title: 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.
  
  
  METHODS
  Optum Labs Data Warehouse (OLDW), a national commercial administrative dataset.
  
  
  METHODS
  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.
  
  
  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.
  
  
  RESULTS
  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.
authors.affiliation: Division of Primary Care Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN. mccoy.rozalina@mayo.edu.|Department of Health Sciences Research, Mayo Clinic, Rochester, MN. mccoy.rozalina@mayo.edu.
authors.forename: Rozalina G
authors.initials: RG
authors.lastname: McCoy
authors.affiliation: OptumLabs, Cambridge, MA.
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Nori
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN.|Division of Endocrinology Diabetes Metabolism & Nutrition, Department of Medicine, Mayo Clinic, Rochester, MN.
authors.forename: Steven A
authors.initials: SA
authors.lastname: Smith
authors.affiliation: OptumLabs, Cambridge, MA.
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Hane
mesh_terms: D000067575:Administrative Claims, Healthcare / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016208:Databases, Factual / Q000706:statistics & numerical data; D003661:Decision Support Techniques; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D015203:Reproducibility of Results; D018570:Risk Assessment
keywords: Diabetes mellitus type 2; decision support techniques; risk assessment/methods; theoretical models