A Bayesian approach to estimate the probability of resistance to bedaquiline in the presence of a genomic variant

BACKGROUND

Bedaquiline is a core drug for treatment of rifampicin-resistant tuberculosis. Few genomic variants have been statistically associated with bedaquiline resistance. Alternative approaches for determining the genotypic-phenotypic association are needed to guide clinical care.

METHODS

Using published phenotype data for variants in Rv0678, atpE, pepQ and Rv1979c genes in 756 Mycobacterium tuberculosis isolates and survey data of the opinion of 33 experts, we applied Bayesian methods to estimate the posterior probability of bedaquiline resistance and corresponding 95% credible intervals.

RESULTS

Experts agreed on the role of Rv0678, and atpE, were uncertain about the role of pepQ and Rv1979c variants and overestimated the probability of bedaquiline resistance for most variant types, resulting in lower posterior probabilities compared to prior estimates. The posterior median probability of bedaquiline resistance was low for synonymous mutations in atpE (0.1%) and Rv0678 (3.3%), high for missense mutations in atpE (60.8%) and nonsense mutations in Rv0678 (55.1%), relatively low for missense (31.5%) mutations and frameshift (30.0%) in Rv0678 and low for missense mutations in pepQ (2.6%) and Rv1979c (2.9%), but 95% credible intervals were wide.

CONCLUSIONS

Bayesian probability estimates of bedaquiline resistance given the presence of a specific mutation could be useful for clinical decision-making as it presents interpretable probabilities compared to standard odds ratios. For a newly emerging variant, the probability of resistance for the variant type and gene can still be used to guide clinical decision-making. Future studies should investigate the feasibility of using Bayesian probabilities for bedaquiline resistance in clinical practice.

Bedaquiline resistance probability to guide treatment decision making for rifampicin-resistant tuberculosis: insights from a qualitative study

BACKGROUND

Bedaquiline (BDQ) is a core drug for rifampicin-resistant tuberculosis (RR-TB) treatment. Accurate prediction of a BDQ-resistant phenotype from genomic data is not yet possible. A Bayesian method to predict BDQ resistance probability from next-generation sequencing data has been proposed as an alternative.

METHODS

We performed a qualitative study to investigate the decision-making of physicians when facing different levels of BDQ resistance probability. Fourteen semi-structured interviews were conducted with physicians experienced in treating RR-TB, sampled purposefully from eight countries with varying income levels and burden of RR-TB. Five simulated patient scenarios were used as a trigger for discussion. Factors influencing the decision of physicians to prescribe BDQ at macro-, meso- and micro levels were explored using thematic analysis.

RESULTS

The perception and interpretation of BDQ resistance probability values varied widely between physicians. The limited availability of other RR-TB drugs and the high cost of BDQ hindered physicians from altering the BDQ-containing regimen and incorporating BDQ resistance probability in their decision-making. The little experience with BDQ susceptibility testing and whole-genome sequencing results, and the discordance between phenotypic susceptibility and resistance probability were other barriers for physicians to interpret the resistance probability estimates. Especially for BDQ resistance probabilities between 25% and 70%, physicians interpreted the resistance probability value dynamically, and other factors such as clinical and bacteriological treatment response, history of exposure to BDQ, and resistance profile were often considered more important than the BDQ probability value for the decision to continue or stop BDQ. In this grey zone, some physicians opted to continue BDQ but added other drugs to strengthen the regimen.

CONCLUSIONS

This study highlights the complexity of physicians' decision-making regarding the use of BDQ in RR-TB regimens for different levels of BDQ resistance probability.. Ensuring sufficient access to BDQ and companion drugs, improving knowledge of the genotype-phenotype association for BDQ resistance, availability of a rapid molecular test, building next-generation sequencing capacity, and developing a clinical decision support system incorporating BDQ resistance probability will all be essential to facilitate the implementation of BDQ resistance probability in personalizing treatment for patients with RR-TB.

Quality assessment practice in systematic reviews of mediation studies: results from an overview of systematic reviews

OBJECTIVE

To describe the bias assessment practice in recently published systematic reviews of mediation studies and to evaluate the quality of different bias assessment tools for mediation analysis proposed in the literature.

METHOD

We conducted an overview of systematic reviews by searching MEDLINE (OvidSP), PsycINFO (OvidSP), Cochrane Database of Systematic Reviews (OvidSP), and PubMed databases for systematic reviews of mediation studies published from 2007 to 2020. Two reviewers independently screened the title, abstracts, and full texts of the identified reports and extracted the data. The publications of all mediation-specific quality assessment tools used in these reviews were also identified for the evaluation of the tools' development and validation.

RESULT

Among 103 eligible reviews, 24 (23%) reviews did not assess the risk of bias of eligible studies, and 48 (47%) assessed risk of bias using a tool that was not specifically designed to evaluate mediation analysis. 31 (30.1%) reviews assessed the risk of mediation-specific biases, either narratively or by using specific tools for mediation studies. However, none of these tools were consensus-based, rigorously developed or validated.

CONCLUSION

The quality assessment practice in recently published systematic reviews of mediation studies is suboptimal. To improve the quality and consistency of risk of bias assessments for mediation studies, a consensus-based bias assessment tool is needed.