A machine learning algorithm to predict a culprit lesion after out of hospital cardiac arrest

Nilesh Pareek*, Christopher Frohmaier, Mathew Smith, Peter Kordis, Antonio Cannata, Jo Nevett, Rachael Fothergill, Robert C Nichol, Mark Sullivan, Nicholas Sunderland, Thomas W Johnson, Marko Noc, Jonathan Byrne, Philip MacCarthy, Ajay M Shah

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)

Abstract

BACKGROUND: We aimed to develop a machine learning algorithm to predict the presence of a culprit lesion in patients with out-of-hospital cardiac arrest (OHCA).

METHODS: We used the King's Out-of-Hospital Cardiac Arrest Registry, a retrospective cohort of 398 patients admitted to King's College Hospital between May 2012 and December 2017. The primary outcome was the presence of a culprit coronary artery lesion, for which a gradient boosting model was optimized to predict. The algorithm was then validated in two independent European cohorts comprising 568 patients.

RESULTS: A culprit lesion was observed in 209/309 (67.4%) patients receiving early coronary angiography in the development, and 199/293 (67.9%) in the Ljubljana and 102/132 (61.1%) in the Bristol validation cohorts, respectively. The algorithm, which is presented as a web application, incorporates nine variables including age, a localizing feature on electrocardiogram (ECG) (≥2 mm of ST change in contiguous leads), regional wall motion abnormality, history of vascular disease and initial shockable rhythm. This model had an area under the curve (AUC) of 0.89 in the development and 0.83/0.81 in the validation cohorts with good calibration and outperforms the current gold standard-ECG alone (AUC: 0.69/0.67/0/67).

CONCLUSIONS: A novel simple machine learning-derived algorithm can be applied to patients with OHCA, to predict a culprit coronary artery disease lesion with high accuracy.

Original languageEnglish
Pages (from-to)80-90
Number of pages11
JournalCatheterization and Cardiovascular Interventions
Volume102
Issue number1
Early online date16 May 2023
DOIs
Publication statusPublished - 1 Jul 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors. Catheterization and Cardiovascular Interventions published by Wiley Periodicals LLC.

Keywords

  • Humans
  • Out-of-Hospital Cardiac Arrest/diagnosis
  • Retrospective Studies
  • Treatment Outcome
  • Coronary Artery Disease/diagnostic imaging
  • Coronary Angiography
  • Algorithms
  • Cardiopulmonary Resuscitation

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