Zhu Kun, Song Haifeng, Zhang Zhenan, Ma Binglei, Bao Xiaoyuan, Zhang Qian, Jin Jie
Department of Urology, Peking University First Hospital, Beijing 100034, China.
Institute of Urology, Peking University, Beijing 100034, China.
Transl Androl Urol. 2020 Jun;9(3):1232-1243. doi: 10.21037/tau.2020.03.45.
To analyze the incidence and risk factors of acute kidney injury (AKI) after partial nephrectomy (PN) in patients with solitary kidney, and to build AKI prediction models using logistic regression and machine learning (ML) approaches.
Clinical data of 87 solitary kidney patients with renal mass who received PN from January 2003 to March 2019 were collected. The diagnosis of AKI was based on KDIGO criteria. Logistic regression analysis and ML method were used to build prediction models.
AKI developed in 52 (59.8%) patients. The logistic regression model had three variables: ischemia time (P=0.003), surgery time (P=0.001) and preoperative fasted blood glucose level (FBG) (P=0.049). The area under curve (AUC) was 0.826, with the specificity and sensitivity of optimal threshold value 82.9% and 69.2%. The ML model had the following variables: ischemia time, surgery time, age, FBG, mean arterial pressure (MAP), colloid, crystalloid, etc. XGBoost model has the best prediction performance. The AUC was 0.749, lower than that of the logistic regression model with no statistical difference (P=0.258), with the specificity and sensitivity 62.9% and 84.6%, respectively.
The incidence of AKI after PN in patients with a solitary kidney was relatively high, it was associated with longer ischemia time, surgery time and higher FBG level, etc. The performance of ML model had no significant difference with logistic regression model. Prospective studies with larger sample sizes are awaited to test and verify our research findings.
分析孤立肾患者行部分肾切除术（PN）后急性肾损伤（AKI）的发生率及危险因素，并使用逻辑回归和机器学习（ML）方法建立AKI预测模型。
收集2003年1月至2019年3月期间87例接受PN治疗的孤立肾肾肿物患者的临床资料。AKI的诊断基于KDIGO标准。采用逻辑回归分析和ML方法建立预测模型。
52例（59.8%）患者发生AKI。逻辑回归模型有三个变量：缺血时间（P=0.003）、手术时间（P=0.001）和术前空腹血糖水平（FBG）（P=0.049）。曲线下面积（AUC）为0.826，最佳阈值的特异性和敏感性分别为82.9%和69.2%。ML模型有以下变量：缺血时间、手术时间、年龄、FBG、平均动脉压（MAP）、胶体、晶体等。XGBoost模型具有最佳预测性能。AUC为0.749，低于逻辑回归模型，但无统计学差异（P=0.258），特异性和敏感性分别为62.9%和84.6%。
孤立肾患者PN后AKI的发生率较高，与缺血时间延长、手术时间延长和FBG水平升高等有关。ML模型的性能与逻辑回归模型无显著差异。有待更大样本量的前瞻性研究来检验和验证我们的研究结果。
