Comparison of Prognostic Performance Between a Machine Learning Model and Manually Measured Grey-White-Matter Ratio on Early Brain Computed Tomography After Out-of-Hospital Cardiac Arrest

Objectives Early prediction of neurological outcomes in patients with out-of-hospital cardiac arrest (OHCA) is critical for guiding treatment decisions. Machine learning (ML) model and grey-white matter ratio (GWR), both derived from brain computed tomography (CT), can be used to predict the neurological outcome. However, their relative performance shortly post-return of spontaneous circulation (ROSC) and whether combining the ML model with prehospital information can improve predictive performance remains unclear. This study aimed (1) to compare the predictive performance for poor neurological outcome between the ML model and manually measured GWR in the early phase after ROSC in patients with OHCA, and (2) to assess the predictive ability of the combination of the ML model and prehospital information. Methods This single-center retrospective study included adult patients who underwent brain CT within two hours post-ROSC. The endpoint was consecutive coma post-ROSC. Three slice levels (basal ganglia, centrum semiovale, high convexity) of brain CT images were used to generate the ML model and the GWR. Residual Network 101 (ResNet-101) with transfer learning was constructed in the ML model. Results Among the 143 cases, 88 patients had a persistent coma, and 55 awoke from coma. Across 10 repeated five-fold cross-validations, the area under the receiver operating characteristic curves (AUCs) for predicting persistent coma between the three-slice ensembled ML model and the average GWR were not significantly different (ML model: 0.796 (interquartile range (IQR): 0.737-0.826), GWR: 0.821 (IQR: 0.763-0.854); p = 0.121). In the regression analysis, the AUC of the model based on prehospital information was 0.846 (95% CI: 0.772-0.92), which improved to 0.905 (95% CI: 0.856-0.953) after adding the ML score. Conclusion The ML model achieved moderate predictive performance, with no significant difference compared with the conventional GWR method. The combination of the ML model and prehospital information could improve predictive performance.