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

Abstract

ABSTRACTBackgroundTuberculosis is expensive to treat, especially since therapy duration is at least six-months, and patients must be followed for up to two years in order to document relapse. There is an urgent need to discover biomarkers that are predictive of long-term treatment outcomes. Currently, tuberculosis programs use two-months sputum conversion for clinical decision making, while phase I clinical trials use extended [14 day] early bactericidal activity [EBA] to triage regimens. Our objective was to develop early treatment stage biomarkers that are predictive of long-term outcomes.Methods and FindingsData from 1,924 patients in the REMoxTB study was divided into [1] a derivation data-set of 318 patients on six-months standard therapy, [2] two sets of validation datasets comprised of 319 patients on six-months standard therapy, and 1,287 patients randomized to four-months experimental therapy. Sputum time-to-positivity [TTP] data was modeled using a system of ordinary differential equations that identified bacillary kill rates [termed γ-slopes], for fast-replicating bacteria [γf] and for semi-dormant/non-replicating persistent bacteria [γs], and to estimate time-to-extinction for all bacteria sub-populations in each patient. Time-to-extinction is used to predict the minimum therapy duration required to achieve cure. Using the derivation dataset, machine learning identified the γs slope, calculated using first 8 weeks of therapy TTP data, as the highest ranked predictor for treatment outcomes. We then computed γs slope thresholds that would reliably predict relapse-free cure for 2, 3, 4, and 6 months therapy duration regimens, and used these to create a diagnostic rule. In the first-validation dataset for six-months therapy duration, the γs-derived decision rule demonstrated a sensitivity of 92% and a specificity of 89%; among patients with positive biomarker the relative risk [RR] of failure was 20.40 [95% confidence interval (CI): 7.17-58.08]. In comparison, two-month sputum culture conversion had a sensitivity of 33% and specificity of 71% [RR=1.20 (95% CI: 0.60-2.34)], while for extended-EBA sensitivity was 14% and specificity was 92% [RR=1.71 [95% CI: 0.73-3.48]. In the second validation dataset for four-months therapy duration, the γs-derived diagnostic rule sensitivity was 81% while specificity was 87% for picking failure versus cure [RR=14.51 (95% CI: 8.33-25.41)]ConclusionsThe ability to predict treatment outcomes during the first eight-weeks of therapy could accelerate evaluation of novel regimens, development of new clinical trial designs, as well as allow personalization of therapy duration in routine treatment programs. Future research applying these diagnostic rules to different clinical trials data are required.