<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Tu PHT</submitter><funding>Fonds Wetenschappelijk Onderzoek</funding><pagination>876</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9682818</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>22(1)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>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.&lt;h4>Methods&lt;/h4>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 prescr</pubmed_abstract><journal>BMC infectious diseases</journal><pubmed_title>Bedaquiline resistance probability to guide treatment decision making for rifampicin-resistant tuberculosis: insights from a qualitative study.</pubmed_title><pmcid>PMC9682818</pmcid><funding_grant_id>G0F8316N</funding_grant_id><pubmed_authors>Loos J</pubmed_authors><pubmed_authors>Dippenaar A</pubmed_authors><pubmed_authors>Conceicao EC</pubmed_authors><pubmed_authors>Tu PHT</pubmed_authors><pubmed_authors>Anlay DZ</pubmed_authors><pubmed_authors>Van Rie A</pubmed_authors></additional><is_claimable>false</is_claimable><name>Bedaquiline resistance probability to guide treatment decision making for rifampicin-resistant tuberculosis: insights from a qualitative study.</name><description>&lt;h4>Background&lt;/h4>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.&lt;h4>Methods&lt;/h4>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 prescr</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Nov</publication><modification>2026-06-03T10:40:09.24Z</modification><creation>2025-04-19T20:36:43.404Z</creation></dates><accession>S-EPMC9682818</accession><cross_references><pubmed>36418994</pubmed><doi>10.1186/s12879-022-07865-7</doi></cross_references></HashMap>