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

Abstract

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.