Derivation and Validation of D-RISK: An Electronic Health Record–Driven Risk Score to Detect Undiagnosed Dysglycemia in Clinical Practice

Abstract

OBJECTIVE We derive and validate D-RISK, an electronic health record (EHR)-driven risk score to optimize and facilitate screening for undiagnosed dysglycemia (predia-betes plus diabetes) in clinical practice. RESEARCH DESIGN AND METHODS We used retrospective EHR data (derivation sample) and a prospective diabetes screening study (validation sample) to develop D-RISK. Logistic regression with backward selection was used to predict dysglycemia (HbA1c $5.7%) using diabetes risk factors consistently captured in structured EHR data. Model coefficients were converted to a points-based risk score. We report discrimination, sensitivity, and specificity and compare D-RISK to the American Diabetes Association (ADA) risk test and the ADA and United States Preventive Services Task Force (USPSTF) screening guidelines. RESULTS The derivation cohort included 11,387 patients (mean age 48 years; 65% female; 42% Hispanic; 32% non-Hispanic Black; mean BMI 32; 29% with hypertension). D-RISK included age, race, BMI, hypertension, and random glucose. The area under curve (AUC) for the risk score was 0.75 (95% CI 0.74–0.76). In the validation screening study (n = 519), the AUC was 0.71 (95% CI 0.66–0.75) which was better than the ADA and USPSTF diabetes screening guidelines (AUC = 0.52 and AUC = 0.58, respectively; P < 0.001 for both). Discrimination was similar to the ADA risk test (AUC = 0.67) using patient-reported data to supplement EHR data, although D-RISK was more sensitive (75% vs. 61%) at the recommended screening thresholds. CONCLUSIONS Designed for use in EHR, D-RISK performs better than commonly used screening guidelines and risk scores and may help detect undiagnosed cases of dysglycemia in clinical practice.