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Improved Detection of Acute Coronary Occlusion Myocardial Infarction by an Artificial Intelligence Electrocardiogram Model in Swedish Emergency Departments

Lindow, Thomas LU ; Nyström, Axel LU ; Forberg, Jakob Lundager LU ; Mokhtari, Arash LU ; Björkelund, Anders LU ; Herman, Robert ; Meyers, H. Pendell ; Smith, Stephen W. and Ekelund, Ulf LU orcid (2026) In JACEP Open 7(5).
Abstract

Objectives: The Queen of Hearts (QoH) ECG artificial intelligence model has demonstrated improved sensitivity for detecting occlusion myocardial infarction (OMI) compared with STEMI criteria, but further validation is needed. We aimed to evaluate QoH's diagnostic performance in patients with chest pain at Swedish emergency departments (EDs). Methods: This retrospective analysis included consecutive patients with chest pain at the ED from the ESC-TROP study (2017-2018). Patients transferred directly from the prehospital setting to the coronary care unit were not included. OMI classification was based on angiographic data and expert adjudication. QoH, conventional STEMI criteria, and the Glasgow ECG Analysis Algorithm were applied to all... (More)

Objectives: The Queen of Hearts (QoH) ECG artificial intelligence model has demonstrated improved sensitivity for detecting occlusion myocardial infarction (OMI) compared with STEMI criteria, but further validation is needed. We aimed to evaluate QoH's diagnostic performance in patients with chest pain at Swedish emergency departments (EDs). Methods: This retrospective analysis included consecutive patients with chest pain at the ED from the ESC-TROP study (2017-2018). Patients transferred directly from the prehospital setting to the coronary care unit were not included. OMI classification was based on angiographic data and expert adjudication. QoH, conventional STEMI criteria, and the Glasgow ECG Analysis Algorithm were applied to all cases. In addition, extended STEMI criteria incorporating additional ECG leads (-V1, -V2, -V3, -aVL, -aVR, and -III) and OMI criteria for left bundle branch block (modified Sgarbossa criteria) and left ventricular hypertrophy (ST elevation V1-V3 ≥0.25 of R and S) were applied. Results: Among 24,511 patients (mean age 59 ± 19 years, 52% male), 467 (1.9%) had OMI. QoH achieved higher sensitivity than STEMI criteria (52% [47 to 57] vs 23% [19 to 27]), similar specificity (99% [99 to 99] vs 98% [98 to 98]), higher positive predictive value (51% [47 to 54] vs 17% [15 to 20]), and similar negative predictive value (99% [99 to 99] vs 98 [98 to 99]). The Glasgow algorithm obtained 32% (28 to 37) sensitivity, 98% (98 to 98) specificity, 26% (23 to 30) PPV, and 99% (99 to 99) NPV, and corresponding number for the extended criteria were 41% (36 to 45), 95% (95 to 96), 14% (12 to 15), and 99% (99 to 99). Conclusion: In ED patients with chest pain, QoH improved sensitivity in OMI detection compared with currently available ECG criteria, with similar specificity.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
acute coronary occlusion, acute myocardial infarction, artificial intelligence, diagnostic accuracy, ST depression, ST elevation
in
JACEP Open
volume
7
issue
5
article number
100473
publisher
John Wiley & Sons Inc.
external identifiers
  • scopus:105047178733
  • pmid:42614578
ISSN
2688-1152
DOI
10.1016/j.acepjo.2026.100473
language
English
LU publication?
yes
id
158a8641-6ddc-4971-abc2-a5a848150365
date added to LUP
2026-09-28 16:16:28
date last changed
2026-09-29 03:04:10
@article{158a8641-6ddc-4971-abc2-a5a848150365,
  abstract     = {{<p>Objectives: The Queen of Hearts (QoH) ECG artificial intelligence model has demonstrated improved sensitivity for detecting occlusion myocardial infarction (OMI) compared with STEMI criteria, but further validation is needed. We aimed to evaluate QoH's diagnostic performance in patients with chest pain at Swedish emergency departments (EDs). Methods: This retrospective analysis included consecutive patients with chest pain at the ED from the ESC-TROP study (2017-2018). Patients transferred directly from the prehospital setting to the coronary care unit were not included. OMI classification was based on angiographic data and expert adjudication. QoH, conventional STEMI criteria, and the Glasgow ECG Analysis Algorithm were applied to all cases. In addition, extended STEMI criteria incorporating additional ECG leads (-V1, -V2, -V3, -aVL, -aVR, and -III) and OMI criteria for left bundle branch block (modified Sgarbossa criteria) and left ventricular hypertrophy (ST elevation V1-V3 ≥0.25 of R and S) were applied. Results: Among 24,511 patients (mean age 59 ± 19 years, 52% male), 467 (1.9%) had OMI. QoH achieved higher sensitivity than STEMI criteria (52% [47 to 57] vs 23% [19 to 27]), similar specificity (99% [99 to 99] vs 98% [98 to 98]), higher positive predictive value (51% [47 to 54] vs 17% [15 to 20]), and similar negative predictive value (99% [99 to 99] vs 98 [98 to 99]). The Glasgow algorithm obtained 32% (28 to 37) sensitivity, 98% (98 to 98) specificity, 26% (23 to 30) PPV, and 99% (99 to 99) NPV, and corresponding number for the extended criteria were 41% (36 to 45), 95% (95 to 96), 14% (12 to 15), and 99% (99 to 99). Conclusion: In ED patients with chest pain, QoH improved sensitivity in OMI detection compared with currently available ECG criteria, with similar specificity.</p>}},
  author       = {{Lindow, Thomas and Nyström, Axel and Forberg, Jakob Lundager and Mokhtari, Arash and Björkelund, Anders and Herman, Robert and Meyers, H. Pendell and Smith, Stephen W. and Ekelund, Ulf}},
  issn         = {{2688-1152}},
  keywords     = {{acute coronary occlusion; acute myocardial infarction; artificial intelligence; diagnostic accuracy; ST depression; ST elevation}},
  language     = {{eng}},
  number       = {{5}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{JACEP Open}},
  title        = {{Improved Detection of Acute Coronary Occlusion Myocardial Infarction by an Artificial Intelligence Electrocardiogram Model in Swedish Emergency Departments}},
  url          = {{http://dx.doi.org/10.1016/j.acepjo.2026.100473}},
  doi          = {{10.1016/j.acepjo.2026.100473}},
  volume       = {{7}},
  year         = {{2026}},
}