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<p>This study aimed to construct machine learning (ML) models to predict in-hospital death in patients with intra-aortic balloon pump (IABP) support during cardiac surgery. 297 patients were enrolled and divided into the survival and death groups. Eight ML models were developed and validated, with performance evaluated using the area under the receiver operating characteristic curve (AUC). Feature importance was analyzed using Shapley Additive exPlanations (SHAP). 70 (23.57%) patients were in the death group. The XGBoost model achieved the best predictive ability, with an AUC of 0.91 in the training set and 0.83 in the testing set. SHAP analysis identified low-density lipoprotein cholesterol (LDLC), intensive care unit stay on postoperative day 5 (ICU 5d), blood urea nitrogen (BUN), cardiac troponin T (cTnT), continuous renal replacement therapy (CRRT), body mass index (BMI), mechanical ventilation support on postoperative day 5 (MV 5d), and N-terminal pro-B-type natriuretic peptide (NT-pro BNP) as the top predictors. SHAP force plots demonstrated individualized prediction. The XGBoost model predicts the risk of in-hospital death in patients, possibly contributing to improving perioperative management and reducing mortality.</p>

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Application of supervised machine learning algorithms to predict in-hospital death risk in patients receiving intra-aortic balloon pump therapy during the perioperative period of cardiac surgery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • Changqing Yang,
  • Han Xie,
  • Zhou Hong,
  • Luo Li,
  • Quanye Li,
  • Peng Zheng,
  • Qin Yin

摘要

This study aimed to construct machine learning (ML) models to predict in-hospital death in patients with intra-aortic balloon pump (IABP) support during cardiac surgery. 297 patients were enrolled and divided into the survival and death groups. Eight ML models were developed and validated, with performance evaluated using the area under the receiver operating characteristic curve (AUC). Feature importance was analyzed using Shapley Additive exPlanations (SHAP). 70 (23.57%) patients were in the death group. The XGBoost model achieved the best predictive ability, with an AUC of 0.91 in the training set and 0.83 in the testing set. SHAP analysis identified low-density lipoprotein cholesterol (LDLC), intensive care unit stay on postoperative day 5 (ICU 5d), blood urea nitrogen (BUN), cardiac troponin T (cTnT), continuous renal replacement therapy (CRRT), body mass index (BMI), mechanical ventilation support on postoperative day 5 (MV 5d), and N-terminal pro-B-type natriuretic peptide (NT-pro BNP) as the top predictors. SHAP force plots demonstrated individualized prediction. The XGBoost model predicts the risk of in-hospital death in patients, possibly contributing to improving perioperative management and reducing mortality.