Whole-genome sequencing predicting phenotypic antitubercular drug resistance: meta-analysis.

BACKGROUND: For simultaneous prediction of phenotypic drug susceptibility test (pDST) for multiple anti-tuberculosis drugs, the whole genome sequencing (WGS) data can be analyzed using either catalogue-based approach, wherein one causative mutation suggests resistance, (e.g., WHO catalog) or non-catalogue-based approach using complicated algorithm (e.g., TB-profiler, machine learning). The aim was to estimate the predictive ability of WGS-based tests with pDST as the reference, and to compare the two approaches. METHODS: Following the systematic literature search, the diagnostic test accuracies for 14 drugs were pooled using a random-effect bivariate model. RESULTS: Out of 779 articles, 44 articles with 16,821 specimens for meta-analysis and 13 articles not for meta-analysis were adopted. The areas under summary receiver operating characteristic curve suggested "excellent" (0.97-1.00) for 2 drugs (isoniazid 0.975, rifampicin 0.975), "very good" (0.93-0.97) for 8 drugs (pyrazinamide 0.946, streptomycin 0.952, amikacin 0.968, kanamycin 0.963, capreomycin 0.965, para-aminosalicylic acid 0.959, levofloxacin 0.960, ofloxacin 0.958), and "good" (0.75-0.93) for 4 drugs (ethambutol 0.926, moxifloxacin 0.896, ethionamide 0.878, prothionamide 0.908). The non-catalogue-based and catalogue-based approaches had similar ability for all drugs. CONCLUSION: WGS accurately identifies isoniazid and rifampicin resistance. For most drugs, positive WGS results reliably predict pDST positive. The two approaches had similar ability.