BackgroundClinical bedaquiline resistance predominantly involves mutations inmmpR5(Rv0678). However,mmpR5resistance-associated variants (RAVs) have a variable relationship with phenotypicM. tuberculosisresistance. We performed a systematic review to (1) assess the maximal sensitivity of sequencing bedaquiline resistance-associated genes and (2) evaluate the association between RAVs and phenotypic resistance, using traditional and machine-based learning techniques.MethodsWe screened public databases for articles published until October 2022. Eligible studies performed sequencing of at leastmmpR5andatpEon clinically-sourcedM. tuberculosisisolates and measured bedaquiline minimum inhibitory concentrations (MICs). We performed genetic analysis for identification of phenotypic resistance and determined the association of RAVs with resistance. Machine-based learning methods were employed to define test characteristics of optimised sets of RAVs, andmmpR5mutations were mapped to the protein structure to highlight mechanisms of resistance.ResultsEighteen eligible studies were identified, comprising 975M. tuberculosisisolates containing ≥1 potential RAV (mutation inmmpR5, atpE, atpBorpepQ), with 201 (20.6%) demonstrating phenotypic bedaquiline resistance. 84/285 (29.5%) resistant isolates had no candidate gene mutation. Sensitivity and positive predictive value of taking an ‘any mutation’ approach was 69% and 14% respectively. Thirteen mutations, all inmmpR5, had a significant association with a resistant MIC (adjusted p<0.05). Gradient-boosted machine classifier models for predicting intermediate/resistant and resistant phenotypes both had receiver operator characteristic c-statistics of 0.73. Frameshift mutations clustered in the alpha 1 helix DNA binding domain, and substitutions in the alpha 2 and 3 helix hinge region and in the alpha 4 helix binding domain.DiscussionSequencing candidate genes is insufficiently sensitive to diagnose clinical bedaquiline resistance, but where identified a limited number of mutations should be assumed to be associated with resistance. Genomic tools are most likely to be effective in combination with rapid phenotypic diagnostics.