Development of machine learning and multivariable models for predicting blood transfusion in head and neck microvascular reconstruction for risk-stratified patient blood management

Abstract

Background: Although blood transfusions have adverse consequences for microvascular head and neck reconstruction, they are frequently administered. Pre-identifying patients would allow risk-stratified patient blood management. Methods: Development of machine learning (ML) and logistic regression (LR) models based on retrospective inclusion of 657 patients from 2011 to 2021. Internal validation and comparison with models from the literature by external validation. Development of a web application and a score chart. Results: Our models achieved an area under the receiver operating characteristic curve (ROC-AUC) of up to 0.825, significantly outperforming LR models from the literature. Preoperative hemoglobin, blood volume, duration of surgery and flap type/size were strong predictors. Conclusions: The use of additional variables improves the prediction for blood transfusion, while models seems to have good generalizability due to surgical standardization and underlying physiological mechanism. The ML models developed showed comparable predictive performance to an LR model. However, ML models face legal hurdles, whereas score charts based on LR could be used after further validation.