Diagnostic Accuracy of Nanopore Sequencing for Detecting Mycobacterium Tuberculosis and Drug-resistant Strains: A Systematic Review and Meta-analysis

• Main Authors: Carandang, Timothy Hudson David Culasino, Cunanan, Dianne Jaula, Co, Gail S, Pilapil, John David, Garcia, Juan Ignacio, Restrepo, Blanca I., Yotebieng, Marcel, Torrelles, Jordi B., Notarte, Kin Israel
• Format: text
• Published: Archīum Ateneo 2025
• Subjects: Detection; Drug resistance; GridION; MinION; Third-generation sequencing; Tuberculosis; Analytical, Diagnostic and Therapeutic Techniques and Equipment; Bacterial Infections and Mycoses; Diagnosis; Medicine and Health Sciences; Respiratory Tract Diseases
• Online Access: https://archium.ateneo.edu/asmph-pubs/305; https://archium.ateneo.edu/context/asmph-pubs/article/1309/viewcontent/s41598_025_90089_x.pdf
• Institution: Ateneo De Manila University

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB) infection, remains a significant public health threat. The timeliness, portability, and capacity of nanopore sequencing for diagnostics can aid in early detection and drug susceptibility testing (DST), which is crucial for effective TB control. This study synthesized current evidence on the diagnostic accuracy of the nanopore sequencing technology in detecting MTB and its DST profile. A comprehensive literature search in PubMed, Scopus, MEDLINE, Cochrane, EMBASE, Web of Science, AIM, IMEMR, IMSEAR, LILACS, WPRO, HERDIN Plus, MedRxiv, and BioRxiv was performed. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Pooled sensitivity, specificity, predictive values (PV), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated. Thirty-two studies were included; 13 addressed MTB detection only, 15 focused on DST only, and 4 examined both MTB detection and DST. No study used Flongle or PromethION. Seven studies were eligible for meta-analysis on MTB detection and five for DST; studies for MTB detection used GridION only while those for DST profile used MinION only. Our results indicate that GridION device has high sensitivity [88.61%; 95% CI (83.81–92.12%)] and specificity [93.18%; 95% CI (85.32–96.98%)], high positive predictive value [94.71%; 95% CI (89.99–97.27%)], moderately high negative predictive value [84.33%; 95% CI (72.02–91.84%)], and excellent DOR [107.23; 95% CI (35.15–327.15)] and AUC (0.932) in detecting MTB. Based on DOR and AUC, the MinION excelled in detecting pyrazinamide and rifampicin resistance; however, it underperformed in detecting isoniazid and ethambutol resistance. Additional studies will be needed to provide more precise estimates for MinION’s sensitivity in detecting drug-resistance, as well as DOR in detecting resistance to pyrazinamide, streptomycin, and ofloxacin. Studies on detecting resistance to bedaquiline, pretomanid, and linezolid are lacking. Subgroup analyses suggest that overall accuracy of MTB detection tends to be higher with prospective study design and use of standards other than CSTB (Chinese national standard for diagnosing TB). Sensitivity analyses reveal that retrospective study design, use of GridION, and use of Illumina whole-genome sequencing (WGS) decrease overall accuracy in detecting any drug-resistant MTB. Findings from both types of analyses, however, should be interpreted with caution because of the low number of studies and uneven distribution of studies in each subgroup.