Optimal strategies for identifying patients with myocardial infarction in general practice

Peter T. Donnan (Lead / Corresponding author), Hamish T. Dougall, Frank M. Sullivan

    Research output: Contribution to journalArticlepeer-review

    11 Citations (Scopus)

    Abstract

    Background. In order to provide evidence-based secondary prevention of coronary heart disease (CHD) in general practice, eligible patients need to be identified. The optimal strategy is one in which all appropriate patients are identified with the least effort. Objective. The purpose of the study was to determine the optimal strategy to identify subjects with a myocardial infarction (MI) from general practice records using different search criteria. Methods. The study was a cross-sectional survey of 10 general practices in Tayside, Scotland. A random sample of all subjects aged over 35 (n = 5061) and registered with the general practices was obtained. The main outcome measures were sensitivity, specificity, positive predictive value (PPV) and yield (the number of records that need to be examined to detect a 'true case'). Results. Of the sample of 5061, 207 (4.1%) were defined to have had a 'gold standard' MI. A Read code for ischaemic heart disease (IHD) had the highest sensitivity (95%) but with a poor PPV (52%). All searches had high specificities. The addition of a record of hospitalization for MI to the Read code for MI gave 100% sensitivity and high yield (1 in 1.11). In situations where the Read coding is of poor quality, the alternative search strategy of a hospital record of MI or receiving aspirin or nitrates was optimum. Conclusions. Patients who had experienced an MI can be easily identified from a combination of a Read code for MI and a record of hospitalization for an MI giving 100% sensitivity and specificity with a yield of 1 in 1.11.

    Original languageEnglish
    Pages (from-to)706-710
    Number of pages5
    JournalFamily Practice
    Volume20
    Issue number6
    DOIs
    Publication statusPublished - Dec 2003

    Keywords

    • Coronary heart disease
    • Myocardial infarction
    • Read coding
    • Secondary prevention

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