A prospective cohort study develops and validates a machine learning model for predicting ecchymosis after total knee arthroplasty.

ABSTRACT: Total knee arthroplasty (TKA) is a standard procedure for end-stage knee diseases, yet perioperative bleeding and its complications, such as ecchymosis, remain significant challenges. Predicting ecchymosis can guide personalized perioperative blood management, enhancing patient recovery and reducing the socioeconomic burden. In this prospective study (June 2023-March 2024), 416 TKA patients were split into ecchymosis (n = 135) and non-ecchymosis (n = 281) groups. The first seven months' data (312 patients) were used for training, and the next three months' (104 patients) for validation. Feature selection used Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest-Recursive Feature Elimination (RF-RFE), and BORUTA to identify key risk factors. The model was tested with advan

SUBMITTER: Luo X 

PROVIDER: S-EPMC12830954 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature