Development of a Multi-institutional Cohort to Facilitate Cardiovascular Disease Biomarker Validation Using Existing Biorepository Samples Linked to Electronic Health Records

Authors


  • This study was conducted within the Cardiovascular Research Network, a consortium of research organizations affiliated with the HMO Research Network and sponsored by the National Heart, Lung, and Blood Institute (U19 HL91179-01). The source biorepository at Marshfield Clinic was supported by the Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS), grant UL1TR000427.

  • The authors have no other funding, financial relationships, or conflicts of interest to disclose.

Address for correspondence:

Porat M. Erlich, PhD, MPH

Center for Health Research Geisinger Health System

100 N. Academy Avenue

Danville, PA 17822

pmerlich@geisinger.edu

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).

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.

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