Assessment of machine learning classifiers for predicting intraoperative blood transfusion in non-cardiac surgery

Abstract

Background: This study aimed to develop a machine learning classifier for predicting intraoperative blood transfusion in non-cardiac surgeries.

Methods: Preoperative data from 6255 patients were extracted from the VitalDB database, an open-source registry. The primary outcome was the area under the receiver operating characteristic (AUROC) curve of ML classifiers in predicting intraoperative blood transfusion, defined as the receipt of at least one unit of packed red blood cells. Five different machine learning algorithms including logistic regression, random forest, adaptive boosting, gradient boosting, and the extremely gradient boosting classifiers were used to construct a binary classifier for intraoperative blood transfusion, and their predictive abilities were compared.

Results: 337 (5%) patients received intraoperative blood transfusion. In the test-set, the logistic regression classifier demonstrated the highest AUROC (0.836, 95% CI, 0.795-0.876), followed by the gradient boosting classifier (0.810, 95% CI, 0.750-0.868), AdaBoost classifier (0.776, 95% CI, 0.722-0.829), random forest classifier (0.735, 95% CI, 0.698-0.771), and XGBoost classifier (0.721, 95% CI, 0.695-0.747). The logistic regression classifier showed a higher AUROC compared to that of a multivariable logistic regression model (0.836 vs. 0.623, P < 0.001). Among various parameters used to construct the logistic regression classifier, the top three most important features were operation time (0.999), preoperative serum hemoglobin level (0.785), and open surgery (0.530).

Conclusion: We successfully developed various ML classifiers using readily available preoperative data to predict intraoperative transfusion in patients undergoing non-cardiac surgeries. In particular, the logistic regression classifier demonstrated the best performance in predicting intraoperative transfusion.