1Univ North Carolina, Chapel Hill, NC2Wake Forest Univ, Winston-Salem, NC
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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 upper limit normal (ULN)), equivocal (values ≥ULN, but < twice ULN) normal (Results: Of the 126 EHRs, 104 were eligible for abstraction of cardiac biomarkers and pain information. Agreement in the troponin I values was 75.5% (95%CI: 65.8%, 83.6%) for day 1 of the hospitalization, decreasing thereafter to 62.5% (95%CI: 24.5%, 91.5%) for Day 4. The kappa coefficient for the classification of troponin I values was 0.96 (95% CI: 0.90, 1.00), We observed a high sensitivity in the abstraction of information on cardiac pain (0.99 (95%CI: 0.94, 1.0)). The specificity of cardiac pain information was 0.24 (95% CI: 0.16, 0.35) when extracted from all note types, increasing to 0.90 (95%CI: 0.75, 0.97) if extracted from discharge notes.Conclusion: Troponin I values and manifestation of ischemia such as cardiac pain are critical to the classification of acute coronary events. Therefore, the observed excellent agreement with the gold standard ARIC abstraction shows promise for the use of EHRs in the surveillance of acute cardiovascular disease.