What does this research mean for the field?

Machine learning models identified significant coronary artery lesions in 38.4% of out-of-hospital cardiac arrest patients without ST-segment elevation, achieving a highest accuracy of 0.751. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

December 2, 2021Open Access

Early Identification of Resuscitated Patients with a Significant Coronary Disease in Out-of-Hospital Cardiac Arrest Survivors without ST-Segment Elevation

Key Result

Machine learning models identified significant coronary artery lesions in 38.4% of OHCA patients without ST-segment elevation, with a highest accuracy of 0.751.

Structured PICO

Does a machine learning model improve early identification of significant coronary artery lesions in out-of-hospital cardiac arrest survivors without ST-segment elevation?

Population

331 out-of-hospital cardiac arrest (OHCA) survivors without ST-segment elevation who underwent coronary angiography (CAG) and targeted temperature management (TTM).

Intervention

Machine learning-based predictive models (regularized logistic regression, random forest classifier, CatBoost classifier, and voting classifier) using clinical variables available before targeted temperature management.

Outcome

Significant coronary artery lesion in coronary angiography (CAG)surrogate

Machine learning models using early clinical variables can moderately predict significant coronary artery disease (AUC 0.751) in OHCA survivors without ST-segment elevation, potentially guiding early angiography decisions.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

This study aimed to develop a machine learning (ML)-based model for identifying patients who had a significant coronary artery disease among out-of-hospital cardiac arrest (OHCA) survivors without ST-segment elevation (STE). This multicenter observational study used data from the Korean Hypothermia Network prospective registry (KORHN-PRO) gathered between October 2015 and December 2018. We used information available before targeted temperature management (TTM) as predictor variables, and the primary outcome was a significant coronary artery lesion in coronary angiography (CAG). Among 1373 OHCA patients treated with TTM, 331 patients without STE who underwent CAG were enrolled. Among them, 127 patients (38.4%) had a significant coronary artery lesion. Four ML algorithms, namely regularized logistic regression (RLR), random forest classifier (RF), CatBoost classifier (CBC), and voting classifier (VC), were used with data collected before CAG. The VC model showed the highest accuracy for predicting significant lesions (area under the curve of 0.751). Eight variables (older age, male, initial shockable rhythm, shorter total collapse duration, higher glucose and creatinine, and lower pH and lactate) were significant to ML models. These results showed that ML models may be useful in developing early predictive tools for identifying high-risk patients with a significant stenosis in CAG.

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