Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis
Abstract
Tuberculosis (TB) afflicted 10.6 million people in 2021, and its global burden is increasing due to multidrug-resistant TB (MDR-TB) and extensively resistant TB (XDR-TB). Here, we analyze multi-domain information from 5,060 TB patients spanning 10 countries with high burden of MDR-TB from the NIAID TB Portals database to determine predictors of TB treatment outcome. Our analysis revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities. Our machine learning model, built with 203 features across modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 83% and area under the curve of 0.84. Notably, our analysis revealed that the drug regimens Bedaquiline-Clofazimine-Cycloserine-Levofloxacin-Linezolid and Bedaquiline-Clofazimine-Linezolid-Moxifloxacin were associated with treatment success and failure, respectively, for MDR non-XDR-TB. Drug combinations predicted to be synergistic by the INDIGO algorithm performed better than antagonistic combinations. Our prioritized set of features predictive of treatment outcomes can ultimately guide the personalized clinical management of TB.
Read the paper
Related studies
- Ferulic acid regulates the AKT/GSK-3β/CRMP-2 signaling pathway in a middle cerebral artery occlusion animal model
- Novel Pharmacokinetic/Pharmacodynamic Parameters Quantify the Exposure–Effect Relationship of Levofloxacin against Fluoroquinolone-Resistant Escherichia coli
- Photochemical behavior of levofloxacin
- Correlation Between Dissolution Profiles of Salt-Form Drugs in Biorelevant Bicarbonate Buffer and Oral Drug Absorption: Importance of Dose/ Fluid Volume Ratio
- Evaluation of the synergistic potential and mechanisms of action for de novo designed cationic antimicrobial peptides
- Synthesis of [11C]levofloxacin
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.