Derivation and Validation of D-RISK: An Electronic Health Record-Driven Risk Score to Detect Undiagnosed Dysglycemia in Clinical Practice
Objective: We derive and validate D-RISK, an EHR-driven risk score to optimize and facilitate screening for undiagnosed dysglycemia (prediabetes + diabetes) in clinical practice. Research Design and Methods: We utilized retrospective EHR data (derivation sample) and a prospective diabetes screening study (validation sample) to develop D-RISK. Logistic regression with backwards 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 US Preventive Services Task Force (USPSTF) screening guidelines. Results: The derivation cohort included 11,387 patients (mean age 48; 65% female; 42% Hispanic; 32% NH-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 the EHR, D-RISK performs better than commonly used screening guidelines and risk scores and may help detect undiagnosed cases of dysglycemia in clinical practice.