Using latent class analysis to identify clinical features of patients with occlusive myocardial infarction: pre-angiogram prediction remains difficult

Charles Knoery, Katie. A. McEwan, Matthew Manktelow, Jonathan Watt, Jamie Smith, Aleeha Iftikhar, Khaled Rjoob, RR Bond, V. E. McGilligan, Aaron Peace, Anne McShane, Janet Heaton, Stephen James Leslie

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Abstract

Background: Treatment decisions in myocardial infarction (MI) are currently stratified by ST elevation (STEMI) or lack of ST elevation (NSTEMI) on the electrocardiogram. This arose from the assumption that ST elevation indicated acute coronary artery occlusion (OMI)1. However, one-quarter of all NSTEMI cases are an OMI, and have a higher mortality2. The purpose of this study was to identify features that could help identify OMI.Methods: Prospectively collected data from patients undergoing percutaneous coronary intervention (PCI) was analysed. Data included presentation characteristics, co-morbidities, treatments, and outcomes. Latent class analysis was undertaken, to determine patterns of presentation and history associated with OMI. Results: A total of 1412 patients underwent PCI for acute MI, and 263 were diagnosed as OMI. Compared to non-occlusive MI, OMI patients are more likely to have fewer co-morbidities but no difference in cerebrovascular disease and increased acute mortality (4.2% vs 1.1%; p<0.001). Of OMI 29.5% had delays to their treatment such as immediate reperfusion therapy. With latent class analysis, while clusters of similar patients are observed in the dataset, the data available did not usefully identify patients with OMI compared to non-OMI.Conclusion: Features between OMI and STEMI are broadly very similar. However, there was no difference in age and risk of cerebrovascular disease in the OMI/non-OMI group. There are no reliable characteristics therefore for identifying OMI versus non-OMI. Delays to treatment also suggest that OMI patients are still missing out on optimal treatment. An alternative strategy is required to improve the identification of OMI patients.
Original languageEnglish
JournalClinical Cardiology
Publication statusAccepted/In press - 23 Nov 2021

Keywords

  • latent analysis
  • cardiology

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