A Multiple Logistic Regression Model Based on Gamma-Glutamyl Transferase as a Biomarker for Early Prediction of Drug-Induced Liver Injury in Vietnamese Patients.

Abstract

The discovery of new biomarkers and the causality of drug-induced liver injury (DILI) is a major focus in modern medicine. Alcoholism is considered a risk factor for DILI. However, the extraction and assessment of alcohol history are difficult due to noncooperation by patients and intermittent management. Therefore, we conducted a case-control study of 1277 patients diagnosed with DILI according to the Roussel Uclaf Causality Assessment Method scale to evaluate gamma-glutamyl transferase (GGT) as a biomarker for predicting DILI in Vietnamese patients, where the proportion of alcoholism is quite high. Further, we built and validated a logistic regression model to predict the risk of DILI in hospitalized patients. The risk of DILI increased by 10% for 1 UI/L higher levels of GGT before prescription (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.00-1.01). A history of alcoholism was not a risk factor for DILI occurrence (OR, 1.83; 95%CI, 0.99-3.04; P = .057). A logistic regression model was successfully built and validated based on age; sex; initial levels of alanine aminotransferase, alkaline phosphatate, GGT, likelihood score of the suspected drug, and history of liver disease; the area under the receiver operating characteristic curve of the model was 0.883 (95%CI, 0.868-0.897). Our results thus suggest the necessity of exercising caution when prescribing to patients without a history of alcoholism but having high GGT levels. This model can be applied clinically to assess the risk of DILI before prescribing to reduce the risk of DILI in the patient.