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Background <p>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.</p> Methods <p>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&#xa0;(DT), and Extreme Gradient Boost (XGBoost) were developed. The model with the largest area under the Receiver Operating Characteristic (ROC)&#xa0;curve (AUC) and good performance in other features was defined as the best model, and Shapley Additive Explanations&#xa0;(SHAP)&#xa0;was used to improve the interpretability of the optimal model.</p> Results <p>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. </p> Conclusion <p>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.</p>

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Construction and validation of prognostic model for ICU mortality in cardiac arrest patients: an interpretable machine learning modeling approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月24日

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  • Yong Li,
  • Ying Liu,
  • Qing Zhang,
  • Hongwei Zhu,
  • Chengli Wen,
  • Xian Jiang

摘要

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.