Abstract
BACKGROUND: Optimal mean arterial pressure (MAP) range after cardiac arrest remains uncertain. This study aimed to investigate the association between MAP and neurological outcomes during the early post-resuscitation period, with the goal of identifying optimal MAP range associated with favorable outcomes. METHODS: This retrospective observational study included 291 post-cardiac arrest patients treated at a tertiary care center. Five machine learning models to predict favorable neurological outcomes using hourly MAP measurements during the first 24 h after return of spontaneous circulation (ROSC) were compared and Random Forest model was selected due to its superior performance. Variable importance and Shapley Additive exPlanations (SHAP) were used to investigate the association between MAP and favorable neurological outcomes. SHAP dependence plots were used to identify optimal MAP ranges associated with favorable outcomes. In addition, individual-level predictions were interpreted using local interpretable model-agnostic explanations (LIME) and SHAP force plots. RESULTS: Machine learning analysis showed that MAP were associated with favorable neurological outcomes, with higher variable importance during the first 6 h after ROSC. SHAP analysis revealed an inverted U-shaped relationship between MAP and favorable neurological outcomes, with an optimal threshold of 79.56 mmHg (IQR: 73.70-82.54). This threshold remained consistent across both early (1-6 h: 79.26 mmHg) and later (7-24 h: 80.09 mmHg) hours. Individual-level explanations using SHAP and LIME highlighted that maintaining higher MAP during the early post-resuscitation period contributed positively to outcome predictions. CONCLUSIONS: Machine learning analysis identified MAP as a major predictor of favorable neurological outcomes, with higher variable importance during the first 6 h after ROSC. MAP showed an inverted U-shaped relationship with favorable neurological outcomes, with an optimal threshold of approximately 80 mmHg.