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

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.