Derivation and validation of the SLNA score: a tumor size-location-number-apperance model to predict transurethral resection of bladder tumor (TURBT) complexity

SLNA评分的推导与验证:一种基于肿瘤大小-位置-数量-外观的模型,用于预测经尿道膀胱肿瘤切除术(TURBT)的复杂性

阅读:1

Abstract

BACKGROUND: Transurethral resection of bladder tumor (TURBT) is the core surgical procedure for the diagnosis and treatment of bladder cancer, with significant variations in its surgical complexity that directly affect the difficulty of surgical operation, the risk of perioperative complications, and the efficacy of postoperative management. This study aimed to develop a scoring system to evaluate the complexity of TURBT and provide a reference for preoperative assessment and postoperative management of bladder cancer patients. METHODS: A retrospective analysis was performed on 388 patients who underwent TURBT from January 2022 to June 2023. The complexity of TURBT was defined based on serious complications (Clavien-Dindo ≥3), operation time >50 minutes, and incomplete resection. A nomogram was constructed to predict complexity based on factors such as tumor size, number of tumors, tumor location, and recurrence. The entire cohort (n=388) was randomly partitioned into: a development/training set (70%, n=272) for model construction and an internal validation/testing set (30%, n=116) for performance assessment. Statistical analysis was conducted using univariate and multivariate logistic regression, and the accuracy was assessed using the receiver operating characteristic (ROC) curve. RESULTS: Of the 388 patients, 276 were classified as having non-complex TURBT, and 112 as complex. Factors significantly associated with complexity included larger tumor size, tumor location, age and gender. The nomogram achieved an area under the curve (AUC) of 0.92 [95% confidence interval (CI): 0.89-0.96] in the training set and 0.87 (95% CI: 0.78-0.96) in the validation set. CONCLUSIONS: The proposed nomogram effectively predicts the complexity of TURBT using preoperative clinical data. This scoring system can aid surgeons in preoperative planning and improve patient outcomes by standardizing the assessment of TURBT complexity across institutions.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。