Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes

Abstract

Aim: Out-of-hospital cardiac arrest (OHCA) is a major healthcare burden, and prognosis is critical in decision-making for treatment and the withdrawal of life-sustaining therapy. This study aimed to develop and validate a deep-learning-based out-of-hospital cardiac arrest prognostic system (DCAPS) for predicting neurologic recovery and survival to discharge. Methods: The study subjects were patients from the Korea OHCA registry who experienced return of spontaneous circulation (ROSC) after OHCA. A total of 36,190 patients were exclusively divided into a set of 28,045 subjects for derivation data and 8,145 subjects for validation data. We used information available for the time of ROSC as predictor variables, and the endpoints were neurologic recovery (cerebral performance category 1 or 2) and survival to discharge. The DCAPS was developed using the derivation data and represented the favorability of prognosis with a score between 0 and 100. Results: The area under the receiver operating characteristic curve (AUROC) of DCAPS for predicting neurologic recovery for the validation data was 0.953 [95% confidence interval 0.952-0.954]; these results significantly outperformed those of logistic regression (0.947 [0.943-0.948]), random forest (0.943 [0.942-0.945]), support vector machine (0.930 [0.929-0.932]), and conventional methods of a previous study (0.817 [0.815-0.820]). The AUROC of the DCAPS for survival to discharge was 0.901 [0.900-0.903], and this result significantly outperformed those of other models as well. Conclusions: The DCAPS predicted neurologic recovery and survival to discharge of OHCA patients accurately and outperformed the conventional method and other machine-learning methods.