2026-10-14T08:35:09Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33814915 2026-09-15T18:02:49Z category_7 category_21 category_734 category_931 category_811 category_64 category_132 category_134 portal_5 item_type_3 month_year_09_2026

The study dataset. Tianfang Shao (16843152) Mariana R. Neves (24950380) Molly Franke (488283) Carole Mitnick (4615363) Jennifer Furin (435179) Ted Cohen (118302) Reza Yaesoubi (207900) Medicine Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Cancer Infectious Diseases Virology xlink "> fluoroquinolones world health organization support clinical decision shorter regimens endorsed sharing platform curated patient characteristics observable including case definition employment variables showed pooled models </ external data could consistently informative predictors country models </ several countries cannot developed prediction models fluoroquinolones among patients auprc reaching 0 prediction models models developed related variables country data typically developed auprc ranging auprc ). used data access data single country whereas demographic three strategies results submitted results may relatively stable recall curve rapid testing neural networks national institute model discrimination marked changes limited set infectious diseases findings highlight externally validated corrected discrimination analysis dataset 175 patients <div> BackgroundFluoroquinolones (FQs) are a cornerstone of most all-oral, shorter regimens endorsed by the World Health Organization for the treatment of rifampicin-resistant or multidrug-resistant tuberculosis (RR/MDR-TB). Knowledge of resistance to FQs can help guide regimen selection at the point of care. In settings where rapid testing for FQ resistance is unavailable, prediction models could support treatment decisions by identifying FQ resistance based on patient characteristics observable at the point of care. These prediction models have been typically developed and evaluated within a single country, and their generalizability across different geographic settings is unclear. Methods and findingsWe used data from 5,175 patients with RR-TB and available FQ drug susceptibility testing (DST) results submitted to the TB Portals, an open-access data-sharing platform curated by the National Institute of Allergy and Infectious Diseases, from eight countries (Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Romania and Ukraine) between 2012 and 2024. Among these patients, 1,772 (34.2%) had FQ-resistant TB. We developed prediction models for FQ resistance using logistic regression, neural networks, and XGBoost. Models were evaluated under three strategies: (1) pooled models trained on multi-country data; (2) within-country models trained and evaluated using internal validation; and (3) cross-country models trained on subsets of countries and externally validated on held-out countries. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Across the three algorithms, pooled models showed moderate optimism-corrected discrimination, with AUROC ranging from 0.70 to 0.72 and AUPRC ranging from 0.57 to 0.59. Within-country models demonstrated slightly better discrimination, with AUROC and AUPRC reaching 0.8 for some countries. Cross-country external validation showed that performance loss from using a model trained on external data could be negligible to >0.1 AUPROC or AUROC, depending on the country and algorithm. A limited set of predictors, including case definition and treatment-history-related variables, were among the most consistently informative predictors, whereas demographic, comorbidity, social-risk, education, and employment variables showed more variable contributions across countries and algorithms. A limitation of our study is that the incidence of RR-TB and FQ resistance was relatively stable in our analysis dataset. Hence, the results may not generalize to scenarios with marked changes in MDR-TB dynamics. ConclusionsPredicting FQ resistance using demographic and clinical characteristics showed moderate ability to identify FQ resistance among patients with RR-TB, but their performance and predictor patterns varied across countries. Models developed for one or several countries cannot be assumed to generalize to other settings without rigorous external validation. These findings highlight the limitations of globally trained prediction models for RR/MDR-TB and underscore the need for locally informed prediction models to support clinical decision-making.</div> 2026-09-15T18:05:18Z Dataset Dataset 10.1371/journal.pmed.1004965.s003 https://figshare.com/articles/dataset/_p_The_study_dataset_p_/33814915 CC BY 4.0