An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery

Abstract

Aim: The study aimed to develop a predictive model with machine learning (ML) algorithm, to predict and manage the need for red blood cell (RBC) transfusion during hip fracture surgery.

Methods: Data of 2785 cases that underwent hip fracture surgery from April 2016 to May 2022 were collected, covering demographics, medical history and comorbidities, type of surgery and preoperative laboratory results. The primary outcome was the intraoperative RBC transfusion. The predicting performance of six algorithms were respectively evaluated with the area under the receiver operating characteristic (AUROC). The SHapley Additive exPlanations (SHAP) package was applied to interpret the Random Forest (RF) model. Data from 122 patients at The Third Affiliated Hospital of Sun Yat-sen University were collected for external validation.

Results: 1417 patients (50.88%) were diagnosed with preoperative anemia (POA) and 209 patients (7.5%) received intraoperative RBC transfusion. Longer estimated duration of surgery, POA, older age, hypoproteinemia, and surgery of internal fixation were revealed as the top 5 important variables contributing to intraoperative RBC transfusion. Among the six ML models, the RF model performed the best, which achieved the highest AUC (0.887, CI 0.838 to 0.926) in the internal validation set. Further, it achieved a comparable AUC of 0.834(0.75, 0.911) in the external validation set.

Conclusion: Our study firstly demonstrated that the RF model with 10 common variables might predict intraoperative RBC transfusion in hip fracture patients.

Keywords: Hip fracture; Machine learning; Predictive model; Red blood cell transfusion; SHapley Additive exPlanations.

Fig. 1

The framework for developing an ML-based predictive model

Fig. 2

Primary predictive variables screening based on LASSO regression. A The horizontal axis lambda represents the penalty term in LASSO, and the vertical axis represents the coefficients of the variables in the LASSO regression. The closer the lambda value is to 1, the smaller the coefficients of the variables in the LASSO regression, and vice versa. The optimal lambda value of 0.00003 was obtained by cross validation method, at which point the coefficients of 10 variables were not equal to 0, thus resulting in 10 variables being selected by the LASSO. B The horizontal axis represents the coefficients of the variables in the LASSO regression, where the coefficient of Hypertension is 0, and the coefficients of other variables are not 0, thus resulting in 10 variables being selected by the LASSO variable selection

Fig. 3

Receiver operating characteristic curves for the machine learning model and logistic regression, Extreme Gradient Boosting(XGBOOST), Gradient Boosting Decision Tree(GBDT), Support Vector Machine(SVM), Multi-Layer Perceptron(MLP), Logistic Regression(LR), Radom Forest(RF)

Fig. 4

Performance of RF model on the internal validation set and on the external validation set

Fig. 5

Feature importance ranking based on Shapley Addictive exPlanations (SHAP) values in RF model

Fig. 6

Examples of website usage. Entering the input value determined the transfusion requirements and displayed how each value contributed to the prediction. A Patient No.1 needs RBC transfusion; B Patient No.2 does not need RBC transfusion