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An Algorithm That Identifies Coronary and Heart Failure Events in the Electronic Health Record
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Details:
  • Pubmed ID:
    23449283
  • Pubmed Central ID:
    PMC3592787
  • Description:
    Introduction

    The advent of universal health care coverage in the United States and the use of electronic health records can make the medical record a disease surveillance tool. The objective of our study was to identify criteria that accurately categorize acute coronary and heart failure events by using electronic health record data exclusively so that the medical record can be used for surveillance without manual record review.

    Methods

    We serially compared 3 computer algorithms to manual record review. The first 2 algorithms relied on ICD-9-CM (International Classification of Diseases, 9th Revision, Clinical Modification) codes, troponin levels, electrocardiogram (ECG) data, and echocardiograph data. The third algorithm relied on a detailed coding system, Intelligent Medical Objects, Inc., (IMO) interface terminology, troponin levels, and echocardiograph data.

    Results

    Cohen’s κ for the initial algorithm was 0.47 (95% confidence interval [CI], 0.41–0.54). Cohen’s κ was 0.61 (95% CI, 0.55–0.68) for the second algorithm. Cohen’s κ for the third algorithm was 0.99 (95% CI, 0.98–1.00).

    Conclusion

    Electronic medical record data are sufficient to categorize coronary heart disease and heart failure events without manual record review. However, only moderate agreement with medical record review can be achieved when the classification is based on 4-digit ICD-9-CM codes because ICD-9-CM 410.9 includes myocardial infarction with elevation of the ST segment on ECG (STEMI) and myocardial infarction without elevation of the ST segment on ECG (nSTEMI). Nearly perfect agreement can be achieved using IMO interface terminology, a more detailed coding system that tracks to ICD9, ICD10 (International Classification of Diseases, Tenth Revision, Clinical Modification), and SnoMED-CT (Systematized Nomenclature of Medicine – Clinical Terms).

  • Document Type:
  • Funding:
    5U50DP000721-04/DP/NCCDPHP CDC HHS/United States
    T32 HL69764/HL/NHLBI NIH HHS/United States
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