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Background <p>Cardiac arrest remains a significant cause of mortality worldwide. Identifying factors associated with in-hospital mortality can improve patient care and outcomes.</p> Methods <p>A retrospective analysis was performed, including 1845 patients diagnosed with cardiac arrest from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and 52 patients from the Affiliated Hospital of Yunnan University (YNU). The study evaluated variables such as demographic characteristics, laboratory test results, and comorbidities. Prediction models for in-hospital mortality following cardiac arrest were constructed using five machine learning algorithms: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (LightGBM). The predictive performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), as well as metrics including accuracy, precision, recall, and the F1 score.</p> Results <p>Among the five machine learning algorithms evaluated, the XGBoost model demonstrated the highest performance. The XGBoost model achieved an AUC of 0.828, with an accuracy of 0.737, a precision of 0.709, an F1 score of 0.713, a recall of 0.717, a positive predictive value (PPV) of 0.709, and a negative predictive value (NPV) of 0.767. External validation of the XGBoost model using data from the Affiliated Hospital of Yunnan University yielded an AUC of 0.845, further supporting its predictive reliability. SHAP (SHapley Additive exPlanations) analysis identified the five variables with the greatest impact on in-hospital mortality in patients with cardiac arrest as mechanical ventilation, age, minimum lactate value, maximum arterial blood gas oxygen partial pressure, and urine output.</p> Conclusions <p>The XGBoost model enabled good prediction of in-hospital mortality in ICU admission cardiac arrest patients, which may be widely used in clinical decision-making.</p> Clinical trial number <p>Not applicable.</p>

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Prediction of in-hospital mortality risk in cardiac arrest patients using machine learning models: a study based on the MIMIC-IV database with external validation from Yunnan University Affiliated Hospital

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月28日

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  • Ji Jia,
  • Wei Zhang,
  • Hua-lei Dai,
  • Ying-xia Guan,
  • Zhi-gang Yang,
  • Wei Wei,
  • Xin-jin Zhang,
  • Si-ming Tao

摘要

Background

Cardiac arrest remains a significant cause of mortality worldwide. Identifying factors associated with in-hospital mortality can improve patient care and outcomes.

Methods

A retrospective analysis was performed, including 1845 patients diagnosed with cardiac arrest from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and 52 patients from the Affiliated Hospital of Yunnan University (YNU). The study evaluated variables such as demographic characteristics, laboratory test results, and comorbidities. Prediction models for in-hospital mortality following cardiac arrest were constructed using five machine learning algorithms: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (LightGBM). The predictive performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), as well as metrics including accuracy, precision, recall, and the F1 score.

Results

Among the five machine learning algorithms evaluated, the XGBoost model demonstrated the highest performance. The XGBoost model achieved an AUC of 0.828, with an accuracy of 0.737, a precision of 0.709, an F1 score of 0.713, a recall of 0.717, a positive predictive value (PPV) of 0.709, and a negative predictive value (NPV) of 0.767. External validation of the XGBoost model using data from the Affiliated Hospital of Yunnan University yielded an AUC of 0.845, further supporting its predictive reliability. SHAP (SHapley Additive exPlanations) analysis identified the five variables with the greatest impact on in-hospital mortality in patients with cardiac arrest as mechanical ventilation, age, minimum lactate value, maximum arterial blood gas oxygen partial pressure, and urine output.

Conclusions

The XGBoost model enabled good prediction of in-hospital mortality in ICU admission cardiac arrest patients, which may be widely used in clinical decision-making.

Clinical trial number

Not applicable.