"Evaluating treatment effect heterogeneity and optimal regimens in multidrug resistant tuberculosis using causal inference modeling".
Résumé en Français
With the increase in antibiotic resistance, the treatment of certain strains of tuberculosis (TB) is becoming more challenging. TB caused 1.4 million deaths in 2014 making it the single most deadly bacterial infection worldwide. Multidrug resistant tuberculosis (MDR-TB) is a condition afflicting an estimated 480 000 globally, and is defined as TB that is resistant to the primary antibiotics used to fight the disease. MDR-TB is treated using different combinations of alternative antibiotics. What is not known is which types of patients respond better to which combinations of antibiotics, and which patient characteristics are most closely related to successful treatment. In order to use statistics to answer this question, a large amount of data is needed. Previous studies (with limited numbers of patients sharing their data) did not have enough information for investigators to unravel the relationships between patient characteristics and the probability of curing the disease. The objectives of our study are to 1) Learn which patient characteristics affect treatment success for different combinations of antibiotics, and 2) Design treatment strategies to maximize the success of treatment for MDR-TB. Our team has previously performed a large review of existing studies and has combined all available data into a much larger dataset. This dataset will allow us to investigate which patient characteristics (such as age, sex and antibiotic resistance) can help predict the success of specific treatments. We will use this information to statistically construct patient-specific treatment guidelines, which will ideally be used by doctors to treat their patients.
We greatly appreciate the support of CIHR and are excited to continue our work on this project!