{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T12:31:51Z","timestamp":1787056311214,"version":"build-2736575974"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Prevention and Control of Emerging and Major Infectious Disease\u2014National Science and Technology Major Project","award":["2025ZD01901005"],"award-info":[{"award-number":["2025ZD01901005"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC2307301"],"award-info":[{"award-number":["2023YFC2307301"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hainan Health Science and Technology Innovation Collaborative Project","award":["WSJK2024MS191"],"award-info":[{"award-number":["WSJK2024MS191"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Microorganisms"],"abstract":"<jats:p>Whole-genome sequencing (WGS) is an increasingly adopted platform for predicting drug resistance in Mycobacterium tuberculosis; however, diagnostic accuracy varies substantially across bioinformatic tools and analytical frameworks, generating considerable uncertainty for clinical laboratory implementation. We conducted a prospectively registered (PROSPERO: CRD420261342739), PRISMA-DTA-compliant systematic review and meta-analysis of diagnostic accuracy studies. PubMed (MEDLINE), Embase, Web of Science, and Cochrane CENTRAL were searched from 1 January 2000 through 28 January 2026. Primary overall sensitivity and specificity were estimated using a tool-level bivariate random-effects model. Exploratory subgroup analyses and meta-regression examined the association between algorithm category and diagnostic-performance heterogeneity. Twenty-eight drug-level evaluations from seven tools (rifampicin, isoniazid, ethambutol, and pyrazinamide for each tool) were compiled from the extracted 2 \u00d7 2 data. For the primary tool-level composite analysis, pooled sensitivity was 0.930 (95% CI: 0.907\u20130.948) and pooled specificity was 0.962 (95% CI: 0.929\u20130.981). In secondary drug-specific analyses, sensitivity was highest for rifampicin (0.960, 95% CI: 0.934\u20130.976) and isoniazid (0.933, 95% CI: 0.906\u20130.953), and lowest for pyrazinamide (0.860, 95% CI: 0.800\u20130.904). Exploratory tool-level comparisons produced pooled sensitivity estimates of 0.920 for rule-based tools, 0.899 for machine learning tools, and 0.951 for hybrid tools. These comparisons involved only seven tool-level analytic units and cannot disentangle algorithm type from individual tool identity, training data, mutation catalogue version, or validation population. WGS-based bioinformatic tools provide highly specific and generally sensitive predictions of Mycobacterium tuberculosis resistance for first-line drugs across diverse clinical settings. Exploratory differences between tool categories should not be interpreted as causal effects of algorithmic architecture. Future studies should use prospective head-to-head evaluations on shared, geographically diverse isolate collections, alongside continued improvement of resistance catalogues and external validation.<\/jats:p>","DOI":"10.3390\/microorganisms14081824","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T12:09:32Z","timestamp":1787054972000},"page":"1824","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Algorithmic Determinants of Performance Heterogeneity in Whole-Genome Sequencing-Based Prediction of Drug Resistance in Mycobacterium tuberculosis: A Systematic Review and Meta-Analysis"],"prefix":"10.3390","volume":"14","author":[{"given":"Baozhen","family":"Peng","sequence":"first","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing 102206, China"},{"name":"Chinese Field Epidemiology Training Program, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing 100050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing 102206, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangchen","family":"Li","sequence":"additional","affiliation":[{"name":"Cosmos Wisdom Mass Spectrometry Center, Zhejiang University Medical School, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huihui","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Field Epidemiology Training Program, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing 100050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing 102206, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Hou","sequence":"additional","affiliation":[{"name":"Tuberculosis Reference Laboratory, Hainan Provincial Center for Disease Control and Prevention, Haikou 570203, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3126-4450","authenticated-orcid":false,"given":"Xichao","family":"Ou","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing 102206, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7890-4312","authenticated-orcid":false,"given":"Yanlin","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing 102206, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,18]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2025). 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