Analysis of influential factors and the construction of a risk prediction model for tigecycline-related drug-induced cholestatic liver disease
- VernacularTitle:替加环素相关药物性胆汁淤积性肝病的影响因素分析及风险预测模型构建
- Author:
Lina LIU
1
;
Jianqing WANG
1
;
Lun ZHANG
1
;
Jun YU
1
Author Information
1. Dept. of Pharmacy，Anhui Public Health Clinical Center （the First Affiliated Hospital of Anhui Medical University North District），Hefei 230012，China
- Publication Type:Journal Article
- Keywords:
tigecycline;
drug-induced cholestatic liver disease;
drug-induced liver injury;
influential factors;
prediction model
- From:
China Pharmacy
2025;36(20):2555-2560
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To analyze the influential factors of drug-induced cholestatic liver disease （DIC） related to tigecycline （TGC）， and establish a prediction model for the risk of this adverse reaction. METHODS Data of 707 hospitalized patients who received TGC treatment in our hospital from August 2022 to August 2024 were collected and randomly divided into training set （n＝566） and test set （n＝141） at a ratio of 8∶2. Prediction variables were screened using the least absolute shrinkage and selection operator regression analysis. Multivariate Logistic regression analysis was used to screen the independent risk factors for TGC-related DIC， and a nomogram prediction model was drawn based on the above factors. The prediction performance of the model was evaluated by the receiver operator characteristic curve （ROC curve） and its area under the curve （AUC）. The accuracy of the model was assessed by the Hosmer-Lemeshow goodness-of-fit test and calibration curves. The clinical net benefit of the prediction model were evaluated by decision curve analysis. RESULTS Among the 707 patients， 93 patients developed DIC， with an incidence rate of 13.15%. Gender， age， high-dose administration of TGC， intensive care unit （ICU） admission， duration of medication of TGC， and concurrent use of antifungal drug voriconazole were independent risk factors for the occurrence of TGC-related DIC （P＜0.05）. The AUC of the training set model was 0.745 （95%CI： 0.687-0.801）， with a sensitivity of 76.6% and a specificity of 60.3%. The AUC of ROC curve of the test set model was 0.762 （95%CI： 0.650-0.900）， with a sensitivity of 81.3% and a specificity of 72.0%. The Hosmer-Lemeshow goodness-of-fit test for the training set， the χ 2 value was 5.187 and P was 0.737； and for the test set， the χ 2 value was 9.980 and P was 0.266. The mean absolute error of the calibration curve for the training set was 0.012， and for the test set， it was 0.038. The risk threshold range for the training set was 4%-45%， and for the test set， it was 4%-28%. CONCLUSIONS Age， gender， high-dose administration of TGC， ICU admission， duration of medication of TGC， and concurrent use of antifungal drug voriconazole are independent risk factors for TGC-related DIC. The established TGC-related DIC risk prediction model has good prediction performance and accuracy.
