Development of Machine Learning Models for Predicting the 1-Year Risk of Reoperation After Lower Limb Oncological...

Oncological resection and reconstruction involving the lower extremities commonly lead to reoperations that impact patient outcomes and healthcare resources. This study aimed to develop a machine learning (ML) model to predict this reoperation risk.

This study was conducted according to TRIPOD + AI. Data from the PARITY trial was used to develop ML models to predict the 1-year reoperation risk following lower extremity oncological resection and reconstruction. Six ML algorithms were tuned and calibrated based on fivefold cross-validation. The best-performing model was identified using classification and calibration metrics.

The polynomial support vector machine (SVM) model was chosen as the best-performing model. During internal validation, the SVM exhibited an AUC-ROC of 0.73 and a Brier score of 0.17. Using an optimal threshold that balances all quadrants of the confusion matrix, the SVM exhibited a sensitivity of 0.45 and a specificity of 0.81. Using a high-sensitivity threshold, the SVM exhibited a sensitivity of 0.68 and a specificity of 0.68. Total operative time was the most important feature for reoperation risk prediction.

The models may facilitate reoperation risk stratification, allowing for better patient counseling and for physicians to implement measures that reduce surgical risks.