Predicting complications after laparoscopic surgery for ureteropelvic junction obstruction using machine learning models: a retrospective cohort study.

IF 2.8 2区 医学 Q2 UROLOGY & NEPHROLOGY World Journal of Urology Pub Date : 2025-03-18 DOI:10.1007/s00345-025-05552-1
Xintao Zhang, Dong Sun, Yu Zhou, Qiongqian Xu, Xue Ren, Jichang Han, Chuncan Ma, Guohua Ma, Zhihao Sun, Yu Jia, Zhihang Zhou, Xiaoyang Liu, Qiangye Zhang, Aiwu Li
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Abstract

Purposes: Postoperative complications in patients with ureteropelvic junction obstruction (UPJO) negatively impact surgical outcomes and may necessitate redo surgery. We aimed to predict the occurrence of postoperative complications in these patients using machine learning algorithms.

Methods: Data of UPJO patients admitted to our hospital for surgical treatment from May 2014 to May 2023 were retrospectively analyzed. Risk factors were screened using multivariate logistic and Lasso regression. Logistic regression (LR), k-nearest neighbours (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB) and Neural Network (NN) were used to create a prediction model.

Results: 526 patients were included, with 97 complications (61 urinary tract infections [UTI] and 36 recurrences). Risk factors for postoperative complications of pyeloplasty were preoperative UTI (Pre-UTI), calculus, renal cortical thickness (RCT), collecting system, time of removal of DJ, removal of drainage, and white blood cell count (WBC). Factors associated with post-UTI were p-UTI, RCT, collecting system, time of removal of DJ, and WBC. Factors influencing postoperative recurrence were p-UTI, calculus, RCT, and drainage removal. Finally, LR was selected to develop the clinical prediction model for postoperative complications, UTIs, and recurrence (area under the curve: 0.929, 0.941, and 0.894, respectively). The present study is the first predictive model on total complications, UTI and recurrence after pyeloplasty and demonstrated strong predictive results. However, there are some limitations; this is a single-center study, and the model has not undergone external validation, which may affect the generalizability of our findings.

Conclusion: UPJO postoperative complications, UTI, and recurrence can be predicted prior to surgery by machine learning.

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利用机器学习模型预测输尿管盆腔交界处梗阻腹腔镜手术后的并发症:一项回顾性队列研究。
目的:输尿管肾盂交界处梗阻(UPJO)患者术后并发症会对手术效果产生负面影响,可能需要重新进行手术。我们旨在利用机器学习算法预测这些患者术后并发症的发生率:我们对 2014 年 5 月至 2023 年 5 月期间本院收治的接受手术治疗的 UPJO 患者的数据进行了回顾性分析。使用多变量逻辑回归和拉索回归筛选风险因素。使用逻辑回归(LR)、k-近邻(KNN)、支持向量机(SVM)、决策树(DT)、随机森林(RF)、极梯度提升(XGB)和神经网络(NN)创建预测模型:共纳入 526 例患者,其中 97 例出现并发症(61 例尿路感染 [UTI] 和 36 例复发)。肾盂成形术术后并发症的风险因素包括术前UTI(前UTI)、结石、肾皮质厚度(RCT)、收集系统、移除DJ的时间、移除引流管和白细胞计数(WBC)。与UTI后相关的因素有UTI前、RCT、收集系统、切除DJ的时间和白细胞计数。影响术后复发的因素有 p-UTI、结石、RCT 和引流管清除。最后,选择 LR 建立术后并发症、UTI 和复发的临床预测模型(曲线下面积分别为 0.929、0.941 和 0.894)。本研究是首个关于肾盂成形术后总并发症、UTI 和复发的预测模型,并显示出很强的预测效果。但也存在一些局限性:这是一项单中心研究,模型没有经过外部验证,这可能会影响我们研究结果的推广性:结论:UPJO术后并发症、UTI和复发可在手术前通过机器学习进行预测。
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来源期刊
World Journal of Urology
World Journal of Urology 医学-泌尿学与肾脏学
CiteScore
6.80
自引率
8.80%
发文量
317
审稿时长
4-8 weeks
期刊介绍: The WORLD JOURNAL OF UROLOGY conveys regularly the essential results of urological research and their practical and clinical relevance to a broad audience of urologists in research and clinical practice. In order to guarantee a balanced program, articles are published to reflect the developments in all fields of urology on an internationally advanced level. Each issue treats a main topic in review articles of invited international experts. Free papers are unrelated articles to the main topic.
期刊最新文献
Exime prostatic stent in acute and chronic urinary retention. Tauber's antegrade sclerotherapy for the treatment of varicocele in children and adolescents. Does the pubertal stage matter? Early angiography improves postoperative clinical outcomes compared to delayed angiography among patients with vascular pathologies following partial nephrectomy. Evaluation of ureteral injury using the PULS grading system in patients undergoing semi-rigid and flexible ureteroscopy. Predicting complications after laparoscopic surgery for ureteropelvic junction obstruction using machine learning models: a retrospective cohort study.
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