School of Medicine, Tongji University, Shanghai, China.
Department of Urology, Chongming Branch, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Ren Fail. 2024 Dec;46(2):2394634. doi: 10.1080/0886022X.2024.2394634. Epub 2024 Aug 23.
This study aims to identify risk factors for acute kidney injury (AKI) in patients with ureterolithiasis and to develop a predictive model for early AKI detection in this population.
A retrospective analysis was conducted on data from 1,016 patients with ureterolithiasis who presented to our outpatient emergency department between January 2021 and December 2022. Using multifactorial logistic regression, we identified independent risk factors for AKI and constructed a nomogram to predict AKI risk. The predictive model's efficacy was assessed through the area under the ROC curve, calibration curves, Hosmer-Lemeshow (HL) test, and decision curve analysis (DCA).
AKI was diagnosed in 18.7% of the patients. Independent risk factors identified included age, fever, diabetes, hyperuricemia, bilateral calculi, functional solitary kidney, self-medication, and prehospital delay. The nomogram demonstrated excellent discriminatory capabilities, with AUCs of 0.818 (95% CI, 0.775-0.861) for the modeling set and 0.782 (95% CI, 0.708-0.856) for the validation set. Both calibration curve and HL test results confirmed strong concordance between the model's predictions and actual observations. DCA highlighted the model's significant clinical utility.
The predictive model developed in this study provides clinicians with a valuable tool for early identification and management of patients at high risk for AKI, thereby potentially enhancing patient outcomes.
本研究旨在确定输尿管结石患者发生急性肾损伤（AKI）的危险因素，并为该人群中早期 AKI 的检测建立预测模型。
对 2021 年 1 月至 2022 年 12 月在我院门诊急诊就诊的 1016 例输尿管结石患者的数据进行回顾性分析。采用多因素逻辑回归分析，确定 AKI 的独立危险因素，并构建预测 AKI 风险的列线图。通过 ROC 曲线下面积、校准曲线、Hosmer-Lemeshow（HL）检验和决策曲线分析（DCA）评估预测模型的效能。
18.7%的患者被诊断为 AKI。确定的独立危险因素包括年龄、发热、糖尿病、高尿酸血症、双侧结石、功能性孤立肾、自行用药和院前延误。该列线图显示出良好的判别能力，模型构建集的 AUC 为 0.818（95%CI，0.775-0.861），验证集的 AUC 为 0.782（95%CI，0.708-0.856）。校准曲线和 HL 检验结果均证实了模型预测与实际观察之间的高度一致性。DCA 强调了该模型的显著临床实用性。
本研究建立的预测模型为临床医生提供了一种有价值的工具，可用于早期识别和管理 AKI 风险较高的患者，从而有可能改善患者的结局。
