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 (prediabetes 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.