{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T06:07:12Z","timestamp":1789106832186,"version":"build-2803163510"},"reference-count":62,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study systematically evaluates the construction methods, predictive factors, and performance of risk prediction models for anti-tuberculosis drug-induced liver injury (ATB-DILI). It aims to furnish evidence-based evidence for early detection of East Asian individuals at high risk of ATB-DILI and the development of personalized clinical treatment plans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A comprehensive literature search was conducted to identify studies related to risk prediction models for ATB-DILI. Key data, including study population, sample size, model construction algorithms, predictive factors, and model discrimination, were extracted. Qualitative synthesis and systematic analysis were then performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 26 studies comprising 31 risk prediction models (cumulative sample size approximately 41,734 cases) were included. Twenty studies reported internal validation and six reported external validation. Model discriminative ability varied from 0.624 to 1.0, indicating at least modest-to-excellent discriminatory ability. Prediction model Risk Of Bias ASsessment Tool (PROBAST) assessment identified only one study at low risk of bias, with all remaining studies rated high risk of bias. Meta-analysis yielded a pooled AUC of 0.81 for ATB-DILI prediction models. History of liver disease, extrapulmonary tuberculosis, age \u226560 years, alcohol consumption, concomitant medication use, retreatment, elevated AST, diabetes, and smoking were associated with higher ATB-DILI risk (all\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05), whereas uric acid showed an inverse association (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05). Importantly, the observed association for non-use of hepatoprotective agents cannot confirm a preventive effect; these factors should inform risk stratification and intensified monitoring rather than guide prophylactic hepatoprotective therapy.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Published risk prediction models for ATB-DILI among East Asian populations were systematically appraised. These models yielded moderate-to-good discriminative performance yet showed substantial heterogeneity and high risk of bias, mainly due to defective missing-data processing, univariate-based predictor selection, insufficient outcome events, and absent external validation. Such methodological shortcomings may overestimate AUC values, especially for retrospective non-validated models. Existing ATB-DILI prediction models are thus of uncertain reliability and generalizability. Future large-sample multicenter prospective studies with standardized outcomes and external validation are required to develop robust clinically applicable models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic review registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/www.crd.york.ac.uk\/PROSPERO\/view\/CRD420261468400<\/jats:ext-link>\n                      , identifier CRD420261468400.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1958154","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:29:08Z","timestamp":1789104548000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Systematic review and meta-analysis of risk prediction models for anti-tuberculosis drug-induced liver injury in East Asian populations"],"prefix":"10.3389","volume":"14","author":[{"given":"Hongjiao","family":"Li","sequence":"first","affiliation":[{"name":"Department of Pharmacy, Nanbu People's Hospital","place":["Nanchong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Pulmonary and Critical Care Medicine, Nanbu People's Hospital","place":["Nanchong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianmei","family":"Zhong","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, Nanbu People's Hospital","place":["Nanchong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e475","DOI":"10.1016\/S2214-109X(26)00071-9","article-title":"Tuberculosis at a crossroads","volume":"14","year":"2026","journal-title":"Lancet Glob Health"},{"key":"B2","first-page":"266","article-title":"Financial burden of tuberculosis patients - China, 2020","volume":"5","author":"Xu","year":"2023","journal-title":"China CDC Wkly."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.3760\/cma.j.cn112147-20240614-00338","article-title":"Guidelines for diagnosis and management of drug-induced liver injury caused by anti-tuberculosis drugs (2024 version)","volume":"47","year":"2024","journal-title":"Chin J Tuberc Respir Dis."},{"key":"B4","article-title":"Hepatotoxicity induced by anti-tuberculosis medications: exploring causes, risk factors, and potential mitigation strategies","author":"Zade","year":"2023","journal-title":"J Drug Deliv Biother."},{"key":"B5","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1080\/03602532.2023.2215478","article-title":"Antitubercular drugs induced liver injury: an updated insight into molecular mechanisms","volume":"55","author":"Ezhilarasan","year":"2023","journal-title":"Drug Metab Rev."},{"key":"B6","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1186\/s12879-025-12005-y","article-title":"Risk of drug-induced liver injury in chronic hepatitis C and tuberculosis co\u2013 infection: a systematic review and meta\u2013 analysis","volume":"25","author":"Wei","year":"2025","journal-title":"BMC Infect Dis."},{"key":"B7","doi-asserted-by":"publisher","first-page":"790","DOI":"10.3390\/jpm12050790","article-title":"Clinical and genetic risk factors for drug-induced liver injury associated with anti-tuberculosis treatment\u2014a study from patients of Portuguese health centers","volume":"12","author":"Cavaco","year":"2022","journal-title":"J Pers Med."},{"key":"B8","doi-asserted-by":"publisher","first-page":"e078276","DOI":"10.1136\/bmj-2023-078276","article-title":"Developing clinical prediction models: a step-by-step guide","volume":"386","author":"Efthimiou","year":"2024","journal-title":"BMJ."},{"key":"B9","doi-asserted-by":"publisher","first-page":"l737","DOI":"10.1136\/bmj.l737","article-title":"Guide to presenting clinical prediction models for use in clinical settings","volume":"365","author":"Bonnett","year":"2019","journal-title":"BMJ."},{"key":"B10","doi-asserted-by":"publisher","first-page":"e77721","DOI":"10.2196\/77721","article-title":"Beyond comparing machine learning and logistic regression in clinical prediction modelling: shifting from model debate to data quality","volume":"27","author":"Hu","year":"2025","journal-title":"J Med Internet Res."},{"key":"B11","doi-asserted-by":"publisher","first-page":"e1001744","DOI":"10.1371\/journal.pmed.1001744","article-title":"Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the charms checklist","volume":"11","author":"Moons","year":"2014","journal-title":"PLoS Med."},{"key":"B12","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"Probast: a tool to assess the risk of bias and applicability of prediction model studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Ann Intern Med Clin Cases."},{"key":"B13","doi-asserted-by":"publisher","first-page":"i6460","DOI":"10.1136\/bmj.i6460","article-title":"A guide to systematic review and meta-analysis of prediction model performance","volume":"356","author":"Debray","year":"2017","journal-title":"BMJ."},{"key":"B14","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3760\/cma.j.cn112147-20210705-00467","article-title":"Nomogram model for predicting risk of anti-tuberculosis drug-induced liver injury among inpatients with tuberculosis","volume":"45","author":"Zhao","year":"2022","journal-title":"Chin J Tuberc Respir Dis."},{"key":"B15","first-page":"705","article-title":"Development and comparison of convolutional neural network and logistic regression models for predicting anti-tuberculosis drug-induced liver injury","volume":"25","author":"Lu","year":"2023","journal-title":"ADRJ"},{"key":"B16","first-page":"21","article-title":"Establishment of an early warning model for first-line anti-tb drug-induced liver injury","volume":"46","author":"Muxing","year":"2024","journal-title":"Chin J Antituberc."},{"key":"B17","article-title":"Analysis of incidence and risk factors of ADLI in Southern Xinjiang and establishment of nomogram prediction model","author":"Siming","year":"2023","journal-title":"Xinjiang Med Univ."},{"key":"B18","article-title":"Development and validation of a predictive model for the risk of antituberculosis drug-induced liver injury and genetic susceptibility studies","author":"Junling","year":"2024","journal-title":"North China Univ Sci Technol."},{"key":"B19","article-title":"Study on dietary patterns and serum energy metabolic markers associated with the risk of liver injury induced by anti-tuberculosis drugs","author":"Xiaona","year":"2023","journal-title":"Qingdao Univ."},{"key":"B20","article-title":"Construction and validation of a risk prediction model for anti-tuberculosis drug-induced liver injury","author":"Yu","year":"2023","journal-title":"North China Univ Sci Technol."},{"key":"B21","first-page":"62","article-title":"Analysis of adverse reactions and risk factors of liver injury caused by anti-tuberculosis drugs","volume":"27","author":"Leina","year":"2022","journal-title":"J Clin Pulm Med."},{"key":"B22","first-page":"1413","article-title":"Analysis of influencing factors and predictive model construction of anti-tuberculosis drug-induced liver injury","volume":"35","author":"Zhengtai","year":"2025","journal-title":"New Med."},{"key":"B23","first-page":"117","article-title":"Development and validation of prognostic models for tuberculosis patients with drug-induced liver injury","volume":"4","author":"Junqi","year":"2019","journal-title":"Electron J Emerg Infect Dis."},{"key":"B24","first-page":"591","article-title":"Construction of a risk prediction model for anti-TB drug-induced liver injury based on TCM constitution assessment","volume":"39","author":"Weiwei","year":"2025","journal-title":"Med J Commun."},{"key":"B25","first-page":"36","article-title":"Analysis of risk facto rs for liver injury caused by anti-tuberculosis drugs based on an artificial neural network model","volume":"33","author":"Jinqi","year":"2024","journal-title":"Chin J Convalescent Med."},{"key":"B26","first-page":"3447","article-title":"Construction of a risk prediction model for childhood anti-tuberculosis drug induced liver injury based on machine learning","volume":"41","author":"Qinqin","year":"2024","journal-title":"Chin J Mod Appl Pharm."},{"key":"B27","article-title":"Construction and performance validation of a machine learning-based predictive model for antituberculosis drug-induced liver injury","author":"Jingyuan","year":"2025"},{"key":"B28","first-page":"425","article-title":"Establishment and verification of the nomogram model of initial pulmonary tuberculosis and anti-tuberculosis treatment complicated with liver function impairment","volume":"38","author":"Yue","year":"2026","journal-title":"Med J West China"},{"key":"B29","first-page":"117","article-title":"Risk factors and risk prediction for the development of drug-induced liver injury in patients with newly treated pulmonary tuberculosis","volume":"46","author":"Qing","year":"2026","journal-title":"J Xuzhou Med Univ."},{"key":"B30","article-title":"Relationship between telomere length and its related genetic variation and anti-tuberculosis drug-induced liver injury","author":"Wenpei","year":"2022","journal-title":"Nanjing Med Univ."},{"key":"B31","doi-asserted-by":"publisher","first-page":"17","DOI":"10.5937\/jomb0-50878","article-title":"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","volume":"44","author":"Shaoqiang","year":"2025","journal-title":"J Med Biochem."},{"key":"B32","doi-asserted-by":"publisher","first-page":"239","DOI":"10.2147\/DDDT.S495555","article-title":"Risk prediction of liver injury in pediatric tuberculosis treatment: development of an automated machine learning model","volume":"19","author":"Zeng","year":"2025","journal-title":"Drug Des Devel Ther."},{"key":"B33","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1186\/s12931-026-03608-3","article-title":"Integrated multiomics profiling predicts anti-tuberculosis drug-induced liver injury","volume":"27","author":"Liu","year":"2026","journal-title":"Respir Res."},{"key":"B34","doi-asserted-by":"publisher","first-page":"16014","DOI":"10.1038\/hgv.2016.14","article-title":"Development of a prediction system for anti-tuberculosis drug-induced liver injury in Japanese patients","volume":"3","author":"Mushiroda","year":"2016","journal-title":"Hum Genome Var."},{"key":"B35","doi-asserted-by":"publisher","first-page":"40631","DOI":"10.1038\/s41598-025-24282-3","article-title":"Development and external validation of a pre-treatment nomogram for predicting drug-induced liver injury risk in tuberculosis patients","volume":"15","author":"Zhou","year":"2025","journal-title":"Sci Rep."},{"key":"B36","doi-asserted-by":"publisher","first-page":"e70131","DOI":"10.1002\/jcph.70131","article-title":"Construction and validation of a predictive model for the risk of anti-tuberculosis drug-induced liver injury based on machine learning algorithms","volume":"66","author":"Cheng","year":"2026","journal-title":"J Clin Pharmacol Toxicol."},{"key":"B37","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.21037\/atm-22-4551","article-title":"Combined electronic medical records and gene polymorphism characteristics to establish an anti-tuberculosis drug-induced hepatic injury (ATDH) prediction model and evaluate the prediction value","volume":"10","author":"Zhang","year":"2022","journal-title":"Ann Transl Med."},{"key":"B38","doi-asserted-by":"publisher","first-page":"1153815","DOI":"10.3389\/fphar.2023.1153815","article-title":"A nomogram model to predict the risk of drug-induced liver injury in patients receiving anti-tuberculosis treatment","volume":"14","author":"Ji","year":"2023","journal-title":"Front Pharmacol."},{"key":"B39","doi-asserted-by":"publisher","first-page":"1816387","DOI":"10.3389\/fphar.2026.1816387","article-title":"Automated machine learning model to predict anti-tuberculosis drug-induced liver injury in patients with tuberculous meningitis","volume":"17","author":"Li","year":"2026","journal-title":"Front Pharmacol."},{"key":"B40","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1111\/bcp.15990","article-title":"Effect of changes in metabolic enzymes and transporters on drug metabolism in the context of liver disease: impact on pharmacokinetics and drug\u2013drug interactions","volume":"90","author":"Armani","year":"2023","journal-title":"Br J Clin Pharmacol."},{"key":"B41","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s40262-024-01466-0","article-title":"Influence of ageing on the pharmacodynamics and pharmacokinetics of chronically administered medicines in geriatric patients: a review","volume":"64","author":"Ngcobo","year":"2025","journal-title":"Clin Pharmacokinet."},{"key":"B42","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1111\/j.1365-2125.2012.04446.x","article-title":"Predicted metabolic drug clearance with increasing adult age","volume":"75","author":"Polasek","year":"2013","journal-title":"Br J Clin Pharmacol."},{"key":"B43","doi-asserted-by":"publisher","first-page":"106","DOI":"10.3390\/biomedicines6040106","article-title":"Alcoholic liver disease: alcohol metabolism, cascade of molecular mechanisms, cellular targets, and clinical aspects","volume":"6","author":"Teschke","year":"2018","journal-title":"Biomedicines"},{"key":"B44","doi-asserted-by":"publisher","first-page":"1509785","DOI":"10.3389\/fchem.2025.1509785","article-title":"Chronic alcohol intake disrupts cytochrome P450 enzyme activity in alcoholic fatty liver disease: insights into metabolic alterations and therapeutic targets","volume":"13","author":"Zhu","year":"2025","journal-title":"Front Chem."},{"key":"B45","doi-asserted-by":"publisher","first-page":"105634","DOI":"10.1016\/j.jbc.2024.105634","article-title":"Dysregulated cysteine metabolism leads to worsened liver pathology in diabetes-tuberculosis comorbid condition","volume":"300","author":"Chaudhary","year":"2024","journal-title":"J Biol Chem."},{"key":"B46","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1007\/s00204-026-04313-2","article-title":"Extending a liver-based model to lung and intestine: a comparative study of CYP1A1 and CYP1A2 induction by cigarette smoke in human in vitro models","volume":"100","author":"Lenich","year":"2026","journal-title":"Arch Toxicol."},{"key":"B47","doi-asserted-by":"publisher","first-page":"12462","DOI":"10.3390\/ijms241512462","article-title":"Potential harm of IQOS smoke to rat liver","volume":"24","author":"Granata","year":"2023","journal-title":"Int J Mol Sci."},{"key":"B48","doi-asserted-by":"publisher","first-page":"4","DOI":"10.4103\/ijp.ijp_116_24","article-title":"Protective effect of hesperidin, ascorbic acid and their combination on oxidative stress, dyslipidemia, and histological changes in antitubercular drug-induced hepatotoxicity in rats","volume":"57","author":"Shanmugam","year":"2025","journal-title":"Indian J Pharmacol."},{"key":"B49","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1111\/cts.13281","article-title":"A literature review of liver function test elevations in rifampin drug-drug interaction studies","volume":"15","author":"Ibrahim","year":"2022","journal-title":"Clin Transl Sci."},{"key":"B50","first-page":"151","article-title":"Role of oxidative stress in antituberculous drugs (individuals and combined) cytotoxicity in Hepg2 cells","volume":"45","author":"Elmorsy","year":"2016","journal-title":"Malays Appl Biol."},{"key":"B51","doi-asserted-by":"publisher","first-page":"576","DOI":"10.70749\/ijbr.v3i5.1396","article-title":"Assessing the hepatotoxic effects of anti-tuberculosis treatment in multi-drug-resistant patients: evidence from Fatimah Jinnah Hospital, Quetta","volume":"3","author":"Muhammad","year":"2025","journal-title":"Indus J Biosci Res."},{"key":"B52","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1016\/j.ijtb.2019.11.005","article-title":"Adverse drug reactions in tuberculosis and management","volume":"4","author":"Prasad","year":"2019","journal-title":"Indian J Tuberc."},{"key":"B53","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1080\/17476348.2021.1927718","article-title":"Extrapulmonary tuberculosis","volume":"15","author":"Sharma","year":"2021","journal-title":"Expert Rev Respir Med."},{"key":"B54","doi-asserted-by":"publisher","first-page":"14709","DOI":"10.1038\/s41598-025-97517-y","article-title":"Prevalence, Clinical reasons and associated factors of extended treatment duration for drug susceptible tuberculosis \u2013 a real-world experience","volume":"15","author":"Liu","year":"2025","journal-title":"Sci Rep."},{"key":"B55","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1024\/1661-8157\/a003370","volume":"108","author":"Arpagaus","year":"2019","journal-title":"Praxis"},{"key":"B56","doi-asserted-by":"publisher","first-page":"1834524","DOI":"10.3389\/fmed.2026.1834524","article-title":"Risk factors for drug-induced liver injury in tuberculosis patients: a meta-analysis and systematic review","volume":"13","author":"Liu","year":"2026","journal-title":"Front Med."},{"key":"B57","doi-asserted-by":"publisher","first-page":"1013751","DOI":"10.3389\/fcimb.2022.1013751","article-title":"Associations between nutritional and immune status and clinicopathologic factors in patients with tuberculosis: a comprehensive analysis","volume":"12","author":"Liu","year":"2022","journal-title":"Front Cell Infect Microbiol."},{"key":"B58","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1007\/s00204-022-03245-x","article-title":"Imatinib-induced hepatotoxicity via oxidative stress and activation of NLRP3 inflammasome: an in vitro and in vivo study","volume":"96","author":"Huang","year":"2022","journal-title":"Arch Toxicol."},{"key":"B59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2147\/HMER.S290542","article-title":"Anti-tuberculosis drug induced hepatotoxicity and associated factors among tuberculosis patients at selected hospitals, Ethiopia","volume":"13","author":"Molla","year":"2021","journal-title":"Hepat Med."},{"key":"B60","doi-asserted-by":"publisher","first-page":"109638","DOI":"10.1016\/j.envres.2020.109638","article-title":"Implications of nonlinearity, confounding, and interactions for estimating exposure concentration-response functions in quantitative risk analysis","volume":"187","author":"Cox","year":"2020","journal-title":"Environ Res."},{"key":"B61","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.3390\/encyclopedia4040118","article-title":"Application of machine learning models in social sciences: managing nonlinear relationships","volume":"4","author":"Kyriazos","year":"2024","journal-title":"Encyclopedia"},{"key":"B62","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1109\/TCBB.2023.3343808","article-title":"Genomic machine learning meta-regression: insights on associations of study features with reported model performance","volume":"21","author":"Barnett","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform."}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1958154\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:29:12Z","timestamp":1789104552000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1958154\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,11]]},"references-count":62,"alternative-id":["10.3389\/fpubh.2026.1958154"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2026.1958154","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,11]]},"article-number":"1958154"}}