Development and Validation of HealthImpact: An Incident Diabetes Prediction Model Based on Administrative Data

Objective To develop and validate a model of incident type 2 diabetes based solely on administrative data. Data Sources/Study Setting Optum Labs Data Warehouse (OLDW), a national commercial administrative dataset. Study Design HealthImpact model was developed and internally validated using nested case–control study design; n = 473,049 in training cohort and n = 303,025 in internal validation cohort. HealthImpact was externally validated in 2,000,000 adults followed prospectively for 3 years. Only adults ≥18 years were included. Data Collection/Extraction Methods Patients with incident diabetes were identified using HEDIS rules. Control subjects were sampled from patients without diabetes. Medical and pharmacy claims data collected over 3 years prior to index date were used to build the model variables. Principal Findings HealthImpact, scored 0–100, has 48 variables with c‐statistic 0.80815. We identified HealthImpact threshold of 90 as identifying patients at high risk of incident diabetes. HealthImpact had excellent discrimination in external validation cohort (c‐statistic 0.8171). The sensitivity, specificity, positive predictive value, and negative predictive value of HealthImpact >90 for new diagnosis of diabetes within 3 years were 32.35, 94.92, 22.25, and 96.90 percent, respectively. Conclusions HealthImpact is an efficient and effective method of risk stratification for incident diabetes that is not predicated on patient‐provided information or laboratory tests.