Machine learning for the prediction of blood transfusion risk during or after mitral valve surgery: a multicenter retrospective cohort study.

This study aimed to identify the optimal prediction method and key preoperative variables for red blood cell (RBC) transfusion risk in patients undergoing mitral valve surgery. We conducted a retrospective study involving 1477 patients from eight large tertiary hospitals in China who underwent mitral valve surgery with cardiopulmonary bypass. From thirty collected preoperative variables, the Max-Relevance and Min-Redundancy (mRMR) method was used for feature selection, and various machine learning models were evaluated. Of the 1477 patients, 862 received RBC transfusions. The mRMR method identified ten significant preoperative variables. The LightGBM model demonstrated superior performance, achieving an area under the curve (AUC) of 0.935 in the training set and 0.734 in the validation set, with 74.2% accuracy in a prospective dataset. SHAP analysis revealed the ten most influential variables were hematocrit, RBC count, weight, body mass index, fibrinogen, hemoglobin, height, age, left ventricular dilation, and sex. In conclusion, LightGBM was identified as the optimal model for predicting RBC transfusion needs. The model's high accuracy can assist clinicians in anticipating transfusions and improving blood management decisions.