Chen Wenwei, He Yanfeng, Lu Kaixin, Liu Changyi, Jiang Tao, Zhang Hua, Gao Rui, Xue Xueyi
Department of Urology, the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China.
Department of Urology, Binhai Campus of the First Affiliated Hospital of Fujian Medical University, National Regional Medical Center, Fuzhou 350212, China.
Zhejiang Da Xue Xue Bao Yi Xue Ban. 2024 Jan 25;54(1):99-107. doi: 10.3724/zdxbyxb-2024-0128.
To analyze the association of serum heparin-binding protein (HBP) and C-reactive protein (CRP) levels with urosepsis following flexible ureteroscopic lithotripsy (FURL) and to construct a back propagation neural network prediction model.
A total of 428 patients with kidney stones who underwent FURL were enrolled. Patients were divided into sepsis group (=42) and control group (=386) according to whether post-operative urosepsis developed. Logistic regression analysis was used to determine the risk factors of post-FURL urosepsis and their interactions. A logistic regression model and a back propagation neural network model were developed for predicting post-FURL urosepsis following FURL, and their predictive performance was evaluated using receiver operating characteristic curves.
Univariate analysis showed that stone surgery history, gender, positive urine culture, stone diameter, diabetes, operation time, white blood cell (WBC), platelet, CRP, and HBP levels were significantly associated with post-FURL urosepsis (all <0.05). Multivariate analysis identified positive urine culture, CRP, and HBP levels as independent risk factors for post-FURL urosepsis (all <0.05). Interaction analysis revealed that CRP and HBP showed both additive (=8.453, 95%: 2.645-16.282; =0.696, 95%: 0.131-1.273; =3.369, 95%: 1.176-7.632) and multiplicative (=1.754, 95%: 1.218-3.650) interactions, while CRP and urine culture demonstrated multiplicative interaction (=2.449, 95%: 1.525-3.825). The back propagation neural network model demonstrated superior predictive performance compared to the logistic regression model.
CRP and HBP levels are independent risk factors for post-FURL urosepsis. The back propagation neural network model based on CRP and HBP exhibits higher predictive accuracy than the logistic regression model, which may provide a reliable risk assessment tool for early discrimination and intervention of post-FURL urosepsis.
分析血清肝素结合蛋白(HBP)和C反应蛋白(CRP)水平与输尿管软镜碎石术(FURL)后尿脓毒症的相关性,并构建反向传播神经网络预测模型。
纳入428例行FURL的肾结石患者。根据术后是否发生尿脓毒症,将患者分为脓毒症组(n = 42)和对照组(n = 386)。采用Logistic回归分析确定FURL术后尿脓毒症的危险因素及其相互作用。建立Logistic回归模型和反向传播神经网络模型用于预测FURL术后尿脓毒症,并使用受试者工作特征曲线评估其预测性能。
单因素分析显示,结石手术史、性别、尿培养阳性、结石直径、糖尿病、手术时间、白细胞(WBC)、血小板、CRP和HBP水平与FURL术后尿脓毒症显著相关(均P < 0.05)。多因素分析确定尿培养阳性、CRP和HBP水平为FURL术后尿脓毒症的独立危险因素(均P < 0.05)。交互分析显示,CRP和HBP表现出相加(P = 8.453,95%CI:2.645 - 16.282;P = 0.696,95%CI:0.131 - 1.273;P = 3.369,95%CI:1.176 - 7.632)和相乘(P = 1.754,95%CI:1.218 - 3.650)交互作用,而CRP和尿培养表现出相乘交互作用(P = 2.449,95%CI:1.525 - 3.825)。反向传播神经网络模型显示出比Logistic回归模型更好的预测性能。
CRP和HBP水平是FURL术后尿脓毒症的独立危险因素。基于CRP和HBP的反向传播神经网络模型比Logistic回归模型具有更高的预测准确性,可为FURL术后尿脓毒症的早期识别和干预提供可靠的风险评估工具。