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

Abstract

Abstract 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 availability of BDQ and companion RR-TB drugs, the cost of BDQ, and the need for consultation with the clinical advisory committee shaped physicians' view on BDQ use and how they weighed BDQ resistance probability in their decision-making. Physicians’ view on the role of BDQ and accuracy of drug susceptibility testing impacted their perception of the BDQ resistance probability estimate. Physicians’ interpretation of BDQ resistance probability values varied widely. Probabilities between 25% and 70% were often seen as a grey zone, where physicians interpret the BDQ resistance probability dynamically, considering patient characteristics, including treatment response, history of exposure to BDQ, and resistance profile. In the 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. Structural barriers, physicians’ views on accuracy of drug susceptibility testing and patient characteristics influenced BDQ prescription and interpretation of the BDQ resistance probability. The development of a clinical decision support system incorporating BDQ resistance probability could facilitate the use of next generation sequencing and implementation of BDQ resistance probability in personalizing treatment for patients with RR-TB.