作者
Yoichi Tagami,Nobuyuki Horita,Megumi Kaneko,Susumu Muraoka,Naoto Fukuda,Ami Izawa,Ayami Kaneko,Kohei Somekawa,Chisato Kamimaki,Hiromi Matsumoto,Katsushi Tanaka,Kota Murohashi,Ayako Aoki,Hiroaki Fujii,Keisuke Watanabe,Yu Hara,Nobuaki Kobayashi,Takeshi Kaneko
摘要
Abstract Background For simultaneous prediction of phenotypic drug susceptibility test (pDST) for multiple antituberculosis drugs, the whole genome sequencing (WGS) data can be analyzed using either a catalog-based approach, wherein 1 causative mutation suggests resistance, (eg, World Health Organization catalog) or noncatalog-based approach using complicated algorithm (eg, 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 2 approaches. Methods Following a systematic literature search, the diagnostic test accuracies for 14 drugs were pooled using a random-effect bivariate model. Results Of 779 articles, 44 with 16 821 specimens for meta-analysis and 13 not for meta-analysis were included. The areas under summary receiver operating characteristic curve suggested test accuracy was 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 noncatalog-based and catalog-based approaches had similar ability for all drugs. Conclusions WGS accurately identifies isoniazid and rifampicin resistance. For most drugs, positive WGS results reliably predict pDST positive. The 2 approaches had similar ability. Clinical Trials Registration UMIN-ID UMIN000049276.