Interpretable Machine Learning Analysis of Factors Associated with Postoperative Hemoglobin Reduction After Total...

Background: Postoperative hemoglobin (Hb) reduction reflects the physiologic extent of perioperative blood loss after total knee arthroplasty (TKA). Whereas previous studies have relied on transfusion as a binary endpoint, transfusion decisions are highly variable across institutions, obscuring the underlying hematologic trajectory. This study aimed to develop and interpret machine learning (ML) models to characterize and quantify the determinants of postoperative Hb reduction in a standardized cohort of non-transfused TKA patients. Methods: A retrospective cohort of 866 patients who underwent primary TKA under a standardized operative protocol-with identical cemented posterior-stabilized implants and uniform cementing technique-was analyzed (1 January 2014-31 March 2024). During the study period, a consistent 1 g intra-articular tranexamic acid (TXA) regimen administered through the drain was introduced and applied to a subset of patients, allowing TXA use to be modeled as a binary predictor. Four ML algorithms (Linear Regression, Random Forest, XGBoost, and Stacking Regressor) were trained using preoperative, demographic, and perioperative variables. Fivefold cross-validation assessed model performance, and SHapley Additive exPlanations (SHAP) values were used to identify influential predictors and enhance interpretability. Results: Across all ML models, preoperative Hb emerged as the strongest determinant of postoperative Hb reduction, followed by TXA use, body mass index (BMI), and platelet count. Ensemble models captured non-linear and interacting effects more effectively than linear regression. Test-set performance was modest (best R² = 0.330), consistent with the influence of unmeasured physiologic factors such as hidden blood loss, fluid dynamics, and inflammatory responses. Accordingly, the primary value of the framework lies in the exploratory and transparent assessment of determinant importance rather than in individual-level prediction. Conclusions: This study provides an interpretable, exploratory ML framework for identifying factors associated with percentage Hb reduction after TKA. Preoperative Hb was the dominant determinant, while TXA use and higher BMI were recurrently associated with smaller predicted percentage reductions. Given the modest test-set performance and the absence of external validation and clinical utility assessment, the models should not be interpreted as tools for individual-level prediction or clinical decision making.