Key result
A machine learning algorithm predicted a culprit coronary artery lesion in OHCA patients with an AUC of 0.89 in the development cohort, outperforming ECG alone (AUC 0.69).
Why the study?
The authors aimed to develop a machine learning algorithm to predict the presence of a culprit coronary artery lesion in patients with out-of-hospital cardiac arrest.
Does a machine learning algorithm improve the prediction of a culprit coronary artery lesion compared to ECG alone in patients with out-of-hospital cardiac arrest?
Cohort (n=966)
Yes
Does a machine learning algorithm improve the prediction of a culprit coronary artery lesion compared to ECG alone in patients with out-of-hospital cardiac arrest?
A novel machine learning algorithm significantly outperforms ECG alone in predicting the presence of a culprit coronary artery lesion in patients with out-of-hospital cardiac arrest.
Answers are grounded in this paper and cite their sources.
May aid culprit lesion prediction in OHCA beyond ECG; hypothesis-generating and requires prospective validation before practice change.
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.
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Pareek et al. (2023) conducted a cohort in Out-of-hospital cardiac arrest (OHCA) (n=966). Machine learning algorithm vs. ECG alone was evaluated on presence of a culprit coronary artery lesion (AUC 0.89). A machine learning algorithm predicted a culprit coronary artery lesion in OHCA patients with an AUC of 0.89 in the development cohort, outperforming ECG alone (AUC 0.69).
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