR and CV% and their relationship depends on the level of glycemic control of the individual patient, with CV% generally adding less clinically relevant information in those with poor control. This illustrates the need for further research and evaluation of composite measures of glycemic control in T1DM.     

Diabetes and Hyperglycemia Quality Improvement Efforts in Hospitals in the United States: Current Status,Practice Variation,and Barriers to Implementation

ObjectiveTo determine the status of diabetes and hyperglycemia quality improvement efforts in hospitals in the United States.MethodsWe designed and administered a survey to a convenience sample of hospitals, and the responses were analyzed statistically.ResultsWe received 269 responses from 1,151 requested surveys. The sample was similar to hospitals in the United States on the basis of hospital type and geographic region (P = no significant difference) but not on the basis of number of beds (P < .001). Among responding hospitals, 39%, 21%, and 15% had fully implemented inpatient diabetes and hyperglycemia quality improvement programs for critically ill, non-critically ill, and perioperative patients, respectively. Moreover, 77%, 44%, and 49% had fully implemented protocols for hypoglycemia, hyperglycemic crises, and diabetic ketoacidosis, respectively. Variations in glucose target ranges were noted. The responding hospitals had no standard biochemical definition of hypoglycemia; 47% defined hypoglycemia as a glucose level ≤ 70 mg/dL, but 29%, 8%, 6%, and 4% used < 60, ≤ce:hsp sp="0.10"/>50, < 40, and < 80 mg/dL, respectively. Almost a third of reporting hospitals had no metric to track the quality of inpatient diabetes and hyperglycemia care. More than half (59%) indicated that they did not have an automated capability to extract and analyze glucose data. The most frequent barrier to implementing a glycemic control program was concern regarding hypoglycemia (61%).ConclusionHospitals are addressing the issue of inpatient diabetes and glycemic control but face obstacles to implementation of quality improvement programs and vary in their approach to management. Improving the consistency of glucose control practices within hospitals in the United States should help enhance patient care and safety. Future efforts to help hospitals overcome barriers to introducing glucose control programs could include developing standardized glycemic control metrics, improving data collection and reporting methods, and providing improved tools that enable clinicians to control glucose safely. (Endocr Pract. 2010;16:219-230)