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.

A total of 11727 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.

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.

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.