Dataset Information

Identification of myocardial infarction type from electronic hospital data in England and Australia: a comparative data linkage study.

ABSTRACT:

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.

SUBMITTER: Nedkoff L

PROVIDER: S-EPMC5695341 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Publications

Identification of myocardial infarction type from electronic hospital data in England and Australia: a comparative data linkage study.

Nedkoff Lee L Lopez Derrick D Goldacre Michael M Sanfilippo Frank F Hobbs Michael M Wright F Lucy FL

BMJ open 20171112 11

<h4>Objective</h4>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).<h4>Methods</h4>All hospital admissions with myocardial infarction (MI) as the principal diagnosis were identified from 2000 to 2013 from both jurisdictions. Fourth-digit IC ...[more]

PMID: 29133337

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Project description:BackgroundA wide range of outcome measures can be calculated for hospital-treated injury patients. These include mortality, use of critical care services, complications, length of stay, treatment costs, readmission and nursing care after discharge. Each address different aspects and phases of injury recovery and can yield vastly different results. This study aims to: (1) measure and report this range of outcomes in hospital-treated injury patients in a defined population; and (2) describe the associations between injury characteristics, socio-demographics and comorbidities and the various outcomes.MethodsA retrospective analysis was conducted of injury-related hospital admissions from July 2012 to June 2014 (152,835 patients) in Victoria, Australia. The admission records were linked within the dataset, enabling follow-up, to assess the outcomes of in-hospital death, burden, complications and 30-day readmissions. Associations between factors and outcomes were determined using univariate regression analysis.ResultsThe proportion of patients who died in hospital was 0.9%, while 26.8% needed post-discharge care. On average patients had 2.4 complications (confidence interval (CI) 2.4-2.5) related to their initial injury, the mean cost of treating a patient was Australian dollars 7013 (CI 6929-7096) and the median length of stay was one day (inter quartile range 1-3). Intensive-care-unit-stay was recorded in 3% of the patients. All-cause 30-day readmissions occurred in 12.3%, non-planned 30-day readmissions in 7.9%, while potentially avoidable 30-day readmissions were observed in 3.2% of the patients. Increasing age was associated with all outcomes. The need for care post-discharge from hospital was highest among children and the oldest age group (85 years and over). Injury severity was associated with all adverse outcomes. Increasing number of comorbidities increased the likelihood of all outcomes. Overall, outcomes are shown to differ by age, gender, comorbidities, body region injured, injury type and injury severity, and to a lesser extent by socio-economic areas.ConclusionsOutcomes and risk factors differ depending on the outcome measured, and the method used for measuring the outcome. Similar outcomes measured in different ways produces varying results. Data linkage has provided a valuable platform for a comprehensive overview of outcomes, which can help design and target secondary and tertiary preventive measures.

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Dataset Information

Identification of myocardial infarction type from electronic hospital data in England and Australia: a comparative data linkage study.

Objective

Methods

Results

Conclusion

Publications

Identification of myocardial infarction type from electronic hospital data in England and Australia: a comparative data linkage study.

Similar Datasets

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