Study on the influencing factors and risk prediction model for proteinuria in patients with malignant tumors induced by apatinib
- VernacularTitle: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
- Author:
Can HUANG
1
;
Shuan WANG
1
;
Jun MA
2
;
Yan GUO
3
;
Lamei QI
1
Author Information
1. Dept. of Pharmaceutical Administration，Anqing Municipal Hospital，Anhui Anqing 246000，China
2. Dept. of General Surgery，Anqing Municipal Hospital，Anhui Anqing 246000，China
3. Dept. of Oncology，Anqing Municipal Hospital，Anhui Anqing 246000，China
- Publication Type:Journal Article
- Keywords:
apatinib;
malignant tumors;
proteinuria;
risk factors;
risk prediction model;
Logistic regression;
ROC curve
- From:
China Pharmacy
2024;35(22):2779-2783
- CountryChina
- Language:Chinese
-
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 aminotransferase 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 （P＜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； P＜0.05）. The binary Logistic regression model equation for the probability （P） of apatinib- induced proteinuria is expressed as LogitP＝1.610XMH+1.233XSH－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 LogitP 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.
