基于少数类样品合成过抽样技术算法的血液透析患者动静脉内瘘功能不良的预测模型建立

    A prediction model of arteriovenous fistula dysfunction in hemodialysis patients was established based on a few sample synthesis oversampling technique algorithm

    • 摘要:
      目的  分析血液透析患者动静脉内瘘(arteriovenous fistula,AVF)功能不良的危险因素,并基于少数类样品合成过抽样技术(synthetic minority oversampling technique,SMOTE)算法建立风险预警模型。
      方法  选取安庆市立医院2019年1月1日至2021年12月31日期间在肾内科就诊且以AVF作为透析通路的血液透析患者400例作为研究对象,根据血液透析患者AVF功能将其分为AVF功能不良组(81例)和AVF功能正常组(319例),分析所选血液透析患者的临床资料,并通过单因素与多因素Logistic回归分析筛选血液透析患者AVF功能不良的危险因素,再通过SMOTE算法对上述危险因素的数据进行重建,从而获得血液透析患者AVF功能不良风险预警模型,并对两种模型的预测效能进行比较。
      结果  女性、糖尿病、白蛋白<35 g/L、C反应蛋白≥25 mg/L、血磷>2 mmol/L、AVF狭窄为血液透析患者AVF功能不良的危险因素(P<0.05)。根据上述危险因素及回归系数,获得原始预警模型P1预警模型的受试者工作特征曲线曲线下面积为0.787(95%CI:0.743~0.831),P2预警模型的受试者工作特征曲线曲线下面积为0.870(95%CI:0.812~0.928),基于SMOTE算法预警模型的真正类率值比原始数据预警模型(0.731比0.763)低,而PPV值(0.742比0.866)、F-score(0.729比0.886)均比原始数据预警模型高。
      结论  女性、糖尿病、白蛋白<35 g/L、C反应蛋白≥25 mg/L、血磷>2 mmol/L、AVF狭窄为血液透析患者AVF功能不良的危险因素,根据上述危险因素构建的SMOTE预警模型相较于传统Logistic回归模型有着更高的预测价值。

       

      Abstract: Objective To explore the risk factors of arteriovenous fistula (AVF) dysfunction in hemodialysis (HD) patients and validate a risk warning model based upon synthetic minority oversampling technique (SMOTE). Methods From January 1, 2019 to December 31, 2021, 400 HD patients with AVF as dialysis access were selected as study subjects. According to AVF function of HD, they were assigned into two groups of poor AVF function (n=81) and normal AVF function (n=319). The relevant clinical data of selected HD patients were reviewed and the risk factors of poor AVF function screened by univariate and multivariate Logistic regression. Then the above risk factors were reconstructed by SMOTE algorithm for established an early warning model of poor AVF function. The prediction efficiency of two models was compared.Results Females, diabetes mellitus (DM), albumin <35 g/L, C-reactive protein (CRP) ≥25 mg/L, blood phosphorus >2 mmol/L and AVF stenosis were the risk factors for AVF dysfunction in HD patients (P<0.05). According to the above risk factors and regression coefficients, area under the ROC curve (AUC) of original warning model P1 was 0.787(95%CI: 0.743-0.831). And area under the ROC curve (AUC) of P2 warning model was 0.870(95%CI: 0.743-0.831). TPR (0.731) of early warning model based upon SMOTE algorithm was lower than original warning model (0.763) while PPV (0.742 vs 0.866) and F-score (0.729 vs 0.886) were higher than original warning model. Conclusions Females, DM, albumin <35 g/L, CRP and AVF stenosis are risk factors for poor AVF function in HD patients. And SMOTE early warning model based upon the above risk factors has higher predictive value as compared with traditional Logistic regression model.

       

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