Nomogram for Predicting Postoperative Major Bleeding in Percutaneous Nephrolithotomy: A Retrospective Study.

Background And Objective: The aim of this study was to develop a nomogram for estimating the risk of postoperative major bleeding in patients who underwent surgery for percutaneous nephrolithotomy.

Methods: This retrospective study included 493 patients who had undergone unilateral percutaneous nephrolithotomy for kidney calculi at Third Xiangya Hospital from January 2022 to June 2024. Patients were temporally divided into a training set (the first 70% of treated patients) and a validation set (the last 30%). The least absolute shrinkage and selection operator, and multivariable logistic regression were employed to identify independent predictors of major bleeding upon which a nomogram was based.

Key Findings And Limitations: This study retrospectively analyzed 493 patients, of whom 86 suffered from major bleeding. Six independent risk factors were identified: solitary kidney, renal parenchymal thickness, surgical staging, surgical approach, tract size, and estimated glomerular filtration rate. The nomogram achieved an area under the curve of 0.81 (95% confidence interval [CI]: 0.76-0.87) in the training set and that of 0.84 (95% CI: 0.75-0.92) in the validation set. A decision curve analysis indicated that the nomogram is clinically valuable for predicting bleeding risk when the probability threshold is set between 0.04 and 0.89 in the training set. Limitations include the lack of external validation, which may affect the generalizability of the model.

Conclusions And Clinical Implications: The nomogram serves as an effective tool for predicting the risk of major bleeding after percutaneous nephrolithotomy and for aiding in preoperative assessments, helping surgeons identify high-risk patients for more informed decision-making.

Patient Summary: The study has identified the key factors that predict major bleeding following percutaneous nephrolithotomy, including the aspects of kidney condition and surgical techniques. This model can assist surgeons in identifying patients at a higher risk of bleeding and in enhancing surgical safety.