The Feasibility of using Liver Function Indices and FibroScan in combination to predict the occurrence of anti-tuberculosis drug-induced liver injury in patients with liver disease
Predicting TB Drug Liver Injury in Patients with Liver Disease
Abstract
Background: The study aims to assess the feasibility of using a combined approach of liver function indices and FibroScan measurements as a predictive tool for the early detection of anti-tuberculosis drug-induced liver injury (DILI) in patients with existing liver disease.
Methods: A retrospective cohort study was conducted, including adult tuberculosis patients with documented liver disease. Liver function was assessed using standard biochemical parameters, and FibroScan examinations were performed to determine liver stiffness measurement (LSM). Patients were monitored for clinical and biochemical signs of DILI throughout treatment. Logistic regression models and Receiver Operating Characteristic (ROC) curves were used for data analysis. Statistical significance was set at p < 0.05.
Results: Patients who developed DILI showed significantly higher levels of ALT, AST, total bilirubin, GGT, and LSM, with strong positive correlations between these markers and DILI occurrence. Logistic regression analysis revealed elevated ALT, AST, TBIL, and GGT were strongly associated with an increased likelihood of DILI. The area under the ROC curves indicated excellent predictive accuracy of these parameters. A nomogram for predicting DILI based on the combined biomarkers was established.
Conclusion: The study demonstrates the feasibility of utilizing liver function indices and FibroScan measurements in combination to predict anti-tuberculosis DILI. The results highlight the importance of baseline liver health assessment and offer promising implications for clinical practice, aiding in individualized risk estimation and therapeutic decision-making for patients with liver disease initiating anti-tuberculosis therapy. Further validation in larger cohorts is warranted to strengthen the predictive model.
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