Back to Search Start Over

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

Authors :
Pareek N
Frohmaier C
Smith M
Kordis P
Cannata A
Nevett J
Fothergill R
Nichol RC
Sullivan M
Sunderland N
Johnson TW
Noc M
Byrne J
MacCarthy P
Shah AM
Source :
Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions [Catheter Cardiovasc Interv] 2023 Jul; Vol. 102 (1), pp. 80-90. Date of Electronic Publication: 2023 May 16.
Publication Year :
2023

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).<br />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.<br />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).<br />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.<br /> (© 2023 The Authors. Catheterization and Cardiovascular Interventions published by Wiley Periodicals LLC.)

Details

Language :
English
ISSN :
1522-726X
Volume :
102
Issue :
1
Database :
MEDLINE
Journal :
Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
Publication Type :
Academic Journal
Accession number :
37191312
Full Text :
https://doi.org/10.1002/ccd.30677