Development and multicenter validation of a machine learning model for predicting in-hospital postoperative cardiac arrest in elderly surgical patients: the critical role of intraoperative hemodynamic disturbances and blood transfusion.
6 affiliations
- 1Department of Hepatobiliary Surgery, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China.
- 2Yixing Branch of the Wuxi Medical Centre Affiliated to Nanjing Medical University, Yixing, China.
- 3Department of General Surgery, The Affiliated Tengzhou Central People's Hospital of Xuzhou Medical University, Zao Zhuang, China.
- 4Clinical Medical Center, Tengzhou Central People's Hospital, Tengzhou, Shandong, China.
- 5Gaomi People's Hospital, Gaomi, Shandong, China.
- 6Tengzhou Hospital of Traditional Chinese Medicine, Shandong, China.
Abstract
Background
In-hospital cardiac arrest (IHCA) is a rare but life-threatening complication in elderly surgical patients. This study aimed to develop and validate a machine learning model for predicting IHCA and to identify key perioperative risk factors.Methods
A multicenter retrospective cohort study was conducted among patients aged ≥65 years who underwent non-cardiac surgery at six tertiary hospitals between January 2020 and January 2025. Clinical, laboratory, and perioperative variables were collected. Five machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and K-Nearest Neighbor (KNN), were developed and compared. Model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration, decision curve analysis, cross-validation, and external validation. SHAP analysis was applied to interpret the optimal model.Results
Among the five models, XGBoost demonstrated the best predictive performance, with AUCs of 0.967 and 0.960 in the training and validation cohorts, respectively. It also achieved the lowest Brier score and the highest clinical net benefit. External validation confirmed excellent discrimination (AUC=0.971) and calibration. SHAP analysis identified advanced age, intraoperative tachycardia, intraoperative hypotension, intraoperative blood transfusion, intraoperative hypoxemia, elevated postoperative neutrophil-to-lymphocyte ratio, B-type natriuretic peptide, lactate, and D-dimer levels as the most important predictors of IHCA.Conclusion
An interpretable XGBoost-based model demonstrated excellent performance for predicting postoperative cardiac arrest in elderly patients. The identified risk factors reflected perioperative vulnerability, intraoperative hemodynamic instability, and postoperative physiological deterioration.
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BibTeX
@article{chen2026development,
title = {{Development and multicenter validation of a machine learning model for predicting in-hospital postoperative cardiac arrest in elderly surgical patients: the critical role of intraoperative hemodynamic disturbances and blood transfusion.}},
author = {Xi Chen and Yuwei Chang and Tao Sun and Yuan Liu and Lei Liu and Chao Jiang and Feng Zhan and Wenyi Du},
journal = {Frontiers in medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1922481},
url = {https://europepmc.org/article/MED/42948089},
note = {CC BY},
}
RIS
TY - JOUR
TI - Development and multicenter validation of a machine learning model for predicting in-hospital postoperative cardiac arrest in elderly surgical patients: the critical role of intraoperative hemodynamic disturbances and blood transfusion.
AU - Xi Chen
AU - Yuwei Chang
AU - Tao Sun
AU - Yuan Liu
AU - Lei Liu
AU - Chao Jiang
AU - Feng Zhan
AU - Wenyi Du
PY - 2026
DA - 2026/10/13
JO - Frontiers in medicine
DO - 10.3389/fmed.2026.1922481
UR - https://europepmc.org/article/MED/42948089
AB - Background In-hospital cardiac arrest (IHCA) is a rare but life-threatening complication in elderly surgical patients. This study aimed to develop and validate a machine learning model for predicting IHCA and to identify key perioperative risk factors.Methods A multicenter retrospective cohort study was conducted among patients aged ≥65 years who underwent non-cardiac surgery at six tertiary hospitals between January 2020 and January 2025. Clinical, laboratory, and perioperative variables were collected. Five machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and K-Nearest Neighbor (KNN), were developed and compared. Model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration, decision curve analysis, cross-validation, and external validation. SHAP analysis was applied to interpret the optimal model.Results Among the five models, XGBoost demonstrated the best predictive performance, with AUCs of 0.967 and 0.960 in the training and validation cohorts, respectively. It also achieved the lowest Brier score and the highest clinical net benefit. External validation confirmed excellent discrimination (AUC=0.971) and calibration. SHAP analysis identified advanced age, intraoperative tachycardia, intraoperative hypotension, intraoperative blood transfusion, intraoperative hypoxemia, elevated postoperative neutrophil-to-lymphocyte ratio, B-type natriuretic peptide, lactate, and D-dimer levels as the most important predictors of IHCA.Conclusion An interpretable XGBoost-based model demonstrated excellent performance for predicting postoperative cardiac arrest in elderly patients. The identified risk factors reflected perioperative vulnerability, intraoperative hemodynamic instability, and postoperative physiological deterioration.
SN - 2296-858X
ER -