Key result
Random Forest model outperforms traditional scores in predicting cardiac arrest ICU mortality with ~0.83 AUC.
Why the study?
The incidence and mortality of cardiac arrest is high, necessitating models for early prediction of ICU mortality risk.
Does a Random Forest machine learning model improve the prediction of ICU mortality in patients with cardiac arrest compared to traditional severity scores?
Cohort (n=4,630)
Yes
Does a Random Forest machine learning model improve the prediction of ICU mortality in patients with cardiac arrest compared to traditional severity scores?
Effect estimate: AUC 0.83 (95% CI 0.78-0.88)
A Random Forest machine learning model using clinical variables like GCS, base excess, anion gap, and urine output accurately predicts ICU mortality risk in cardiac arrest patients, outperforming traditional severity scores.
May aid ICU risk stratification after cardiac arrest; leaves open prospective validation against traditional scores.
BACKGROUND: The incidence and mortality of cardiac arrest (CA) is high. We developed interpretable machine learning models for early prediction of ICU mortality risk in patients diagnosed with CA. METHODS: Data from the Medical Information Mart for Intensive Care (MIMIC-IV, version 2.2) was randomized to training set (0.7) and internal validation set (0.3), and data from eICU(version 2.0.1) was used as external validation set. Five models including Logistic Regression (LR), Random Forest (RF), K Nearest Neighbor (KNN), Decision Tree (DT), and Extreme Gradient Boost (XGBoost) were developed. The model with the largest area under the Receiver Operating Characteristic (ROC) curve (AUC) and good performance in other features was defined as the best model, and Shapley Additive Explanations (SHAP) was used to improve the interpretability of the optimal model. RESULTS: A total of 1088 patients from MIMIC-IV, and 3542 patients from eICU were included. Seven variables were selected to construct models by Least Absolute Shrinkage and Selection Operator (LASSO) regression. The RF model was the best predictive model with AUC and 95% CI at 0.83 (0.78-0.88) in internal validation set, and 0.71(0.68-0.74) in external validation set. SHAP analysis found that the variables that had a high impact on the risk of ICU death were minimal Glasgow Coma Scale (GCS), base excess, anion gap, and urine output. CONCLUSION: RF is the optimal model for predicting the risk of ICU death in CA patients. The development of this model is important for early identification and intervention of CA patients who are at risk of dying in the ICU.
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Li et al. (2025) conducted a cohort in Cardiac arrest (n=4,630). Random Forest (RF) machine learning model vs. Other machine learning models and traditional disease severity scores (SOFA, OASIS, SAPS II, APS III, LODS) was evaluated on Area under the Receiver Operating Characteristic curve (AUC) for predicting ICU mortality risk in the internal validation set (AUC 0.83, 95% CI 0.78-0.88). The Random Forest model was the optimal model for predicting the risk of ICU death in cardiac arrest patients, achieving an AUC of 0.83 in the internal validation set.
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