Completeness In The Abstraction Of Cardiac Biomarkers And Cardiac Pain Data From Electronic Health Records (Ehr). Findings From The Atherosclerosis Risk In Communities (Aric) Study

Background: Calibration of case-finding algorithms from electronic health records (EHR) against established disease surveillance protocols is key to avoiding misclassification bias when using EHR data in epidemiological research. We examined the agreement in the classification of troponin I levels and identification of cardiac pain in hospital EHR data against manually abstracted charts for hospitalizations observed by the ARIC community surveillance of cardiovascular events. Methods: A structured data request for laboratory data and provider notes was submitted to hospitals in the ARIC community surveillance program. Computer programs were developed to extract dates of service, type of laboratory assays performed, and individual assay values for days 1-4 of each hospitalization. Presence of cardiac pain was extracted from provider notes using natural language processing protocols. We calculated percent agreement for troponin I values, kappa statistics for their classification as abnormal (values ≥ twice ...