Use of troponins in the classification of myocardial infarction from electronic health records. The Atherosclerosis Risk in Communities (ARIC) Study

Anna M. Kucharska-Newton, Matthew Shane Loop, Manuela Bullo, Carlton Moore, Stephanie W. Haas, Lynne Wagenknecht, Eric A. Whitsel, Gerardo Heiss

Research output: Contribution to journalArticlepeer-review


Objective: Electronic health record (EHR) data are underutilized for abstracting classification criteria for heart disease. We compared extraction of EHR data on troponin I and T levels with human abstraction. Methods: Using EHR for hospitalizations identified through the Atherosclerosis Risk in Communities (ARIC) Study in four US hospitals, we compared blood levels of troponins I and T extracted from EHR structured data elements with levels obtained through data abstraction by human abstractors to 3 decimal places. Observations were divided randomly 50/50 into training and validation sets. Bayesian multilevel logistic regression models were used to estimate agreement by hospital in first and maximum troponin levels, troponin assessment date, troponin upper limit of normal (ULN), and classification of troponin levels as normal (< ULN), equivocal (1-2× ULN), abnormal (>2× ULN), or missing. Results: Estimated overall agreement in first measured troponin level in the validation data was 88.2% (95% credible interval: 65.0%-97.5%) and 95.5% (91.2-98.2%) for the maximum troponin level observed during hospitalization. The largest variation in probability of agreement was for first troponin measured, which ranged from 66.4% to 95.8% among hospitals. Conclusion: Extraction of maximum troponin values during a hospitalization from EHR structured data is feasible and accurate.

Original languageEnglish
Pages (from-to)152-156
Number of pages5
JournalInternational Journal of Cardiology
StatePublished - Feb 1 2022

Bibliographical note

Funding Information:
The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services , under Contract nos. ( HHSN268201700001I , HHSN268201700002I , HHSN268201700003I , HHSN268201700005I , HHSN268201700004I ). The authors thank the staff and participants of the ARIC study for their important contributions.

Publisher Copyright:
© 2021 Elsevier B.V.


  • Algorithmic abstraction
  • Electronic health records
  • Myocardial infarction
  • Troponin

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine


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