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OBJECTIVE: To determine the utility of International Classification of Diseases (ICD) codes in investigating trends in ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) using person-linked electronic hospitalisation data in England and Western Australia (WA). METHODS: All hospital admissions with myocardial infarction (MI) as the principal diagnosis were identified from 2000 to 2013 from both jurisdictions. Fourth-digit ICD-10 codes were used to delineate all MI types-STEMI, NSTEMI, unspecified and subsequent MI. The annual frequency of each MI type was calculated as a proportion of all MI admissions. For all MI and each MI type, age-standardised rates were calculated and age-adjusted Poisson regression models used to estimate annual percentage changes in rates. RESULTS: In 2000, STEMI accounted for 49% of all MI admissions in England and 59% in WA, decreasing to 35% and 25% respectively by 2013. Less than 10% of admissions were recorded as NSTEMI in England throughout the study period, whereas by 2013, 70% of admissions were NSTEMI in WA. Unspecified MI comprised 60% of all MI admissions in England by 2013, compared with <1% in WA. Trends in age-standardised rates differed for all MI (England, -2.7%/year; WA, +1.7%/year), underpinned by differing age-adjusted trends in NSTEMI (England, -6.1%/year; WA, +10.2%/year). CONCLUSION: Differences between the proportion and trends for MI types in English and WA data were observed. These were consistent with the coding standards in each country. This has important implications for using electronic hospital data for monitoring MI and identifying MI types for outcome studies.

Original publication

DOI

10.1136/bmjopen-2017-019217

Type

Journal article

Journal

BMJ Open

Publication Date

12/11/2017

Volume

7

Keywords

cardiac epidemiology, myocardial infarction, Adult, Age Distribution, Aged, Aged, 80 and over, England, Female, Hospitalization, Humans, Information Storage and Retrieval, Male, Middle Aged, Myocardial Infarction, Regression Analysis, Risk Factors, Sex Distribution, Time Factors, Western Australia