Evidence map›Paper›PMID 29133337›Full record

ArticleBMJ open2017

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

Lee Nedkoff, Derrick Lopez, Michael Goldacre, Frank Sanfilippo, Michael Hobbs, F Lucy Wright

Abstract readComparative Study
In one paragraph

Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Initial treatment of uninsured patients with ST-elevation myocardial infarction by facility percutaneous coronary intervention capabilities.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024
    Article
  3. Observational
  4. Article
  5. Evidence-practice gaps in P2YInternal medicine journal · 2022
    Article
  6. Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Lee NedkoffSchool of Population and Global Health, The University of Western Australia, Perth, Australia.
Derrick LopezSchool of Population and Global Health, The University of Western Australia, Perth, Australia.
Michael GoldacreUnit of Health-Care Epidemiology, Nuffield Department of Public Health, University of Oxford, Oxford, UK.
Frank SanfilippoSchool of Population and Global Health, The University of Western Australia, Perth, Australia.ORCID http://orcid.org/0000-0003-3639-0787
Michael HobbsSchool of Population and Global Health, The University of Western Australia, Perth, Australia.
F Lucy WrightCancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo 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).

methodsAll 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.

resultsIn 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).

conclusionDifferences 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.

Indexed as

AdultAgedAged, 80 and overAge DistributionEnglandFemaleHospitalizationHumansInformation Storage and RetrievalMaleMiddle AgedMyocardial InfarctionRegression AnalysisRisk FactorsSex DistributionTime Factorscardiac epidemiologymyocardial infarction

Identifiers

PMID29133337
PMCPMC5695341

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.