Construction of a back propagation neural network model for predicting urosepsis after flexible ureteroscopic lithotripsy based on heparin-binding protein and C-reactive protein levels.

Wenwei Chen, Yanfeng He, Kaixin Lu, Changyi Liu, Tao Jiang, Hua Zhang, Rui Gao, Xueyi Xue
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Abstract

Objectives: 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.

Methods: A total of 428 patients with kidney stones who underwent FURL were enrolled. Patients were divided into sepsis group (n=42) and control group (n=386) according to whether post-operative urosepsis developed. Logistic regression analysis was used to determine the risk factors and interactions of post-FURL urosepsis. A Logistic regression model and a neural network model were developed for predicting post-FURL urosepsis following FURL, and their predictive performance was evaluated using ROC curves.

Results: Univariate analysis showed that stone surgery history, gender, urine culture, stone diameter, diabetes, operation time, white blood cell (WBC), platelet, CRP, and HBP levels were significantly associated with post-FURL urosepsis (P<0.05). Multivariate analysis identified positive urine culture, CRP, and HBP levels as independent risk factors for post-FURL urosepsis (P<0.05). Interaction analysis revealed that CRP and HBP showed both additive (RERI=8.453, 95%CI: 2.645-16.282; AP=0.696, 95%CI: 0.131-1.273; S=3.369, 95%CI: 1.176-7.632) and multiplicative (OR=1.754, 95%CI: 1.218-3.650) interactions, while CRP and urine culture demonstrated multiplicative interaction (OR=2.449, 95%CI: 1.525-3.825). The neural network model showed superior predictive performance compared to the Logistic regression model.

Conclusions: CRP and HBP levels are independent risk factors for post-FURL urosepsis. The 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.

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基于肝素结合蛋白和c反应蛋白水平预测输尿管镜碎石术后尿脓毒症的反向传播神经网络模型构建
目的:分析输尿管镜碎石术(FURL)术后血清肝素结合蛋白(HBP)和c反应蛋白(CRP)水平与尿脓毒症的关系,并建立反向传播神经网络预测模型。方法:共有428例肾结石患者接受了FURL。根据术后是否发生尿脓毒症分为脓毒症组(n=42)和对照组(n=386)。采用Logistic回归分析确定furl后尿脓毒症的危险因素及其相互作用。建立Logistic回归模型和神经网络模型预测FURL术后尿脓毒症,并采用ROC曲线评价其预测性能。结果:单因素分析显示,结石手术史、性别、尿培养、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术后尿脓毒症相关(PPRERI=8.453, 95%CI: 2.645-16.282;Ap =0.696, 95%ci: 0.131 ~ 1.273;S=3.369, 95%CI: 1.176 ~ 7.632)和乘法交互作用(OR=1.754, 95%CI: 1.218 ~ 3.650),而CRP和尿培养表现为乘法交互作用(OR=2.449, 95%CI: 1.525 ~ 3.825)。与Logistic回归模型相比,神经网络模型具有更好的预测性能。结论: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的神经网络模型预测准确率高于Logistic回归模型,可为furl后尿脓毒症的早期识别和干预提供可靠的风险评估工具。
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CiteScore
3.80
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0.00%
发文量
67
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