Machine Learning for Prediction of Blood Transfusion Rates in Primary Total Knee Arthroplasty

Abstract

Acute blood loss anemia requiring allogeneic blood transfusion with inherent risks is still a postoperative complication of total knee arthroplasty (TKA). This study aimed to use machine learning models for the prediction of blood transfusion following primary TKA and to identify contributing factors. A total of 1328 patients who underwent primary TKA in our institute were evaluated using data extracted MARQCI database to identify patient demographics and surgical variables that may be associated with blood transfusion. Multilayer perceptron neural networks (MPNN) machine learning algorithm was used to predict transfusion rates and the importance of factors associated with blood transfusion following TKA. Statistical analyses including bivariate correlate analysis, Chi-Square test, and t test were performed for demographic analysis and to determine the correlation between blood transfusion and other variables. Results demonstrated important factors associated with transfusion rates include pre- and post-operative hemoglobin level, ASA score, tranexamic acid usage, age, BMI and other factors. The MPNN machine learning achieved excellent performance across discrimination (AUC=0.997). This study demonstrated that MPNN for the prediction of patient-specific blood transfusion rates following TKA represented a novel application of machine learning with the potential to improve pre-operative planning for treatment outcome.