Assessment of epidemiological and genetic characteristics and clinical outcomes of resistance to bedaquiline in patients treated for rifampicin-resistant tuberculosis: a cross-sectional and longitudinal study.

BACKGROUND: Bedaquiline improves outcomes of patients with rifampicin-resistant and multidrug-resistant (MDR) tuberculosis; however, emerging resistance threatens this success. We did a cross-sectional and longitudinal analysis evaluating the epidemiology, genetic basis, and treatment outcomes associated with bedaquiline resistance, using data from South Africa (2015-19). METHODS: Patients with drug-resistant tuberculosis starting bedaquiline-based treatment had surveillance samples submitted at baseline, month 2, and month 6, along with demographic information. Culture-positive baseline and post-baseline isolates had phenotypic resistance determined. Eligible patients were aged 12 years or older with a positive culture sample at baseline or, if the sample was invalid or negative, a sample within 30 days of the baseline sample submitted for bedaquiline drug susceptibility testing. For the longitudinal study, the first surveillance sample had to be phenotypically susceptible to bedaquiline for inclusion. Whole-genome sequencing was done on bedaquiline-resistant isolates and a subset of bedaquiline-susceptible isolates. The National Institute for Communicable Diseases tuberculosis reference laboratory, and national tuberculosis surveillance databases were matched to the Electronic Drug-Resistant Tuberculosis Register. We assessed baseline resistance prevalence, mutations, transmission, cumulative resistance incidence, and odds ratios (ORs) associating risk factors for resistance with patient outcomes. FINDINGS: Between Jan 1, 2015, and July 31, 2019, 8041 patients had surveillance samples submitted, of whom 2023 were included in the cross-sectional analysis and 695 in the longitudinal analysis. Baseline bedaquiline resistance prevalence was 3·8% (76 of 2023 patients; 95% CI 2·9-4·6), and it was associated with previous exposure to bedaquiline or clofazimine (OR 7·1, 95% CI 2·3-21·9) and with rifampicin-resistant or MDR tuberculosis with additional resistance to either fluoroquinolones or injectable drugs (pre-extensively-drug resistant [XDR] tuberculosis: 4·2, 1·7-10·5) or to both (XDR tuberculosis: 4·8, 2·0-11·7). Rv0678 mutations were the sole genetic basis of phenotypic resistance. Baseline resistance could be attributed to previous bedaquiline or clofazimine exposure in four (5·3%) of 76 patients and to primary transmission in six (7·9%). Odds of successful treatment outcomes were lower in patients with baseline bedaquiline resistance (0·5, 0·3-1). Resistance during treatment developed in 16 (2·3%) of 695 patients, at a median of 90 days (IQR 62-195), with 12 of these 16 having pre-XDR or XDR. INTERPRETATION: Bedaquiline resistance was associated with poorer treatment outcomes. Rapid assessment of bedaquiline resistance, especially when patients were previously exposed to bedaquiline or clofazimine, should be prioritised at baseline or if patients remain culture-positive after 2 months of treatment. Preventing resistance by use of novel combination therapies, current treatment optimisation, and patient support is essential. FUNDING: National Institute for Communicable Diseases of South Africa.

Evaluating the performance of propensity score matching based approaches in individual patient data meta-analysis.

BACKGROUND: Individual-patient data meta-analysis (IPD-MA) is an increasingly popular approach because of its analytical benefits. IPD-MA of observational studies must overcome the problem of confounding, otherwise biased estimates of treatment effect may be obtained. One approach to reducing confounding bias could be the use of propensity score matching (PSM). IPD-MA can be considered as two-stage clustered data (patients within studies) and propensity score matching can be implemented within studies, across studies, and combining both. METHODS: This article focuses on implementation of four PSM-based approaches for the analysis of data structure that exploit IPD-MA in two ways: (i) estimation of propensity score model using single-level or random-effects logistic regression; and (ii) matching of propensity scores (PS) across studies, within studies or preferential-within studies. We investigated the performance of these approaches through a simulation study, which considers an IPD-MA that examined the success of different treatments for multidrug-resistant tuberculosis (MDR-TB). The simulation parameters were varied according to three treatment prevalences (according to studies, 50% and 30%), three levels of heterogeneity between studies (low, moderate and high) and three levels of pooled odds ratio (1, 1.5, 3). RESULTS: All approaches showed greater biases at the higher levels of heterogeneity regardless of the choices of treatment prevalences. However, matching of propensity scores using within-study and preferential-within study reported better performance compared to matching across studies when treatment prevalence varied across-studies. For fixed prevalences, a random-effect propensity score model to estimate propensity scores followed by matching of propensity scores across-studies achieved lower biases compared to other PSM-based approaches. CONCLUSIONS: Propensity score matching has wide application in health research while only limited literature is available on the implementation of PSM methods in IPD-MA, and until now methodological performance of PSM methods have not been examined. We believe, this work offers an intuition to the applied researcher for the choice of the PSM-based approaches.