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.
Highlights
Coronary artery disease is the main cause of out-of-hospital cardiac arrest (OHCA) [1]
Current international guidelines recommend that coronary angiography (CAG) be performed emergently for all cardiac arrest patients with suspected cardiac cause of arrest and ST-segment elevation (STE) on electrocardiogram (ECG) [3]
In OHCA patients without STE, early CAG is suggested for selected patients, but guidelines do not provide specific characteristics of patients who may benefit from immediate CAG [4]
Summary
Coronary artery disease is the main cause of out-of-hospital cardiac arrest (OHCA) [1]. Current international guidelines recommend that coronary angiography (CAG) be performed emergently for all cardiac arrest patients with suspected cardiac cause of arrest and ST-segment elevation (STE) on electrocardiogram (ECG) [3]. The main challenge is to identify the best candidates for CAG among resuscitated cardiac arrest patients without STE. OHCA survivors without STE do not always have obstructive coronary artery disease, and identifying these patients is complicated. Clinical findings such as chest pain are often lacking, and troponin levels can be increased in resuscitated patients even without acute coronary causes. The decision for CAG should consider multiple factors, including previous medical history, symptoms before the arrest, initial cardiac arrest rhythm, laboratory results, and ECG patterns after the return of spontaneous survival (ROSC)
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