Open AccessDevelopment of a Multi‐institutional Cohort to Facilitate Cardiovascular Disease Biomarker Validation Using Existing Biorepository Samples Linked to Electronic Health Records
Author(s) -
Cross Deanna S.,
McCarty Catherine A.,
Steinhubl Steven R.,
Carey David J.,
Erlich Porat M.
Publication year - 2013
Publication title -
clinical cardiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.263
H-Index - 72
eISSN - 1932-8737
pISSN - 0160-9289
DOI - 10.1002/clc.22146
Subject(s) - medicine , biomarker , receiver operating characteristic , cohort , electronic health record , biorepository , population , myocardial infarction , cohort study , health care , gerontology , bioinformatics , environmental health , biobank , biochemistry , chemistry , economics , biology , economic growth
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 ). Hypothesis 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.