Abstract
Background
This study aimed to develop and validate an interpretable machine learning model to predict postoperative fever (POF) after flexible ureteroscopic lithotripsy (fURL).
Objective
This study adopted a single-center retrospective temporal cohort design. Strictly adhering to predefined inclusion and exclusion criteria, patients with upper urinary tract stones (maximum diameter
Results
Among the six machine learning algorithms, The LightGBM model yielded the optimal discriminative metrics in this model comparison, including the maximum AUC (0.896, 95% CI: 0.853–0.940) and PR-AUC (0.787), as well as the highest specificity (0.967) and accuracy (0.891). DeLong pairwise comparisons demonstrated that LightGBM was statistically superior to four competing models; however, the AUC difference between LightGBM and XGBoost did not reach statistical significance. The SHAP analysis further clarifies the contribution of each variable in the prediction process. Temporal validation revealed acceptable temporal generalization performance of the LightGBM predictive model, with an AUC of 0.723 (95% CI: 0.648–0.799).
Conclusion
This study employed an interpretable machine learning model to effectively predict POF following fURL. This model provides a reference for surgical risk stratification, which helps establish personalized postoperative monitoring strategies.