Machine learning-driven personalized tranexamic acid administration recommendations improve perioperative outcomes in orthopedic surgery patients:A large-scale database study
10.16016/j.2097-0927.202507025
- VernacularTitle:机器学习驱动的个性化氨甲环酸用药推荐改善骨科手术患者围手术期预后:一项基于大样本数据库的研究
- Author:
Jian LI
1
;
Mi ZHOU
;
Xiang LIU
;
Yiziting ZHU
;
Xin SHU
;
Xuhao ZHANG
;
Wenquan HE
Author Information
1. 重庆市合川区人民医院麻醉科
- Keywords:
orthopedic surgery;
perioperative period;
tranexamic acid;
strategy recommendation
- From:
Journal of Army Medical University
2025;47(22):2868-2880
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a personalized recommendation strategy for tranexamic acid administration during the perioperative period of orthopedic surgery based on machine learning,aiming to reduce perioperative bleeding and related complications and improving clinical outcomes.Methods A total of 11 727 patients undergoing orthopedic surgery from the INSPIRE database were subjected in this study.Missing data were handled using multiple imputation methods,and relevant feature variables were screened using Boruta analysis.We constructed various machine learning models,including Gradient Boosting Machine(GBM),Generalized Linear Model(GLM),eXtreme Gradient Boosting(XGBoost),K-Nearest Neighbors(KNN),Neural Network(NNET),Naive Bayes(NB),and Random Forest(RF),to evaluate their performance in predicting intraoperative bleeding and prolonged postoperative length of hospital stay.The optimal model was then selected and further integrated using a weighted ensemble,aiming to achieve the best prognosis by recommending usage strategies for tranexamic acid.The predictive performance of the constructed model was then verified against the testing set,and compared with the physician decision-making to complete the evaluation.Results In predicting intraoperative bleeding,the RF model achieved an area under the receiver operating characteristic curve(AUC)of 0.73,which was significantly better than other models.In predicting the prolonged postoperative length of hospital stay,the XGBoost model performed the best,with an AUC value of 0.84.Based on the above best-performing models,an ensemble strategy was implemented.The patients who followed the recommended strategy had reduced intraoperative bleeding and shorter postoperative length of hospital stay.Conclusion The use of tranexamic acid is associated with intraoperative bleeding and postoperative length of hospital stay.Personalized decision-making recommendation based on our constructed model can effectively improve the outcomes of the patients undergoing orthopedic surgery.
