Using latent class analysis to identify clinical features of patients with occlusive myocardial infarction:

Charles Knoery1, Katie A McEwan2, Matthew Manktelow3

  • 1Division of Rural Health and Wellbeing, Institute of Health Research and Innovation, Centre for Health Science, University of the Highlands and Islands, Inverness, UK.

Clinical Cardiology
|February 8, 2022
PubMed

Insights

Identifying acute coronary occlusion (OMI) in myocardial infarction (MI) is challenging. Current methods fail to reliably distinguish OMI from non-OMI, leading to treatment delays and increased mortality.

Area of Science:

  • Cardiology
  • Interventional Cardiology
  • Acute Coronary Syndromes

Background:

  • Current myocardial infarction (MI) classification relies on ST elevation (STEMI) versus non-ST elevation (NSTEMI).
  • This stratification assumes ST elevation indicates acute coronary occlusion (OMI), but one-quarter of NSTEMI cases are OMI with higher mortality.
  • Identifying OMI is crucial for timely and effective treatment.

Purpose of the Study:

  • To identify clinical features that can reliably distinguish acute coronary occlusion (OMI) from non-occlusive myocardial infarction (MI).

Main Methods:

  • Analysis of prospectively collected data from 1412 patients undergoing percutaneous coronary intervention (PCI) for acute MI.
  • Latent class analysis was used to identify patterns of presentation and history associated with OMI.

Main Results:

  • 263 patients were diagnosed with OMI. OMI patients had fewer comorbidities but similar cerebrovascular disease risk and increased acute mortality (4.2% vs. 1.1%).
  • Nearly 30% of OMI patients experienced treatment delays, including reperfusion therapy.
  • Latent class analysis did not effectively differentiate OMI from non-OMI patients based on available data.

Conclusions:

  • Clinical features of OMI and STEMI are similar, but OMI/non-OMI groups showed no differences in age or cerebrovascular disease risk.
  • No reliable characteristics currently exist to distinguish OMI from non-OMI.
  • Treatment delays indicate OMI patients are not receiving optimal care, necessitating an alternative identification strategy.
Abstract

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