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

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. Following the systematic literature search, the diagnostic test accuracies for 14 drugs were pooled using a random-effect bivariate model. 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. WGS accurately identifies isoniazid and rifampicin resistance. For most drugs, positive WGS results reliably predict pDST positive. The two approaches had similar ability.

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