Effectiveness of treatment strategies for multdrug resistant tuberculosis
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Abstract
Tuberculosis is one of the leading causes of infectious disease deaths worldwide, and multidrug resistant tuberculosis (MDR-TB) is a threat to its control. As it is resistant to the most effective first-line drugs, its treatment requires use of several second-line drugs that are less effective and can be highly toxic. In recent years, the WHO’s recommendations have been updated to prioritize regimens that use oral medications rather than injectables and ones that do not have to be taken for as long as in the past. The probability of successful treatment outcomes may be decreased by difficulty in adhering to regimens as well as emergence of resistance to second-line agents. In this dissertation, we aim to identify characteristics of participants and treatment regimens that promote successful outcomes. First, we attempt to identify participants that are adherent or non-adherent to regimens using a latent class analysis method. We analyzed data from the Predictors of resistance emergence evaluation in multidrug resistant-tuberculosis patients on treatment (PREEMPT) study, a prospective cohort study of people on treatment for MDR-TB and found that about 80% of the study population were expected to be fully adherent. Then, we assessed the effectiveness of different treatment regimens by examining whether treatment outcomes differ by WHO-recommended treatment regimens. Using the PREEMPT cohort, we found that the longer-term regimens appeared to prevent unsuccessful treatment outcomes but that newer generation drugs might improve outcomes. Finally, we attempted to identify predictors of emergence of resistance to second-line drugs while on treatment. We trained a prediction model using a least absolute shrinkage and selection operator (lasso) model on the Preserving effective tuberculosis treatment (PETTS) cohort study, a study of people on treatment for MDR-TB. We used the PREEMPT data as well as a clinical trial of delamanid vs. standard of care for MDR-TB to validate our prediction model. We found that creation of a well-validated prediction model for emergence of resistance is difficult to achieve because of the rarity of the outcome in our datasets. In summary, we identified a subpopulation of individuals who are less likely to fully adhere to MDR-TB treatment, effectiveness of longer-term WHO regimens, and difficulty of predicting emergence of resistance to second-line TB medications.
Description
2026