Analysis of influencing factors affecting the efficacy of Shenfukang capsule in the treatment of chronic kidney disease and construction of prediction model
- VernacularTitle:肾福康胶囊治疗慢性肾脏病疗效的影响因素分析及预测模型建立
- Author:
Li TANG
1
;
Mengyuan QIN
2
;
Yufang YANG
2
;
Xiaoqin ZOU
3
;
Zhiwei LIANG
1
;
Xiaobin ZHONG
1
Author Information
1. Dept. of Pharmacy，Guangxi Medical University Cancer Hospital，Nanning 530012，China
2. Dept. of Pharmacy，The First Affiliated Hospital of Guangxi Medical University，Nanning 530021，China
3. Dept. of Scientific Research，The First Affiliated Hospital of Guangxi Medical University，Nanning 530021，China
- Publication Type:Journal Article
- Keywords:
Shenfukang capsule;
chronic kidney disease;
influencing factors;
prediction model;
nomogram
- From:
China Pharmacy
2026;37(13):1740-1745
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To explore the influencing factors of Shenfukang capsule in the treatment of chronic kidney disease （CKD） and construct a nomogram prediction model for evaluating its therapeutic efficacy. METHODS CKD patients who were hospitalized from July 2019 to August 2022 in the First Affiliated Hospital of Guangxi Medical University and treated with Shenfukang capsule were selected as study subjects. Clinical data of the patients were collected from the hospital’s electronic medical record system， and they were divided into an effective group and an ineffective group based on treatment outcomes. Lasso-Logistic multivariate regression analysis was used to screen the influencing factors of the efficacy of Shenfukang capsule. Using the effectiveness of Shenfukang capsule treatment as the prediction outcome and the screened influencing factors as predictor variables， a nomogram prediction model was constructed using R software. All patients were randomly divided into a training cohort and a validation cohort. The discriminative ability， calibration， and clinical net benefit of the model were evaluated using the receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis， respectively. RESULTS Lasso-Logistic regression analysis revealed that concurrent diabetes mellitus， decreased levels of blood urea nitrogen， triglycerides， and serum phosphorus， as well as prolonged prothrombin time and elevated apolipoprotein AⅠ levels， were influencing factors for reduced efficacy of Shenfukang capsule in CKD treatment. The evaluation results of the nomogram prediction model showed that the area under curve values were 0.745 and 0.797 in the training and validation cohorts， respectively， which were close and both exceeded 0.70， indicating stable predictive performance. The calibration curves demonstrated good agreement in both datasets， suggesting satisfactory calibration performance. The clinical decision curves were positioned above the two extreme reference lines， indicating high clinical benefit of the model. CONCLUSIONS Diabetes mellitus， blood urea nitrogen， triglycerides， apolipoprotein AⅠ， serum phosphorus， and prothrombin time were factors associated with the efficacy of Shenfukang capsule in CKD patients. The nomogram prediction model established in this study may provide a basis for rational clinical application of Shenfukang capsule and for improving its therapeutic efficacy in CKD.
