Study on the influencing factors and risk prediction model for proteinuria in patients with malignant tumors induced by apatinib

HUANG Can; WANG Shuan; MA jun; GUO Yan; QI Lamei

doi:10.6039/j.issn.1001-0408.2024.22.13

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Study on the influencing factors and risk prediction model for proteinuria in patients with malignant tumors induced by apatinib

更新时间：2024-11-26

- Study on the influencing factors and risk prediction model for proteinuria in patients with malignant tumors induced by apatinib
- ZHONGGUO YAOFANG Vol. 35, Issue 22, Pages: 2779-2783(2024)
- 作者机构：
  
  1.安庆市立医院药事管理科，安徽安庆　246000
  2.安庆市立医院普外科，安徽安庆　246000
  3.安庆市立医院肿瘤内科，安徽安庆　246000
- 作者简介：
- 基金信息：
- DOI：10.6039/j.issn.1001-0408.2024.22.13
  CLC： R969.3;R979.1
- Published：30 November 2024，
  
  Received：25 March 2024，
  
  Revised：17 July 2024，
- 稿件说明：
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黄灿,王栓,马军等.阿帕替尼致恶性肿瘤患者蛋白尿影响因素及风险预测模型研究 ^Δ[J].中国药房,2024,35(22):2779-2783.

HUANG Can,WANG Shuan,MA jun,et al.Study on the influencing factors and risk prediction model for proteinuria in patients with malignant tumors induced by apatinib[J].ZHONGGUO YAOFANG,2024,35(22):2779-2783.
黄灿,王栓,马军等.阿帕替尼致恶性肿瘤患者蛋白尿影响因素及风险预测模型研究 ^Δ[J].中国药房,2024,35(22):2779-2783. DOI： 10.6039/j.issn.1001-0408.2024.22.13.

HUANG Can,WANG Shuan,MA jun,et al.Study on the influencing factors and risk prediction model for proteinuria in patients with malignant tumors induced by apatinib[J].ZHONGGUO YAOFANG,2024,35(22):2779-2783. DOI： 10.6039/j.issn.1001-0408.2024.22.13.

摘要

目的

研究恶性肿瘤患者使用阿帕替尼治疗后发生蛋白尿的影响因素，据此构建并评价其风险预测模型。

方法

选取我院2020年1月－2022年12月使用阿帕替尼治疗的恶性肿瘤患者120例作为训练集，回顾性收集其临床资料，采用单因素分析和多因素Logistic回归分析确定阿帕替尼致蛋白尿的独立危险因素，并构建风险预测模型；采用受试者操作特征（ROC）曲线对其预测价值进行评价。选取2023年1－12月我院使用阿帕替尼治疗的恶性肿瘤患者34例作为验证集，利用其临床资料交叉验证预测模型的准确性。

结果

120例训练集患者的蛋白尿发生率为26.67%。蛋白尿组有吸烟史、合并高血压、阿帕替尼日剂量≥500 mg的患者比例，以及丙氨酸转氨酶水平均显著高于非蛋白尿组，而中性粒细胞计数显著低于非蛋白尿组（

＜0.05）。其中，有吸烟史、合并高血压是阿帕替尼致蛋白尿的独立危险因素（比值比分别为5.005、5.342，95%置信区间分别为1.806～13.872、1.227～9.602，

＜0.05）。阿帕替尼致蛋白尿发生概率（

）的二元Logistic回归模型方程为Logit

＝1.610

+1.233

－1.483（MH为合并高血压，SH为有吸烟史），模型准确度为80.0%。ROC曲线分析结果显示，曲线下面积为0.771，最大约登指数为0.474，此时Logit

的最佳截断值为0.159 9，模型的敏感度为90.6%、特异性为56.8%。交叉验证结果显示，34例患者总体预测准确率为88.24%。

结论

有吸烟史和合并高血压是阿帕替尼致蛋白尿的独立危险因素；所建风险预测模型具有中等预测价值，可用于预测阿帕替尼致恶性肿瘤患者蛋白尿的发生风险。

Abstract

OBJECTIVE

To study the influencing factors for proteinuria in patients with malignant tumors treated with apatinib， then establish and evaluate a risk prediction model based on it.

METHODS

A total of 120 patients with malignant tumors treated with apatinib in our hospital from January 2020 to December 2022 were selected as the training set， and the clinical data was collected. Univariate analysis and multivariate Logistic regression analysis were used to identify independent risk factors for proteinuria associated with apatinib and then construct a risk prediction model. The predictive value of the model was evaluated by using the receiver operator characteristic （ROC） curve. A total of 34 patients with malignant tumors treated with apatinib from January to December 2023 in our hospital were selected as the validation set， and their clinical data were obtained to cross-validate the accuracy of the prediction model.

RESULTS

The incidence of proteinuria in the training set of 120 patients was 26.67%. The proportions of patients with smoking history， combined hypertension， apatinib daily dose of ≥500 mg， and alanine amino

transferase level were significantly higher in proteinuria group than those in non-proteinuria group. At the same time， the neutrophilic granulocyte count was significantly lower than that in non-proteinuria group （

＜0.05）. Patients with smoking history and combined hypertension were the independent risk factors for apatinib-induced proteinuria （odds ratios were 5.005 and 5.342， respectively； with 95% confidence intervals of 1.806-13.872 and 1.227-9.602， respectively；

＜0.05）. The binary Logistic regression model equation for the probability （

） of apatinib-induced proteinuria is expressed as Logit

＝1.610

+1.233

－1.483 （MH for combined hypertension， SH for the smoking history）， with a model accuracy of 80.0%. ROC curve analysis demonstrated the area under the ROC curve of 0.771， the maximum Youden’s index of 0.474， and the optimal cut-off value for Logit

was 0.159 9， with a sensitivity of 90.6% and specificity of 56.8%. Cross-validation results indicated an overall prediction accuracy of 88.24% for the 34 patients.

CONCLUSIONS

Combined hypertension and smoking history are independent risk factors for apatinib-induced proteinuria. The constructed risk prediction model has moderate predictive value and can be used to predict the risk of proteinuria in patients with malignant tumors induced by apatinib.

关键词

阿帕替尼恶性肿瘤蛋白尿危险因素风险预测模型Logistic回归受试者操作特征曲线

Keywords

malignant tumorsproteinuriarisk factorsrisk prediction modelLogistic regressionROC curve

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