Tang Cheng, Liu Chengmeng, Jiang Weimin, Zhou Xing, Yang Guang, Xu Yong
Department of Urology, Xiangya Hospital Zhuzhou Central South University, Zhuzhou, China.
Department of Surgery, Xiangya Hospital Zhuzhou Central South University, Zhuzhou, China.
Transl Androl Urol. 2025 Aug 30;14(8):2171-2184. doi: 10.21037/tau-2025-244. Epub 2025 Aug 26.
Urinary stones are a common condition with increasing prevalence worldwide. Predicting the type of urinary stones is essential for guiding treatment, yet complex imaging models are not always accessible. This study aims to identify simpler clinical predictors and explore the risk factors for infected urinary stones using statistical and receiver operating characteristic (ROC) analyses.
A retrospective analysis was conducted on 1,067 patients with urinary stones who underwent surgical treatment between 2018 and 2023. Patients were classified into infected and non-infected stone groups based on stone composition. Logistic regression analysis adjusted for age, gender, body mass index (BMI), and serum electrolytes was performed to identify significant predictors, with sensitivity analyses using inverse probability weighting (IPW) to address cohort imbalance. The predictive performance of key factors was assessed using ROC curves.
Of the total cohort, 686 (64.3%) had infectious stones, and 381 (35.7%) had non-infectious stones. Infectious stones were more common in females, younger patients, and those with ureteral stones. Preoperative urine cultures revealed (24.3%) and (18.2%) as predominant pathogens. Multivariate analysis identified three independent predictors for infectious stones: alkaline urine pH [hazard ratio (HR) 2.54, 95% confidence interval (CI): 1.33-4.88, P<0.001], ureteral stone location (HR 5.60, 95% CI: 2.38-13.17, P<0.001), and absence of diabetes mellitus (HR 4.74, 95% CI: 1.50-15.03, P=0.01). Sensitivity analyses confirmed robustness (adjusted HRs: 2.49, 5.58 and 4.65, respectively). Among these, ureteral stone location had the best predictive performance [area under the curve (AUC) =0.782, sensitivity 84.0%, specificity 71.5%], followed by urine pH (AUC =0.766, sensitivity 79.3%, specificity 68.7%), while diabetes status showed weaker predictive ability (AUC =0.623).
Alkaline urine pH and ureteral stone location are strong predictors of infected urinary stones, while diabetes status is less predictive. These findings highlight the importance of integrating simple clinical parameters to improve the preoperative assessment and management of patients with urinary stones, particularly in resource-limited settings.
尿路结石是一种常见疾病，在全球范围内患病率呈上升趋势。预测尿路结石的类型对于指导治疗至关重要，但复杂的成像模型并非总是可用。本研究旨在通过统计分析和受试者工作特征（ROC）分析，确定更简单的临床预测因素，并探讨感染性尿路结石的危险因素。
对2018年至2023年间接受手术治疗的1067例尿路结石患者进行回顾性分析。根据结石成分将患者分为感染性结石组和非感染性结石组。进行逻辑回归分析，并对年龄、性别、体重指数（BMI）和血清电解质进行校正，以确定显著的预测因素，并使用逆概率加权（IPW）进行敏感性分析以解决队列不平衡问题。使用ROC曲线评估关键因素的预测性能。
在整个队列中，686例（64.3%）患有感染性结石，381例（35.7%）患有非感染性结石。感染性结石在女性、年轻患者和输尿管结石患者中更为常见。术前尿培养显示（24.3%）和（18.2%）为主要病原体。多变量分析确定了感染性结石的三个独立预测因素：碱性尿液pH值[风险比（HR）2.54，95%置信区间（CI）：1.33 - 4.88，P<0.001]、输尿管结石位置（HR 5.60，95% CI：2.38 - 13.17，P<0.001）和无糖尿病（HR 4.74，95% CI：1.50 - 15.03，P = 0.01）。敏感性分析证实了结果的稳健性（调整后的HR分别为2.49、5.58和4.65）。其中，输尿管结石位置的预测性能最佳[曲线下面积（AUC）=0.782，敏感性84.0%，特异性71.5%]，其次是尿液pH值（AUC =0.766，敏感性79.3%，特异性68.7%），而糖尿病状态的预测能力较弱（AUC =0.623）。
碱性尿液pH值和输尿管结石位置是感染性尿路结石的强预测因素，而糖尿病状态的预测性较差。这些发现强调了整合简单临床参数以改善尿路结石患者术前评估和管理的重要性，特别是在资源有限的环境中。
