Performance and limitations of automated ECG interpretation statements in patients with suspected acute coronary syndrome.


Journal

Journal of electrocardiology
ISSN: 1532-8430
Titre abrégé: J Electrocardiol
Pays: United States
ID NLM: 0153605

Informations de publication

Date de publication:
Historique:
received: 25 05 2021
revised: 11 08 2021
accepted: 11 08 2021
pubmed: 2 9 2021
medline: 28 1 2022
entrez: 1 9 2021
Statut: ppublish

Résumé

The 12‑lead ECG plays an important role in triaging patients with symptomatic coronary artery disease, making automated ECG interpretation statements of "Acute MI" or "Acute Ischemia" crucial, especially during prehospital transport when access to physician interpretation of the ECG is limited. However, it remains unknown how automated interpretation statements correspond to adjudicated clinical outcomes during hospitalization. We sought to evaluate the diagnostic performance of prehospital automated interpretation statements to four well-defined clinical outcomes of interest: confirmed ST- segment elevation myocardial infarction (STEMI); presence of actionable coronary culprit lesions, myocardial necrosis, or any acute coronary syndrome (ACS). An observational cohort study that enrolled consecutive patients with non-traumatic chest pain transported via ambulance. Prehospital ECGs were obtained with the Philips MRX monitor from the medical command center and re-processed using manufacturer-specific diagnostic algorithms to denote the likelihood of >>>Acute MI<<< or >>>Acute Ischemia<<<. Two independent reviewers retrospectively adjudicated the study outcomes and disagreements were resolved by a third reviewer. Our study included 2400 patients (age 59 ± 16, 47% females, 41% Black), with 190 (8%) patients with documented automated diagnostic statements of acute MI or acute ischemia. The sensitivity/specificity of the automated algorithm for detecting confirmed STEMI (n = 143, 6%); presence of actionable coronary culprit lesions (n = 258, 11%), myocardial necrosis (n = 291, 12%), or any ACS (n = 378, 16%) were 62.9%/95.6%; 37.2%/95.6%; 38.5%/96.4%; and 30.7%/96.3%, respectively. Although being very specific, automated interpretation statements of acute MI/acute ischemia on prehospital ECGs are not satisfactorily sensitive to exclude symptomatic coronary disease. Patients without these automated interpretation statements should be considered further for significant underlying coronary disease based on the clinical context. ClinicalTrials.gov # NCT04237688.

Sections du résumé

BACKGROUND
The 12‑lead ECG plays an important role in triaging patients with symptomatic coronary artery disease, making automated ECG interpretation statements of "Acute MI" or "Acute Ischemia" crucial, especially during prehospital transport when access to physician interpretation of the ECG is limited. However, it remains unknown how automated interpretation statements correspond to adjudicated clinical outcomes during hospitalization. We sought to evaluate the diagnostic performance of prehospital automated interpretation statements to four well-defined clinical outcomes of interest: confirmed ST- segment elevation myocardial infarction (STEMI); presence of actionable coronary culprit lesions, myocardial necrosis, or any acute coronary syndrome (ACS).
METHODS
An observational cohort study that enrolled consecutive patients with non-traumatic chest pain transported via ambulance. Prehospital ECGs were obtained with the Philips MRX monitor from the medical command center and re-processed using manufacturer-specific diagnostic algorithms to denote the likelihood of >>>Acute MI<<< or >>>Acute Ischemia<<<. Two independent reviewers retrospectively adjudicated the study outcomes and disagreements were resolved by a third reviewer.
RESULTS
Our study included 2400 patients (age 59 ± 16, 47% females, 41% Black), with 190 (8%) patients with documented automated diagnostic statements of acute MI or acute ischemia. The sensitivity/specificity of the automated algorithm for detecting confirmed STEMI (n = 143, 6%); presence of actionable coronary culprit lesions (n = 258, 11%), myocardial necrosis (n = 291, 12%), or any ACS (n = 378, 16%) were 62.9%/95.6%; 37.2%/95.6%; 38.5%/96.4%; and 30.7%/96.3%, respectively.
CONCLUSION
Although being very specific, automated interpretation statements of acute MI/acute ischemia on prehospital ECGs are not satisfactorily sensitive to exclude symptomatic coronary disease. Patients without these automated interpretation statements should be considered further for significant underlying coronary disease based on the clinical context.
TRIAL REGISTRATION
ClinicalTrials.gov # NCT04237688.

Identifiants

pubmed: 34465465
pii: S0022-0736(21)00179-5
doi: 10.1016/j.jelectrocard.2021.08.014
pmc: PMC8664995
mid: NIHMS1736692
pii:
doi:

Banques de données

ClinicalTrials.gov
['NCT04237688']

Types de publication

Journal Article Observational Study Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

45-50

Subventions

Organisme : NHLBI NIH HHS
ID : R01 HL137761
Pays : United States

Informations de copyright

Copyright © 2021 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest US Patent # 10820822.

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Auteurs

Ziad Faramand (Z)

Department of Acute & Tertiary Care Nursing at University of Pittsburgh, PA, USA; Department of Emergency Medicine at University of Pittsburgh, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.

Stephanie Helman (S)

Department of Acute & Tertiary Care Nursing at University of Pittsburgh, PA, USA.

Abdullah Ahmad (A)

Englewood Hospital and Medical Center, Englewood, NJ, USA.

Christian Martin-Gill (C)

Department of Emergency Medicine at University of Pittsburgh, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.

Clifton Callaway (C)

Department of Emergency Medicine at University of Pittsburgh, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.

Samir Saba (S)

Division of Cardiology at University of Pittsburgh, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.

Richard E Gregg (RE)

Philips Healthcare, Andover, MA, USA.

John Wang (J)

Philips Healthcare, Andover, MA, USA.

Salah Al-Zaiti (S)

Department of Acute & Tertiary Care Nursing at University of Pittsburgh, PA, USA; Department of Emergency Medicine at University of Pittsburgh, PA, USA; Division of Cardiology at University of Pittsburgh, PA, USA. Electronic address: ssa33@pitt.edu.

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