Construction and validation of a perioperative blood transfusion model for patients undergoing total hip arthroplasty with osteonecrosis of the femoral head based on machine learning.

BACKGROUND

This study aimed to construct a predictive model utilizing multiple machine learning (ML) models to estimate the likelihood of perioperative blood transfusion in patients with osteonecrosis of the femoral head (ONFH) who underwent total hip arthroplasty (THA).

METHODS

Patients diagnosed with ONFH who underwent THA at our institution between October 2018 and October 2023 were included in the study. Feature selection was conducted using Lasso regression and correlation analysis. An unbiased evaluation framework incorporating nested resampling was established to assess four ML models. A nomogram was subsequently developed based on the selected features.

RESULTS

Seven features were identified, namely blood loss, hemoglobin (HGB) levels, weight, body temperature, systolic pressure, and direct bilirubin. Four ML models were constructed based on these features. The area under the curve (AUC) values for Random Forest, Extreme Gradient Boosting, Light Gradient Boosting Machine, and Logistic Regression (LR) were 1.00, 1.00, 1.00, and 0.93 in the internal validation set, and 0.89, 0.90, 0.88, and 0.91 in the external test set, respectively. Furthermore, a nomogram model based on LR was developed using the aforementioned seven features, yielding AUC values of 0.95 and 0.90 for the training and test sets, respectively, thereby surpassing the AUC values of preoperative HGB levels (0.80 and 0.76).

CONCLUSION

Both the ML models and the nomogram exhibit significant potential for forecasting the likelihood of perioperative blood transfusion in patients with ONFH undergoing THA, which may aid clinicians in improving the accuracy of blood transfusion predictions.