Comparison of model-based and expert-rule based electrocardiographic identification of the culprit artery in patients with acute coronary syndrome.

Published

Journal Article

Culprit coronary artery assessment in the triage ECG of patients with suspected acute coronary syndrome (ACS) is relevant a priori knowledge preceding percutaneous coronary intervention (PCI). We compared a model-based automated method (Olson method) with an expert-rule based method for the culprit artery assessment.In each of the 53 patients who were admitted with the working diagnosis of suspected ACS, scheduled for emergent angiography with a view on revascularization as initial treatment and subsequently found to have an angiographically documented completely occluded culprit artery, culprit artery location was assessed in the preceding ECG by both the model-based Olson method and the expert-rule based method that considered either visual or computer-measured J-point amplitudes. ECG culprit artery estimations were compared with the angiographic culprit lesion locations. Proportions of correct classifications were compared by a Z test at the 5% significance level.The Olson method performed slightly, but not significantly, better, when the expert-rule based method used visual assessment of J-point amplitudes (88.7% versus 81.1% correct; P=0.28). However, the Olson method performed significantly better when the expert-rule based method used computer-measured J-point amplitudes (88.7% versus 71.7% correct; P<0.05).The automated model-based Olson method performed at least at the level of expert cardiologists using a manual rule-based method.

Full Text

Cited Authors

  • Kamphuis, VP; Wagner, GS; Pahlm, O; Man, S; Olson, CW; Bacharova, L; Swenne, CA

Published Date

  • July 2015

Published In

Volume / Issue

  • 48 / 4

Start / End Page

  • 483 - 489

PubMed ID

  • 26025202

Pubmed Central ID

  • 26025202

Electronic International Standard Serial Number (EISSN)

  • 1532-8430

International Standard Serial Number (ISSN)

  • 0022-0736

Digital Object Identifier (DOI)

  • 10.1016/j.jelectrocard.2015.05.003

Language

  • eng