Bacterial load slopes represent biomarkers of tuberculosis therapy success, failure, and relapse.

ABSTRACT: There is an urgent need to discover biomarkers that are predictive of long-term TB treatment outcomes, since treatment is expense and prolonged to document relapse. We used mathematical modeling and machine learning to characterize a predictive biomarker for TB treatment outcomes. We computed bacterial kill rates, γ_f for fast- and γ_s for slow/non-replicating bacteria, using patient sputum data to determine treatment duration by computing time-to-extinction of all bacterial subpopulations. We then derived a γ_s-slope-based rule using first 8 weeks sputum data, that demonstrated a sensitivity of 92% and a specificity of 89% at predicting relapse-free cure for 2, 3, 4, and 6 months TB regimens. In comparison, current methods (two-month sputum culture conversion

SUBMITTER: Magombedze G 

PROVIDER: S-EPMC8172544 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature