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

ECG interpretation acute myocardial infarction agreement between human and computer diagnostic electrocardiogram heart human-computer infarction intervention primary percutaneous coronary intervention service scan

Journal

JMIR medical informatics
ISSN: 2291-9694
Titre abrégé: JMIR Med Inform
Pays: Canada
ID NLM: 101645109

Informations de publication

Date de publication:
02 Mar 2021
Historique:
received: 08 09 2020
accepted: 17 01 2021
revised: 13 10 2020
entrez: 2 3 2021
pubmed: 3 3 2021
medline: 3 3 2021
Statut: epublish

Résumé

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. 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. 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. 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. 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.

Sections du résumé

BACKGROUND 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 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 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 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 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.

Identifiants

pubmed: 33650984
pii: v9i3e24188
doi: 10.2196/24188
pmc: PMC7967222
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e24188

Informations de copyright

©Aleeha Iftikhar, Raymond Bond, Victoria Mcgilligan, Stephen J Leslie, Charles Knoery, James Shand, Adesh Ramsewak, Divyesh Sharma, Anne McShane, Khaled Rjoob, Aaron Peace. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 02.03.2021.

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Auteurs

Aleeha Iftikhar (A)

Computing Engineering and Build Environment, Ulster University, Belfast, United Kingdom.

Raymond Bond (R)

Computing Engineering and Build Environment, Ulster University, Belfast, United Kingdom.

Victoria Mcgilligan (V)

Centre for Personalised Medicine, Ulster University, Londonderry, United Kingdom.

Stephen J Leslie (SJ)

Cardiac Unit, Raigmore Hospital, Inverness, United Kingdom.

Charles Knoery (C)

Cardiac Unit, Raigmore Hospital, Inverness, United Kingdom.

James Shand (J)

Department of Cardiology, Altnagelvin Hospital, Western Health and Social Care Trust, Londonderry, United Kingdom.

Adesh Ramsewak (A)

Department of Cardiology, Altnagelvin Hospital, Western Health and Social Care Trust, Londonderry, United Kingdom.

Divyesh Sharma (D)

Department of Cardiology, Altnagelvin Hospital, Western Health and Social Care Trust, Londonderry, United Kingdom.

Anne McShane (A)

Letterkenny University Hospital, Letterkenny, Ireland.

Khaled Rjoob (K)

Computing Engineering and Build Environment, Ulster University, Belfast, United Kingdom.

Aaron Peace (A)

Department of Cardiology, Altnagelvin Hospital, Western Health and Social Care Trust, Londonderry, United Kingdom.

Classifications MeSH