Temporal trends in concordance between ICD-coded and cardiac biomarker-classified hospitalisation rates for acute coronary syndromes: a linked hospital and biomarker data study.


Journal

Open heart
ISSN: 2053-3624
Titre abrégé: Open Heart
Pays: England
ID NLM: 101631219

Informations de publication

Date de publication:
23 Oct 2024
Historique:
received: 02 10 2024
accepted: 06 10 2024
medline: 25 10 2024
pubmed: 25 10 2024
entrez: 24 10 2024
Statut: epublish

Résumé

Since 2000, the definition of myocardial infarction (MI) has evolved with reliance on cardiac troponin (cTn) tests. The implications of this change on trends of acute coronary syndrome (ACS) subtypes obtained from routinely collected hospital morbidity data are unclear. Using person-linked hospitalisation data, we compared International Classification of Diseases (ICD)-coded data with biomarker-classified admission rates for ST-segment elevation MI (STEMI), non-STEMI (NSTEMI) and unstable angina (UA) in Western Australia (WA). We used linked hospitalisation data from all WA tertiary hospitals to identify patients with a principal diagnosis of STEMI, NSTEMI or UA between 2002 and 2016. Linked biomarker results were classified as 'diagnostic' for MI according to established criteria. We calculated age-standardised and sex-standardised rates (ASSRs) for ICD-coded versus biomarker-classified admissions by ACS subtypes and estimated annual change in admissions using Poisson regression adjusting for age and sex. There were 37 272 ACS admissions in 30 683 patients (64.2% male), and 96% of cases had linked biomarker data, predominantly conventional cTn at the start and high-sensitive cTn from late 2013. Despite lower ASSRs, trends in MI classified with a diagnostic biomarker were concordant with ICD-coded admissions rates for both STEMI and NSTEMI. Between 2002 and 2010, STEMI rates declined by 4.1% (95% CI 5.0%, 3.1%) and 3.4% (95% CI 4.6%, 2.3%) in ICD-coded and biomarker-classified admissions, respectively, and both plateaued thereafter. For NSTEMI between 2002 and 2010, the ICD-coded and biomarker-classified rates increased 8.0% per year (95% CI 7.2%, 8.9%) and 8.0% (95% CI 7.0%, 9.0%), respectively, and both subsequently declined. For UA, both ICD-coded and biomarker-classified UA admission rates declined in a steady and concordant manner between 2002 and 2016. The present study supports the validity of using administrative data to monitor population trends in ACS subtypes as they appear to generally reflect the redefinition of MI in the troponin era.

Sections du résumé

BACKGROUND BACKGROUND
Since 2000, the definition of myocardial infarction (MI) has evolved with reliance on cardiac troponin (cTn) tests. The implications of this change on trends of acute coronary syndrome (ACS) subtypes obtained from routinely collected hospital morbidity data are unclear. Using person-linked hospitalisation data, we compared International Classification of Diseases (ICD)-coded data with biomarker-classified admission rates for ST-segment elevation MI (STEMI), non-STEMI (NSTEMI) and unstable angina (UA) in Western Australia (WA).
METHODS METHODS
We used linked hospitalisation data from all WA tertiary hospitals to identify patients with a principal diagnosis of STEMI, NSTEMI or UA between 2002 and 2016. Linked biomarker results were classified as 'diagnostic' for MI according to established criteria. We calculated age-standardised and sex-standardised rates (ASSRs) for ICD-coded versus biomarker-classified admissions by ACS subtypes and estimated annual change in admissions using Poisson regression adjusting for age and sex.
RESULTS RESULTS
There were 37 272 ACS admissions in 30 683 patients (64.2% male), and 96% of cases had linked biomarker data, predominantly conventional cTn at the start and high-sensitive cTn from late 2013. Despite lower ASSRs, trends in MI classified with a diagnostic biomarker were concordant with ICD-coded admissions rates for both STEMI and NSTEMI. Between 2002 and 2010, STEMI rates declined by 4.1% (95% CI 5.0%, 3.1%) and 3.4% (95% CI 4.6%, 2.3%) in ICD-coded and biomarker-classified admissions, respectively, and both plateaued thereafter. For NSTEMI between 2002 and 2010, the ICD-coded and biomarker-classified rates increased 8.0% per year (95% CI 7.2%, 8.9%) and 8.0% (95% CI 7.0%, 9.0%), respectively, and both subsequently declined. For UA, both ICD-coded and biomarker-classified UA admission rates declined in a steady and concordant manner between 2002 and 2016.
CONCLUSIONS CONCLUSIONS
The present study supports the validity of using administrative data to monitor population trends in ACS subtypes as they appear to generally reflect the redefinition of MI in the troponin era.

Identifiants

pubmed: 39448082
pii: openhrt-2024-002995
doi: 10.1136/openhrt-2024-002995
pii:
doi:

Substances chimiques

Biomarkers 0
Troponin 0

Types de publication

Journal Article Multicenter Study Comparative Study

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

Auteurs

Dawit Zemedikun (D)

Cardiovascular Epidemiology Research Centre, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia dawit.zemedikun@uwa.edu.au.
Victor Chang Cardiac Research Institute, The University of Western Australia, Perth, Western Australia, Australia.

Joseph Hung (J)

Cardiovascular Epidemiology Research Centre, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia.
School of Medicine, The University of Western Australia, Perth, Western Australia, Australia.

Derrick Lopez (D)

Cardiovascular Epidemiology Research Centre, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia.

Matthew Knuiman (M)

Cardiovascular Epidemiology Research Centre, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia.

David Youens (D)

Cardiovascular Epidemiology Research Centre, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia.
School of Population Health, Curtin University, Perth, Western Australia, Australia.

Tom G Briffa (TG)

Cardiovascular Epidemiology Research Centre, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia.

Frank Sanfilippo (F)

Cardiovascular Epidemiology Research Centre, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia.

Lee Nedkoff (L)

Cardiovascular Epidemiology Research Centre, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia.
Victor Chang Cardiac Research Institute, The University of Western Australia, Perth, Western Australia, Australia.

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