ArticleScientific reports2025
Development and external validation of a pre-treatment nomogram for predicting drug-induced liver injury risk in tuberculosis patients.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk factors for drug-induced liver injury in tuberculosis patients: a meta-analysis and systematic review.Frontiers in medicine · 2026Pooled it
- Ethanol as a Modifier of Drug Toxicity in Humans: Pathways of Toxicity and Organ-Level Consequences.International journal of molecular sciences · 2026Review
- Development and prospective validation of a machine learning model for risk stratification of drug-induced liver injury using real-world clinical data.Therapeutic advances in drug safety · 2026Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Drug-induced liver injury (DILI) frequently complicates anti-tuberculosis (TB) treatment, particularly in regions with a high TB burden. Early pre-treatment identification of patients at elevated risk is essential for timely intervention and safer treatment outcomes. In this retrospective two-center cohort study, we collected baseline data from 2022 to 2024 of 2624 patients admitted to two tertiary hospitals before starting standard drug-susceptible anti-TB therapy (isoniazid, rifampicin, pyrazinamide, ethambutol). Patients were randomly divided into training (n = 1512), internal validation (n = 648), and external validation (n = 564) cohorts. Multivariable logistic regression found DILI predictors, and a pre-treatment risk-forecasting nomogram was built. Model performance was assessed by AUC, calibration plots, and decision curve analysis (DCA). Six baseline predictors emerged: age ≥ 60 years, BMI < 18.5 kg/m
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