Human–Computer Agreement of Electrocardiogram Interpretation for Patients Referred to and Declined for Primary Percutaneous Coronary Intervention: Retrospective Data Analysis Study

Aleeha Iftikhar, RR Bond, V. E. McGilligan, Stephen Leslie, Charles Knoery, James A Shand, Adesh Ramsewak, Divyesh Sharma, Adam Canning, Anne McShane, Khaled Rjoob, Aaron Peace

Research output: Contribution to journalArticlepeer-review

56 Downloads (Pure)

Abstract

Background: When a patient is suspected of having an acute myocardial infarction, they are accepted or declined for primary percutaneous coronary intervention partly based on clinical assessment of their 12-lead electrocardiogram (ECG) and ST-elevation myocardial infarction criteria. Objective: We retrospectively determined the agreement rate between human (specialists called activator nurses) and computer interpretations of ECGs of patients who were declined for primary percutaneous coronary intervention. Methods: Various features of patients who were referred for primary percutaneous coronary intervention were analyzed. Both the human and computer ECG interpretations were simplified to either “suggesting” or “not suggesting” acute myocardial infarction to avoid analysis of complex heterogeneous and synonymous diagnostic terms. Analyses, to measure agreement, and logistic regression, to determine if these ECG interpretations (and other variables such as patient age, chest pain) could predict patient mortality, were carried out. Results: Of a total of 1464 patients referred to and declined for primary percutaneous coronary intervention, 722 (49.3%) computer diagnoses suggested acute myocardial infarction, whereas 634 (43.3%) of the human interpretations suggested acute myocardial infarction (P<.001). The human and computer agreed that there was a possible acute myocardial infarction for 342 out of 1464 (23.3%) patients. However, there was a higher rate of human–computer agreement for patients not having acute myocardial infarctions (450/1464, 30.7%). The overall agreement rate was 54.1% (792/1464). Cohen κ showed poor agreement (κ=0.08, P=.001). Only the age (odds ratio [OR] 1.07, 95% CI 1.05-1.09) and chest pain (OR 0.59, 95% CI 0.39-0.89) independent variables were statistically significant (P=.008) in predicting mortality after 30 days and 1 year. The odds for mortality within 1 year of referral were lower in patients with chest pain compared to those patients without chest pain. A referral being out of hours was a trending variable (OR 1.41, 95% CI 0.95-2.11, P=.09) for predicting the odds of 1-year mortality. Conclusions: Mortality in patients who were declined for primary percutaneous coronary intervention was higher than the reported mortality for ST-elevation myocardial infarction patients at 1 year. Agreement between computerized and human ECG interpretation is poor, perhaps leading to a high rate of inappropriate referrals. Work is needed to improve computer and human decision making when reading ECGs to ensure that patients are referred to the correct treatment facility for time-critical therapy.

Original languageEnglish
Article numbere24188
Pages (from-to)1-11
Number of pages11
JournalJMIR Medical Informatics
Volume9
Issue number3
DOIs
Publication statusPublished (in print/issue) - 2 Mar 2021

Bibliographical note

Publisher Copyright:
©Aleeha Iftikhar, Raymond Bond, Victoria Mcgilligan, Stephen J Leslie, Charles Knoery, James Shand, Adesh Ramsewak, Divyesh Sharma, Anne McShane, Khaled Rjoob, Aaron Peace.

Keywords

  • ECG Interpretation
  • Acute Myocardial Infarction
  • Agreement between Human and Computer
  • Primary percutaneous coronary intervention
  • Heart
  • Intervention
  • Diagnostic
  • Electrocardiogram
  • Human-computer
  • Agreement between human and computer
  • ECG interpretation
  • Infarction
  • Scan
  • Acute myocardial infarction
  • Primary percutaneous coronary intervention service

Fingerprint

Dive into the research topics of 'Human–Computer Agreement of Electrocardiogram Interpretation for Patients Referred to and Declined for Primary Percutaneous Coronary Intervention: Retrospective Data Analysis Study'. Together they form a unique fingerprint.

Cite this