title: Development of a multi-institutional cohort to facilitate cardiovascular disease biomarker validation using existing biorepository samples linked to electronic health records.
abstract: BACKGROUND
  Emerging biomarkers for acute myocardial infarction (AMI) may enhance conventional risk-prediction algorithms if they are informative and associated with risk independently of established predictors. In this study, we constructed a cohort for testing emerging biomarkers for AMI in managed-care populations using existing biospecimen repositories linked to electronic health records (EHR).
  
  
  OBJECTIVE
  Electronic health record-based biorepositories collected by healthcare systems can be federated to provide large, methodologically sound testing sets for biomarker validation.
  
  
  METHODS
  Subjects ages 40 to 80 years were selected from 2 existing population-based biospecimen repositories. Incident AMI status and covariates were ascertained from the EHR. An ad hoc model for AMI risk was parameterized and validated. Simulation was used to test incremental gains in performance due to the inclusion of biomarkers in this model. Gains in performance were assessed in terms of area under the receiver operating characteristic curve (ROC-AUC) and case reclassification.
  
  
  RESULTS
  A total of 18 329 individuals (57% female) contributed 108 400 person-years of EHR follow-up. The crude AMI incidence was 10.8 and 5.0 per 1000 person-years among males and females, respectively. Compared with the model with risk factors alone, inclusion of a simulated biomarker yielded substantial gains in sensitivity without loss of specificity. Furthermore, a net ROC-AUC gain of 13.3% was observed, as well as correct reclassification of 9.8% of incident cases (79 of 806) that were otherwise not considered statin-indicated at baseline under the National Cholesterol Education Program Adult Treatment Panel III criteria.
  
  
  CONCLUSIONS
  More research is needed to assess incremental contribution of emerging biomarkers for AMI prediction in managed-care populations.
authors.affiliation: Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin.
authors.forename: Deanna S
authors.initials: DS
authors.lastname: Cross
authors.forename: Catherine A
authors.initials: CA
authors.lastname: McCarty
authors.forename: Steven R
authors.initials: SR
authors.lastname: Steinhubl
authors.forename: David J
authors.initials: DJ
authors.lastname: Carey
authors.forename: Porat M
authors.initials: PM
authors.lastname: Erlich
mesh_terms: D019540:Area Under Curve; D018070:Biological Specimen Banks; D015415:Biomarkers / Q000378:metabolism; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000453:epidemiology / Q000378:metabolism; D003198:Computer Simulation; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008498:Medical Record Linkage; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012307:Risk Factors; D013997:Time Factors; D014481:United States / Q000453:epidemiology
chemical_list: D015415:Biomarkers