An algorithm to identify incident myocardial infarction using Medicaid data

Authors

  • Neesha N. Choma MD, MPH,

    1. Veterans Administration, Tennessee Valley Healthcare System, Tennessee Valley Geriatric Research Education Clinical Center (GRECC), TN, USA
    2. HSR&D Targeted Research Enhancement Program Center for Patient Healthcare Behavior, TN, USA
    3. VA National Quality Scholars Fellowship Program, Nashville, TN, USA
    4. Department of Medicine, Vanderbilt University, Nashville , TN, USA
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  • Marie R. Griffin MD, MPH,

    1. Veterans Administration, Tennessee Valley Healthcare System, Tennessee Valley Geriatric Research Education Clinical Center (GRECC), TN, USA
    2. HSR&D Targeted Research Enhancement Program Center for Patient Healthcare Behavior, TN, USA
    3. Tennessee Valley VA Clinical Research Center of Excellence (CRCoE), TN, USA
    4. Department of Medicine, Vanderbilt University, Nashville , TN, USA
    5. Department of Preventive Medicine (Pharmacoepidemiology) and Center for Education and Research on Therapeutics Vanderbilt University, Nashville , TN, USA
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  • Robert L. Huang MD, MPH,

    1. Division of Cardiology, Vanderbilt University, Nashville,TN, USA
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  • Edward F. Mitchel Jr. MS,

    1. Department of Preventive Medicine (Pharmacoepidemiology) and Center for Education and Research on Therapeutics Vanderbilt University, Nashville , TN, USA
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  • Lisa A. Kaltenbach MS,

    1. Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
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  • Patricia Gideon RN,

    1. Department of Preventive Medicine (Pharmacoepidemiology) and Center for Education and Research on Therapeutics Vanderbilt University, Nashville , TN, USA
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  • Shannon M. Stratton BS,

    1. Department of Preventive Medicine (Pharmacoepidemiology) and Center for Education and Research on Therapeutics Vanderbilt University, Nashville , TN, USA
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  • Christianne L. Roumie MD, MPH

    Corresponding author
    1. Veterans Administration, Tennessee Valley Healthcare System, Tennessee Valley Geriatric Research Education Clinical Center (GRECC), TN, USA
    2. HSR&D Targeted Research Enhancement Program Center for Patient Healthcare Behavior, TN, USA
    3. VA National Quality Scholars Fellowship Program, Nashville, TN, USA
    4. Tennessee Valley VA Clinical Research Center of Excellence (CRCoE), TN, USA
    5. Department of Medicine, Vanderbilt University, Nashville , TN, USA
    • Nashville VA Medical Center, 1310 24th Ave South GRECC, Nashville, TN, 37212.
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  • This was an investigator initiated (Dr Griffin PI) study funded by Pfizer who had no role in the conduct of the study, collection of data, data management, or analysis. Pfizer scientists reviewed and commented on the overall protocol, but had no role in this manuscript. Dr Griffin reports receiving consulting fees from Merck Inc. Drs Choma, Griffin, and Roumie had full access to the data in the study and take responsibility for the integrity and the analysis of data.

Abstract

Purpose

Studies of non-steroidal anti-inflammatory drugs (NSAIDs) and cardiovascular events using administrative data require identification of incident acute myocardial infarctions (AMIs) and information on whether confounders differ by NSAID status.

Methods

We identified patients with a first AMI hospitalization from Tennessee Medicaid files as those with primary ICD-9 discharge diagnosis 410.x and hospitalization stay of > 2 calendar days. Eligible persons were non-institutionalized, aged 50–84 years between 1999–2004, had continuous enrollment and no AMI, stroke, or non-cardiovascular serious medical illness in the prior year. Of 5524 patients with a potential first AMI, a systematic sample (n = 350) was selected for review. Using defined criteria, we classified events using chest pain history, EKG, and cardiac enzymes, and calculated the positive predictive value (PPV) for definite or probable AMI.

Results

337 of 350 (96.3%) charts were abstracted and 307 (91.1%), 6 (1.8%), and 24 (7.1%) events were categorized as definite, probable, and no AMI, respectively. PPV for any definite or probable AMI was 92.8% (95% CI 89.6–95.2); for an AMI without an event in the past year 91.7% (95% CI 88.3–94.2), and for an incident AMI was 72.7% (95% CI 67.7–77.2). Age-adjusted prevalence of current smoking (46.4% vs. 39.1%, p = 0.35) and aspirin use (36.9% vs. 35.9%, p = 0.90) was similar among NSAID users and non-users

Conclusions

ICD-9 code 410.x had high predictive value for identifying AMI. Among those with AMI, smoking and aspirin use was similar in NSAID exposure groups, suggesting these factors will not confound the relationship between NSAIDs and cardiovascular outcomes. Copyright © 2009 John Wiley & Sons, Ltd.

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