Wu Yifan, Gong Jue, Qiu Chenyu, Yin Guangming, Yuan Peng
Department of Urology, Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
Eur Urol Open Sci. 2025 Jul 25;79:9-16. doi: 10.1016/j.euros.2025.07.001. eCollection 2025 Sep.
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.
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.
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.
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.
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.
本研究旨在建立一种列线图，用于评估接受经皮肾镜取石术患者术后大出血的风险。
这项回顾性研究纳入了2022年1月至2024年6月在中南大学湘雅三医院接受单侧经皮肾镜取石术治疗肾结石的493例患者。患者按时间顺序分为训练集（前70%接受治疗的患者）和验证集（后30%）。采用最小绝对收缩和选择算子以及多变量逻辑回归来确定大出血的独立预测因素，并在此基础上构建列线图。
本研究对493例患者进行了回顾性分析，其中86例发生大出血。确定了六个独立危险因素：孤立肾、肾实质厚度、手术分期、手术入路、通道大小和估计肾小球滤过率。该列线图在训练集中的曲线下面积为0.81（95%置信区间[CI]：0.76 - 0.87），在验证集中为0.84（95%CI：0.75 - 0.92）。决策曲线分析表明，当训练集中概率阈值设定在0.04至0.89之间时，该列线图在预测出血风险方面具有临床价值。局限性包括缺乏外部验证，这可能会影响模型的通用性。
该列线图是预测经皮肾镜取石术后大出血风险及辅助术前评估的有效工具，有助于外科医生识别高危患者，从而做出更明智的决策。
该研究确定了预测经皮肾镜取石术后大出血的关键因素，包括肾脏状况和手术技术等方面。该模型可帮助外科医生识别出血风险较高的患者，提高手术安全性。
