老年前列腺增生患者急性尿潴留发生风险预测模型的建立及内部验证

Establishment and internal validation of a risk prediction model for acute urinary retention in elderly patients with benign prostatic hyperplasia

摘要

目的 分析老年良性前列腺增生（BPH）患者发生急性尿潴留（AUR）的独立危险因素，构建列线图预测模型并验证其临床应用价值。方法 回顾性收集2020年1月1日-2024年12月31日贵黔国际医院泌尿外科收治的654例老年BPH患者的临床资料，根据是否发生AUR分为AUR组（185例）与非AUR组（469例）。采用倾向性评分匹配（PSM）法对两组患者身体质量指数、糖尿病史、高血压史、肺部感染史、慢性阻塞性肺疾病史进行1∶1匹配，匹配成功178对。基于匹配后数据进行多因素logistic回归筛选独立危险因素并构建列线图模型，采用Bootstrap法进行内部验证，Hosmer-Lemeshow检验和校准曲线评价模型的一致性和校准度，决策曲线分析（DCA）评价模型的临床有效性，并绘制受试者工作特征（ROC）曲线，计算曲线下面积（AUC）以评估模型的区分度，并根据最大约登指数进行危险度分组。结果 多因素logistic回归分析显示，前列腺特异性抗原密度（PSAD）（OR=2.593，95%CI:1.647～4.083）、红细胞分布宽度与白蛋白比值（RAR）（OR=5.453，95%CI:1.682～17.678）、前列腺上下径（SI）（OR=1.822，95%CI:1.415～2.346）是老年BPH患者发生AUR的独立危险因素（均P＜0.05）。基于此构建老年BPH患者AUR发生风险列线图预测模型（PRS评分模型），该模型的AUC为0.722（95%CI:0.669～0.775）。校准曲线分析显示，该模型预测结果与实际结果之间具有较好的一致性（Hosmer-Lemeshow拟合优度检验，χ²=4.685，P=0.791）。DCA提示预测阈值为0.3～0.9时，PRS评分模型可获得显著临床净收益。根据最大约登指数确定该模型总分最佳截断值为80.59分，据此可将患者分为高危组（PRS评分≥80.59分）与低危组（PRS评分＜80.59分）。结论 基于PSAD、RAR、SI构建的PRS评分模型具有良好的区分度、校准度及临床有效性，可为早期识别老年BPH发生AUR的高危人群提供参考。

Abstract

Objective To explore the independent risk factors for acute urinary retention（AUR）in elderly patients with benign prostatic hyperplasia（BPH），construct a nomogram prediction model，and validate its clinical utility. Methods Clinical data of 654 elderly BPH patients treated in our hospital during Jan.1，2020 and Dec.31，2024 were retrospectively collected. Patients were divided into the AUR group（n=185）and the non-AUR group（n=469）according to whether AUR occurred. A 1∶1 propensity score matching（PSM）was performed，with body mass index，history of diabetes，history of hypertension，history of pulmonary infection，and history of chronic obstructive pulmonary disease as covariates，yielding 178 matched pairs. Based on the matched data，multivariate logistic regression was used to identify independent risk factors，and a nomogram model was constructed. Internal validation was conducted using the Bootstrap method. The consistency and calibration of the model were evaluated with the Hosmer-Lemeshow test and calibration curve，and clinical utility was assessed with decision curve analysis（DCA）. The receiver operating characteristic（ROC）curve was plotted，and the area under the curve（AUC）was calculated to assess discrimination. Risk stratification was performed based on the maximum Youden index. Results Multivariate logistic regression analysis identified prostate-specific antigen density（PSAD，OR=2.593，95%CI:1.647-4.083），red blood cell distribution width to albumin ratio（RAR，OR=5.453，95%CI:1.682-17.678），and prostate sagittal diameter（SI，OR=1.822，95%CI:1.415-2.346）as independent risk factors for AUR in elderly BPH patients（all P＜0.05）. Based on these factors，a prediction model for AUR in elderly BPH patients，abbreviated as the“PRS scoring model，” was constructed. The AUC of the model was 0.722（95% CI:0.669-0.775）. Calibration curve analysis demonstrated good consistency between the predicted and observed outcomes（Hosmer-Lemeshow goodness-of-fit test:χ ²=4.685，P=0.791）. DCA indicated that the PRS scoring model yielded significant net clinical benefit when the threshold probability ranged from 0.3 to 0.9. The optimal cutoff value of the total nomogram score determined with the Youden index was 80.59，based on which patients were classified into a high-risk group（PRS score ≥80.59）and a low-risk group（PRS score＜80.59）. Conclusion The PRS scoring model based on PSAD，RAR，and SI demonstrates good discrimination，calibration，and clinical utility，and may serve as a reference for early identification of elderly BPH patients at high risk of developing AUR.

关键词

老年 / 前列腺增生 / 急性尿潴留 / 倾向性评分匹配 / 列线图预测模型

Key words

elderly / benign prostatic hyperplasia / acute urinary retention / propensity score matching / nomogram prediction model

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基金资助

贵阳市高层次创新型青年卫生人才培养计划项目(No.2024筑卫健科技合同字第014号)