Analysis of Drug Resistance Characteristics and Risk Factors of Cavitary Tuberculosis Based on Whole Genome...

Pulmonary cavitation, a high-burden driver of pulmonary tuberculosis (PTB) transmission, necessitates targeted interventions. This study comprehensively analyzes the drug-resistance profiles and risk factors associated with cavitary TB to inform precise prevention and control strategies.

This study analyzed 1247 cavitary PTB patients from Guangxi (2020-2024) with complete strain and clinical data. Whole-genome sequencing (WGS) characterized strain lineages and drug resistance. Key predictors were selected using Lasso regression, and the optimal model was identified from 9 machine learning (ML) models based on AUC, Shapley Additive Explanations (SHAP) elucidated feature contributions to severe cavitary TB risk.

Lineages were primarily classified as Lineage 2 (65.20%), Lineage 4 (29.91%), and 35 cases of mixed infections (2.81%). Concordance between WGS and phenotypic drug susceptibility testing was moderate for Isoniazid (INH) (κ = 0.634; χ ² = 32.667, P < .001) but good for rifampicin (RFP) (κ = 0.774; difference not significant). Predominant mutations were rpoB_p.Ser450Leu (RFP), katG_p.Ser315Thr (INH), embB_p.Met306Val (Ethambutol, EMB), and rpsL_p.Lys43Arg (Streptomycin, S). Lasso regression selected 10 variables: fatigue, fever, history of previous TB treatment, gender, age, RFP, INH, occupation, S, and ethnicity. After evaluating 9 ML models, the Gradient Boosting Machine was selected as optimal. SHAP analysis identified fatigue, older age, history of TB treatment, fever, male, and rpoB_p.Ser450 mutation were positively associated with severe cavity formation.

The rpoB_p.Ser450 mutation is linked to severe cavitary PTB, but clinical and population studies are still needed to confirm this association. Therefore, new tools based on clinical indicators and biomarkers are needed to achieve early warning and timely intervention for the risk of severe cavitation.