Inpatient Glucose Values: Determining the Nondiabetic Range and Use in Identifying Patients at High Risk for Diabetes.

BACKGROUND: Many individuals with diabetes remain undiagnosed, leading to delays in treatment and higher risk for subsequent diabetes complications. Despite recommendations for diabetes screening in high-risk groups, the optimal approach is not known. We evaluated the utility of inpatient glucose levels as an opportunistic screening tool for identifying patients at high risk for diabetes. METHODS: We retrospectively examined 462,421 patients in the US Department of Veterans Affairs healthcare system, hospitalized on medical/surgical services in 2000-2010, for ≥3 days, with ≥2 inpatient random plasma glucose (RPG) measurements. All had continuity of care: ≥1 primary care visit and ≥1 glucose measurement within 2 years before hospitalization and yearly for ≥3 years after discharge. Glucose levels during hospitalization and incidence of diabetes within 3 years after discharge in patients without diabetes were evaluated. RESULTS: Patients had a mean age of 65.0 years, body mass index of 29.9 kg/m2, and were 96% male, 71% white, and 18% black. Pre-existing diabetes was present in 39.4%, 1.3% were diagnosed during hospitalization, 8.1% were diagnosed 5 years after discharge, and 51.3% were never diagnosed (NonDM). The NonDM group had the lowest mean hospital RPG value (112 mg/dL [6.2 mmol/L]). Having at least 2 RPG values >140 mg/dL (>7.8 mmol/L), the 95th percentile of NonDM hospital glucose, provided 81% specificity for identifying incident diabetes within 3 years after discharge. CONCLUSIONS: Screening for diabetes could be considered in patients with at least 2 hospital glucose values at/above the 95th percentile of the nondiabetic range (141 mg/dL [7.8 mmol/L]).

Reduced Cardiovascular Disease Incidence With a National Lifestyle Change Program.

INTRODUCTION: Lifestyle change programs implemented within healthcare systems could reach many Americans, but their impact on cardiovascular disease (CVD) remains unclear. The MOVE! program is the largest lifestyle change program implemented in a healthcare setting in the U.S. This study aimed to determine whether MOVE! participation was associated with reduced CVD incidence. METHODS: This retrospective cohort study, analyzed in 2013-2015, used national Veterans Health Administration databases to identify MOVE! participants and eligible non-participants for comparison (2005-2012). Patients eligible for MOVE!-obese or overweight with a weight-related health condition, and no baseline CVD-were examined (N=1,463,003). Of these, 169,248 (12%) were MOVE! PARTICIPANTS: Patients were 92% male, 76% white, with mean age 52 years and BMI of 32. The main outcome was incidence of CVD (ICD-9 and procedure codes for coronary artery disease, cerebrovascular disease, peripheral vascular disease, and heart failure). RESULTS: Adjusting for age, race, sex, BMI, statin use, and baseline comorbidities, over a mean 4.9 years of follow-up, MOVE! participation was associated with lower incidence of total CVD (hazard ratio [HR]=0.83, 95% CI=0.80, 0.86); coronary artery disease (HR=0.81, 95% CI=0.77, 0.86); cerebrovascular disease (HR=0.87, 95% CI=0.82, 0.92); peripheral vascular disease (HR=0.89, 95% CI=0.83, 0.94); and heart failure (HR=0.78, 95% CI=0.74, 0.83). The association between MOVE! participation and CVD incidence remained significant when examined across categories of race/ethnicity, BMI, diabetes, hypertension, smoking status, and statin use. CONCLUSIONS: Although participation was limited, MOVE! was associated with reduced CVD incidence in a nationwide healthcare setting.

Translating What Works: A New Approach to Improve Diabetes Management.

BACKGROUND: The most efficacious strategies to improve diabetes control include case management, health care team changes, patient education, and facilitated transmission of patient data to clinicians ("facilitated relay"), but these strategies have not been translated to permit general use in clinical practice. METHODS: A web-based decision support program was developed to include these features, and assessed in patients who had A1c ≥7.0% despite using metformin with/without sulfonylureas or insulin. Staff entered patients' glucose data, obtained management recommendations, reviewed the plan with a clinician, and discussed the new plan with patients. RESULTS: 113 subjects were 96% male and 32% black, with average age 65.6 years and BMI 32.8. During prior primary care, A1c averaged 8.32 ± 0.16% (SEM). In all patients, baseline A1c was 8.18 ± 0.11%, and decreased to 7.54 ± 0.12%, 7.16 ± 0.13%, and 7.54 ± 0.16% at 3, 6, and 12 months, respectively, all P < .001. In 42 subjects who provided glucose data and made requested changes in medications, A1c was 8.12 ± 0.09% at baseline and fell to 7.29 ± 0.11%, 6.98 ± 0.10%, and 7.05 ± 0.10% at 3, 6, and 12 months, respectively, all P < .001. Chart review of 16 subjects followed for 12 months demonstrated that hypoglycemia (symptoms and/or glucose <70 mg/dl) averaged less than 1 episode/patient/month, and there was no severe hypoglycemia. CONCLUSIONS: A novel decision support program improved A1c with little hypoglycemia. Use of this approach should allow primary care teams to keep patients well controlled, and reduce the need for specialist referrals.