Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records

Sulaiman Somani, Stephen Yoffie, Shelly Teng, Shreyas Havaldar, Girish N. Nadkarni, Shan Zhao, Benjamin S. Glicksberg

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Objectives: Classifying hospital admissions into various acute myocardial infarction phenotypes in electronic health records (EHRs) is a challenging task with strong research implications that remains unsolved. To our knowledge, this study is the first study to design and validate phenotyping algorithms using cardiac catheterizations to identify not only patients with a ST-elevation myocardial infarction (STEMI), but the specific encounter when it occurred. Materials and Methods: We design and validate multi-modal algorithms to phenotype STEMI on a multicenter EHR containing 5.1 million patients and 115 million patient encounters by using discharge summaries, diagnosis codes, electrocardiography readings, and the presence of cardiac catheterizations on the encounter. Results: We demonstrate that robustly phenotyping STEMIs by selecting discharge summaries containing "STEM"has the potential to capture the most number of STEMIs (positive predictive value [PPV] = 0.36, N = 2110), but that addition of a STEMI-related International Classification of Disease (ICD) code and cardiac catheterizations to these summaries yields the highest precision (PPV = 0.94, N = 952). Discussion and Conclusion: In this study, we demonstrate that the incorporation of percutaneous coronary intervention increases the PPV for detecting STEMI-related patient encounters from the EHR.

Original languageEnglish
Article numberooab068
JournalJAMIA Open
Volume4
Issue number3
DOIs
StatePublished - 1 Jul 2021

Keywords

  • big data
  • cardiology
  • electronic health records
  • myocardial infarction
  • phenotyping

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