AI Article Synopsis
- There is an urgent need for predictive biomarkers to assess long-term TB treatment outcomes, as current methods are expensive and time-consuming.
- Using mathematical modeling and machine learning, researchers identified a promising biomarker based on the bacterial kill rates during the first 8 weeks of treatment.
- This new model showed a high sensitivity of 92% and specificity of 89% in predicting relapse-free cure, outperforming existing methods significantly, which have sensitivities below 34%.
Article 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 for fast- and γ 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 γslope-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 and the Extended-EBA) methods performed poorly, with sensitivities less than 34%. These biomarkers will accelerate evaluation of novel TB regimens, aid better clinical trial designs and will allow personalization of therapy duration in routine treatment programs.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172544 | PMC |
| http://dx.doi.org/10.1038/s42003-021-02184-0 | DOI Listing |
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