Early prediction of non-recovery in drug-induced liver injury by integrating genetic variants and clinical variables: an interpretable machine learning approach

Background and Aims

Early predictors of 6-month non-recovery in drug-induced liver injury (DILI) remain limited. Genetic variants are stable host characteristics that may complement baseline clinical variables. We aimed to develop and validate an interpretable, clinical-genetic machine learning model for predicting 6-month non-recovery in patients with DILI.

Methods

This retrospective, single-center study included 338 patients with DILI, who were classified as recovered (n = 171) or non-recovered (n = 167) at 6 months. Candidate single-nucleotide polymorphisms and baseline clinical variables were collected during initial hospitalization. Features were selected using complementary screening approaches. Multiple machine learning models were developed and compared. Model discrimination, calibration, clinical utility, the incremental value of genetic predictors, and interpretability using SHapley Additive exPlanations (SHAP) were assessed.

Results

Five predictors were consistently retained for model development: rs72631567, rs28521457, alanine aminotransferase, monocyte percentage, and low-density lipoprotein. Among the candidate algorithms, the light gradient boosting machine model showed the best performance, with area under the receiver operating characteristic curve (AUC) values of 0.92 (95% confidence interval [CI] 0.89–0.95) in the training set and 0.81 (95% CI 0.70–0.91) in the validation set. The model showed acceptable calibration and favorable decision-curve performance. In ablation analysis, the clinical-only model showed limited discrimination (AUC 0.57, 95% CI 0.43–0.71). SHAP analysis identified rs72631567 as the most influential predictor.

Conclusions

An interpretable model that integrates host genetic variants with baseline clinical variables demonstrated good internal performance for early prediction of 6-month non-recovery in DILI. These findings support external validation of genotype-informed risk stratification in patients with DILI.

Full text

https://www.xiahepublishing.com/2310-8819/JCTH-2026-00447

The study was recently published in the Journal of Clinical and Translational Hepatology.

The Journal of Clinical and Translational Hepatology (JCTH) is owned by the Second Affiliated Hospital of Chongqing Medical University and published by XIA & HE Publishing Inc. JCTH publishes high quality, peer reviewed studies in the translational and clinical human health sciences of liver diseases. JCTH has established high standards for publication of original research, which are characterized by a study’s novelty, quality, and ethical conduct in the scientific process as well as in the communication of the research findings. Each issue includes articles by leading authorities on topics in hepatology that are germane to the most current challenges in the field. Special features include reports on the latest advances in drug development and technology that are relevant to liver diseases. Regular features of JCTH also include editorials, correspondences and invited commentaries on rapidly progressing areas in hepatology. All articles published by JCTH, both solicited and unsolicited, must pass our rigorous peer review process.

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