Zhang Yue, Lu Chuan, Xu Jingying, Ma Qiqi, Han Mei, Ying Li
Department of Gastroenterology, The Second Hospital of Dalian Medical University, Dalian, China.
Department of Cardiology, The Second Hospital of Dalian Medical University, Dalian, China.
Front Med (Lausanne). 2025 Apr 28;12:1571406. doi: 10.3389/fmed.2025.1571406. eCollection 2025.
Drug-induced liver injury (DILI) is becoming a worldwide emerging problem. However, few studies focus on the diagnostic performance of non-invasive markers in DILI. This study aims to develop novel integrative models to identify DILI-associated liver inflammation and fibrosis, and compare the predictive values with previously developed indexes.
A total of 72 DILI patients diagnosed as DILI through liver biopsy were enrolled in this study. Patients were divided into absent-mild (S0-S1, G0-G1) group and moderate-severe (S2-S4, G2-G4) group based on the histological severity of inflammation and fibrosis. We used the area under the receiver operating characteristics curves (AUC) to test the model performances. Backward stepwise regression, best subset and logistic regression models were employed for feature selection and model building. Prediction models were presented with nomogram and evaluated by AUC, Brier score, calibration curves and decision curve analysis (DCA).
For diagnosing moderate-severe inflammation and fibrosis, we calculated the AUC of gamma-glutamyl transpeptidase-to-platelet ratio (GPR), aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4) and fibrosis-5 index (FIB-5), which were 0.708 and 0.676, 0.778 and 0.667, 0.822 and 0.742, 0.831 and 0.808, respectively. Then, backward stepwise regression, best subset and logistic regression models were conducted for predicting significant liver inflammation and fibrosis. For the prediction of ≥G2 inflammation grade, the AUC was 0.856, 0.822, 0.755, and for the prediction of ≥S2 fibrosis grade, the AUC was 0.889, 0.889, 0.826. Through Brier score, calibration curves and DCA, it was further demonstrated that backward stepwise regression model was highly effective to predict both moderate-severe inflammation and fibrosis for DILI.
The backward stepwise regression model we proposed in this study is more suitable than the existing non-invasive biomarkers and can be conveniently used in the individualized diagnosis of DILI-related liver inflammation and fibrosis.
药物性肝损伤（DILI）正成为一个全球性的新问题。然而，很少有研究关注非侵入性标志物在DILI中的诊断性能。本研究旨在开发新的综合模型以识别与DILI相关的肝脏炎症和纤维化，并将预测价值与先前开发的指标进行比较。
本研究纳入了72例经肝活检确诊为DILI的患者。根据炎症和纤维化的组织学严重程度，将患者分为轻度（S0-S1，G0-G1）组和中重度（S2-S4，G2-G4）组。我们使用受试者操作特征曲线下面积（AUC）来测试模型性能。采用向后逐步回归、最佳子集和逻辑回归模型进行特征选择和模型构建。预测模型以列线图呈现，并通过AUC、Brier评分、校准曲线和决策曲线分析（DCA）进行评估。
为诊断中重度炎症和纤维化，我们计算了γ-谷氨酰转肽酶与血小板比值（GPR）、天冬氨酸氨基转移酶与血小板比值指数（APRI）、纤维化-4指数（FIB-4）和纤维化-5指数（FIB-5）的AUC，分别为0.708和0.676、0.778和0.667、0.822和0.742、0.831和0.808。然后，进行向后逐步回归、最佳子集和逻辑回归模型以预测显著的肝脏炎症和纤维化。对于预测≥G2炎症分级，AUC分别为0.856、0.822、0.755；对于预测≥S2纤维化分级，AUC分别为0.889、0.889、0.826。通过Brier评分、校准曲线和DCA进一步证明，向后逐步回归模型在预测DILI的中重度炎症和纤维化方面非常有效。
我们在本研究中提出的向后逐步回归模型比现有的非侵入性生物标志物更合适，可方便地用于DILI相关肝脏炎症和纤维化的个体化诊断。
