Predictive Analysis of Drug-Resistant Tuberculosis: Integrating Molecular Markers, Clinical Governance, and Community-Engaged Education in Rural South Africa

Background: Drug-resistant tuberculosis (DR-TB) remains a critical challenge in high-burden rural settings. This study aims to bridge genomic resistance markers with machine learning to create a predictive model for timely diagnosis and the prevention of multidrug-resistant tuberculosis (MDR-TB). Methods: We conducted a retrospective analysis of clinical, demographic, and genomic data from 207 Mycobacterium tuberculosis isolates (representing 207 unique patients). Resistance was categorized as any resistance or MDR-TB.” Predictors included age, sex, and key mutations (S315T, -15C>T, and substitutions). Logistic regression (LR) was used to calculate adjusted odds ratios (aORs), while Random Forest (RF) assessed non-linear feature importance. Model validity was confirmed via 10-fold cross-validation. A Systems Network Analysis mapped the integration of model outputs into Clinical Governance (SOPs/KPIs) and Community-Engaged Education. Results: Resistance to at least one drug was found in 58.9% of isolates, with 21.7% classified as MDR-TB. Predominant mutations included S315T (29.0%) and S450L (26.6%). LR identified S450L (aOR 4.20, 95% CI: 2.10–8.45) and S315T (aOR 2.85, 95% CI: 1.40–5.80) as the strongest predictors; demographic variables (age, sex) were not statistically significant. Models achieved high discriminative power (AUC: 0.96 for any resistance; 0.99 for MDR-TB). Risk stratification categorized 18% of patients as high risk. Simulations showed that prioritizing high-risk patients for reflex LPA testing could reduce the median time to appropriate treatment from 14 to 3 days, potentially preventing 12–15% of Isoniazid-resistant TB (Hr-TB) cases from progressing to MDR-TB. Conclusion: Integrating molecular markers with machine learning enables highly accurate, risk-stratified clinical interventions. By embedding these predictive models into governance frameworks and utilizing Community Health Workers (CHWs) for targeted education, rural health systems can significantly reduce diagnostic delays and interrupt the progression of MDR-TB.