Diagnostic Accuracy of Nanopore Sequencing for DetectingMycobacterium tuberculosisand Drug-Resistant Strains: A Systematic Review and Meta-Analysis

Diagnostic Accuracy of Nanopore Sequencing for Detecting Mycobacterium tuberculosis and Drug-Resistant Strains: A Systematic Review and Meta-Analysis | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Diagnostic Accuracy of Nanopore Sequencing for Detecting Mycobacterium tuberculosis and Drug-Resistant Strains: A Systematic Review and Meta-Analysis Timothy Hudson David Culasino Carandang , Dianne Jaula Cunanan , Gail S. Co , John David Pilapil , Juan Ignacio Garcia , View ORCID Profile Blanca I. Restrepo , Marcel Yotebieng , Jordi B. Torrelles , View ORCID Profile Kin Israel Notarte doi: https://doi.org/10.1101/2024.06.05.24308511 Timothy Hudson David Culasino Carandang 1 Pamantasan ng Lungsod ng Maynila College of Medicine , Manila, 1002, Philippines Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dianne Jaula Cunanan 2 St. Luke’s Medical Center College of Medicine , Quezon City, 1112, Philippines Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gail S. Co 3 Ateneo School of Medicine and Public Health , Pasig, 1604, Philippines Find this author on Google Scholar Find this author on PubMed Search for this author on this site John David Pilapil 4 Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology , Hong Kong SAR, 999077, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Juan Ignacio Garcia 5 Tuberculosis Group, Disease Intervention & Prevention and Population Health Programs, Texas Biomedical Research Institute , San Antonio, TX, 78227, US 6 International Center for the Advancement of Research & Education (I•CARE), Texas Biomedical Research Institute , San Antonio, TX, 78227, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site Blanca I. Restrepo 6 International Center for the Advancement of Research & Education (I•CARE), Texas Biomedical Research Institute , San Antonio, TX, 78227, US 7 University of Texas Health Science Center at Houston, School of Public Health , Brownsville campus, Brownsville, TX, 7852, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Blanca I. Restrepo Marcel Yotebieng 8 Division of General Internal Medicine, Department of Medicine, Albert Einstein College of Medicine , Bronx, NY, 10461, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jordi B. Torrelles 5 Tuberculosis Group, Disease Intervention & Prevention and Population Health Programs, Texas Biomedical Research Institute , San Antonio, TX, 78227, US 6 International Center for the Advancement of Research & Education (I•CARE), Texas Biomedical Research Institute , San Antonio, TX, 78227, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kin Israel Notarte 9 Department of Pathology, Johns Hopkins University School of Medicine , Baltimore, MD, 21205, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kin Israel Notarte For correspondence: knotart1{at}jhmi.edu kinotarte{at}gmail.com Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT 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. INTRODUCTION Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB) infection, remains a significant public health threat, with an estimated 10 million new infections annually 1 . In 2022, it was the second leading cause of mortality among infectious diseases after COVID-19 2 . Though affecting all countries and age groups, TB is most prevalent in low- and middle-income countries (LMICs), with almost half of the cases occurring in mainly eight countries (Bangladesh, China, India, Indonesia, Nigeria, Pakistan, Philippines, and South Africa) 2 . Early detection and diagnosis can dramatically affect treatment outcomes and prognosis, preventing the development of drug resistance. Yet, some diagnostic challenges present, such as lack of point-of-care (POC) tests that can be accessed in remote areas and the long turnaround time of the culture, which to this date remains the gold standard for diagnosis 3 . Nucleic acid amplification tests (NAAT) such as GeneXpert ® MTB/RIF (Xpert) have improved the detection and management of TB. Due to its timeliness, high sensitivity, and high specificity compared to acid-fast bacilli (AFB) smears and cultures, the Xpert has gained widespread use, particularly in diagnosing multi-drug resistant (MDR) TB, as well as TB in people living with HIV (PLWH). Numerous studies have been performed evaluating Xpert’s utility 4 , 5 , prompting policy recommendations from the World Health Organization (WHO) 6 . While Xpert offers rapid diagnosis, its high laboratory cost, encompassing consumables, annual calibrations, and maintenance, restricts its use in resource-limited settings where access to NAAT technologies might be inconsistent. 4 , 7 , 8 Recent developments in third-generation sequencing technology, such as Oxford Nanopore Technologies sequencing platforms, have shown several advantages over other diagnostic tools used for TB. In this context, nanopore sequencing is being applied in full-length transcript detection and base modification detection focused on rapid clinical diagnoses. MTB is an ideal candidate for analysis since nanopore sequencing excels in the investigation of complex genomic loci and large repetitive elements 9 . The ability of nanopore sequencing to generate long read lengths and conduct real-time analysis offers rapid detection and comprehensive profiling of drug resistance, leading to prompt and suitable treatment strategies, minimizing treatment-related costs, and improving patient outcomes. Its portability and capacity to directly sequence nucleic acid molecules simplify the workflow and allow on-site sequencing, improving access to quality diagnostics even in LMICs 10 . This technology depends on nanoscale protein pores working as biosensors of changes in ionic current as the DNA or RNA is translocated into each pore 11 . Each DNA or RNA base has a fixed size that blocks current flow as the strand is translocated, registering a unique and interpretable alteration in current. To date, there is no systematic review nor meta-analysis of existing studies on the diagnostic accuracy of nanopore sequencing for TB detection and DST. To address this important knowledge gap, this study aims to appraise and synthesize current evidence on the sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), diagnostic odds ratio (DOR) and area under the curve (AUC) of nanopore sequencing technology in detecting MTB and its drug-resistant patterns. METHODS Study protocol and registration - The study protocol was submitted to The International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42024518764) and reported in compliance with the PRISMA 2020 guidelines 12 . Search strategy - Literature searches were independently conducted by two different co-authors using the following databases: PubMed, Scopus, MEDLINE, Cochrane, EMBASE, Web of Science, AIM, IMEMR, IMSEAR, LILACS, WPRO, HERDIN Plus, MedRxiv and BioRxiv. Separate comprehensive searches were done for studies about MTB detection and drug susceptibility. As the first nanopore sequencing platform, namely minION, was first commercialized in 2014, our search range was narrowed down to English articles published from January 2014 until February 2024 13 . The selected reference list of each article was also screened to identify studies relevant to this review. The following search terms were used: “ Mycobacterium tuberculosis ” OR “ M. tuberculosis” AND “nanopore sequencing” OR “nanopore-based sequencing” OR “Oxford nanopore” OR “nanopore technology” OR “Flongle” OR “minION” OR “gridION” OR “promethION”. The combination of these search terms is detailed in the supplementary materials. Study selection - All studies that compare nanopore sequencing technology to any conventional diagnostic test or reference standard in MTB detection and/or DST on clinical specimens was included except reviews, meta-analyses, commentary papers, case reports, conference summaries, animal experiments, and non-English literature. Two researchers independently screened the titles and abstracts. Disagreements were resolved by a third researcher in a blinded manner. Data extraction - Basic diagnostic accuracy parameters such as true positive, false positive, true negative, and false negative were extracted using a 2 X 2 table. Other relevant data included the following: year, country, research type, sample size, type of nanopore sequencing technology, comparator group, reference standard, sample type, and average age of patients. Research type was determined using the classification algorithm by Mathes & Pieper 14 . Data extraction was carried out independently by two researchers. Risk of bias assessment - The risk of bias in diagnostic accuracy was assessed using the QUADAS-2 tool 15 . One researcher independently evaluated all included studies. Risk of bias assessment was carried out independently by two researchers. Statistical analysis - Pooled sensitivity, specificity, NPV, PPV, and DOR were reported with 95% confident interval (CI). Heterogeneity was quantified using the inconsistency value (I2); where I2 >50% is considered as a remarkable heterogeneity, which warranted subgroup analysis or meta-regression to identify sources of heterogeneity. All pooled effect sizes were determined using a random effects model to account for both within- and between-studies variance. Receiver operating characteristic (ROC) curves were plotted to assess the overall accuracy of nanopore sequencing; there was a high diagnostic value if the AUC values exceeded 0.9. Subgroup analyses and meta-regression were not performed since there were < 10 studies included in the meta-analysis. Deek’s test for publication bias was performed if there were ≥ 10 eligible studies with computable diagnostic odds ratios 16 . Statistical significance was set at p <0.05. Analyses were performed using the “ mada ” and “ meta ” packages of R statistical software vr. 4.3.3 and IBM SPSS vr. 26 17 . RESULTS Characteristics of included studies Comprehensive search across multiple databases yielded a total of 1,073 records about MTB detection and 226 records about MTB drug susceptibility. The full search strategies were summarized in Supplementary Tables 1 and 2 . Following a thorough screening process, 17 studies about MTB detection were included while 19 studies about MTB drug susceptibility were selected ( Fig. 1 ). Download figure Open in new tab Figure 1. PRISMA flow diagram of this study selection process. To facilitate our analysis, studies were classified into two categories: those focusing on the detection of MTB ( Table 1 ) and those focusing on the detection of DR-MTB ( Table 2 ). Among the 32 articles, 13 articles related to MTB detection only 18 - 30 ,15 articles focused on DR-MTB detection only 31 - 45 , while 4 articles examined both 46 - 49 . View this table: View inline View popup Table 1. Characteristics of included studies on detection of MTB. View this table: View inline View popup Table 2. Characteristics of included studies on detection of DR-MTB Detection of MTB Out of the 17 articles, nine were retrospective cross-sectional while eight were prospective cross-sectional. GridION was predominantly used in these studies (12 out of 17), followed by minION alone (4 out of 19), then the combination of both sequencing platforms (1 out of 19). No study used Flongle or PromethION. Included studies were published between 2017 and 2024 ( Table 1 ). Risk of bias and applicability assessment for detection of MTB Separate assessments of the risk of bias and applicability concerns using the QUADAS-2 tool were performed for studies on MTB detection ( Figure 2 ) and drug susceptibility ( Figure 4 ). Mainly, patient selection is the top source of bias and applicability concerns. In the 17 MTB detection studies selected, only one explicitly described the sampling strategy, consisting of consecutive enrollment 47 ( Table 1 ). Further, nine of the selected studies were focused on general infections, lower respiratory tract infections, and pneumonia, and thus, their MTB results derived from secondary analyses; hence, the heightened applicability concerns. Additionally, among the 17 MTB detection studies, nine adopted a retrospective cross-sectional design ( Figure 2 ), increasing the risk of bias concerning the evaluated index test, reference test, and the flow of timing in these studies. Download figure Open in new tab Figure 2. Heat map showing risk of bias and applicability concerns among the selected 17 studies focusing on the detection of MTB. Meta-analysis of MTB detection studies Heterogeneity test. Our results showed that the logit-transformed sensitivity did not correlate with logit transformed false positive rate (1-specificity) (Spearman correlation -0.486 ( p = 0.356)). Our analysis revealed no threshold effect among the included studies. Higgin’s I2 of the diagnostic measures ranged from 63% -88%. Evaluation of diagnostic accuracy. The meta-analysis for detecting MTB included seven studies, with 1,709 specimens from 1,741 adult participants. Our results indicate that the diagnostic sensitivity of nanopore sequencing in detecting MTB ranged from 75% to 95%, while its specificity varied between 81% and 100%. Nanopore sequencing had a pooled sensitivity of 88.61% (95% CI 83.81–92.12%, I2 = 63%), and a pooled specificity of 93.18% (95% CI: 85.32– 96.98%, I2 = 77%) ( Figure 3 ). Its PPV ranged from 81% to 100%, while its NPV varied between 67% and 99%. The pooled PPV value was 94.71% (95% CI 89.99–97.27%, I2 = 65%), while its pooled NPV was computed to be 84.33% (95% CI: 72.02–91.84%, I2 = 88%) ( Figure 3 ). Our results also indicated that AUC was excellent [AUC = 0.932 (SE = 0.03)], with a remarkable wide DOR range, where values varied from 13 to 1,311. Pooled DOR was determined to be 107.23 (95% CI 35.15–327.15), with moderate heterogeneity (I2 = 65%) ( Figure 3 ). The summary receiver operating characteristic (SROC) curve for nanopore sequencing in MTB detection is shown in Figure 3 . Youden’s J index was calculated to be 0.82 ( Figure 3 ). Download figure Open in new tab Figure 3. Diagnostic accuracy measures of nanopore sequencing in MTB detection. Publication bias . Deek’s test was not performed since there were only 7 studies with calculated DORs at most (n<10). Detection of DR-MTB Among 19 studies selected, 11 studies adopted a prospective cross-sectional design, while 7 used a retrospective cross-sectional approach ( Table 2 ). MinION alone was predominantly used in these studies (14 out of 19), followed by GridION alone (3 out of 19), then the combination of both sequencing platforms (2 out of 19). These studies span publication years from 2017 to 2024. No study used Flongle or PromethION. Risk of bias and applicability assessment for DR-MTB Among the 16 DR-MTB studies, two studies followed STARD recommendations 50 ; one study stated the use of consecutive sampling 47 and one study mentioned use of random sampling 41 . Further lack of clinical information about the samples contributed to the applicability concerns in drug resistance studies. Additionally, 6 out of the 16 DR-MTB studies employed a case-control design, amplifying the risk of bias related to the index test, reference test, and the sequencing of events in these investigations ( Figure 4 ). Download figure Open in new tab Figure 4. Heat maps showing risk of bias and applicability concerns among studies focusing on the detection of DR-MTB. Meta-analysis of studies on DR-MTB Heterogeneity test. Our results showed that the logit-transformed sensitivity did not correlate with the logit-transformed false positive rate (1-specificity) across all drug resistance groups [Spearman coefficient range: -0.5 to 0.5 ( p-value range = 0. 5 to 1)]. Higgin’s I2 of the diagnostic measures ranged from 0% to 87%. Evaluation of diagnostic accuracy. The meta-analysis on detecting DR-MTB comprised 917 adult participants from five studies. Our results indicated that pooled sensitivity of nanopore sequencing was lower than its specificity in detecting any drug resistance; pooled sensitivity ranged from 77.99% to 93.66%, while the pooled specificity varied between 94.84% and 99.47% ( Table 3 ). The pooled PPV ranged from 87.57% to 100% while the pooled NPV varied between 91.27% and 99.46% ( Table 3 ). View this table: View inline View popup Download powerpoint Table 3. Summary of pooled diagnostic indices for nanopore sequencing in the detection of DR-MTB. Our results indicated that among first-line DST, accurate detection of pyrazinamide-resistance was most challenging in terms of sensitivity and PPV. Conversely, detection of rifampicin resistance was easiest for nanopore sequencing among first-line DST in terms of sensitivity and PPV. Pooled specificity of nanopore sequencing was highest in detecting ethambutol-resistance and lowest in isoniazid resistance. Pooled NPV, on the other hand, was highest in detecting pyrazinamide-resistance and lowest in rifampicin-resistance. Meanwhile, among second-line DST, the pooled sensitivity and NPV of nanopore sequencing were the highest but PPV was the lowest in detecting kanamycin-resistance. The highest specificity was noted in detecting ofloxacin resistance. Detection of streptomycin-resistance appeared to be more challenging; pooled specificity and NPV was at its lowest for MTB resistant to this aminoglycoside. Interestingly, pooled sensitivity was lowest, but pooled PPV was the highest for moxifloxacin-resistance. Resistance to ethionamide 39 , ciprofloxacin 36 , capreomycin 36 , and bedaquiline 43 were each investigated by only one study; diagnostic accuracy measures are shown in Table 4 . However, only one bedaquiline-resistant sample was detected in the sole study it was mentioned, making calculation of diagnostic accuracy measures impossible. Furthermore, no study mentioned about pretomanid or linezolid resistance. Among these three drugs, nanopore sequencing had the lowest diagnostic accuracy measures in detecting ethionamide resistance. View this table: View inline View popup Download powerpoint Table 4. Summary of diagnostic indices of nanopore sequencing in detecting DR-MTB resistant to drugs mentioned in only one study 36 , 39 , 43 The AUC indicated clinically acceptable performance across all DST studies, with values ranging from 0.85 to 0.990 ( Table 5 ), observed for both first-line and second-line DST ( Figure 5 ) 51 . AUC was excellent in most of DST investigated; it was lower than 0.9 only in detection of ethambutol and streptomycin resistance. The range of pooled DOR was remarkably wide; values varied from 106.94 to 8943.00 ( Table 5 ). DOR was the lowest in detecting isoniazid and streptomycin resistance, and the highest in detecting pyrazinamide and moxifloxacin resistance. Download figure Open in new tab Figure 5. Summary ROC curve of nanopore sequencing in detection of DR-MTB resistant to first-line drugs. INH – Isoniazid; RIF – Rifampicin; PZA – Pyrazinamide; EMB – Ethambutol; and second-line drugs. STR – Streptomycin; AMI – Amikacin; KAN – Kanamycin; OFX – Ofloxacin; MOX – Moxifloxacin; CAP – Capreomycin. View this table: View inline View popup Download powerpoint Table 5. Diagnostic accuracy of nanopore sequencing in the detection of DR-MTB. Publication bias . Deek’s test was not performed since there were only 4 studies with calculated DOR at most (n<10). DISCUSSION Most of the studies aimed at evaluating only MTB detection by nanopore sequencing used GridION. In fact, GridION was used in all the studies included in this meta-analysis. Our study shows that GridION has high sensitivity [88.61%; 95% CI (83.81–92.12%)] and specificity [93.18%; 95% CI (85.32–96.98%)] in detecting MTB, indicating that this technology is accurate enough when compared to reference genotypic or phenotypic methods 52 . Its high PPV [94.71%; 95% CI (89.99–97.27%)] and moderately high NPV [84.31%; 95% CI (72.02–91.84%)] suggests its usefulness in clinical practice 52 GridION also has an excellent discriminatory performance, as reflected by its large DOR [107.23; 95% CI (35.15–327.15)], which far exceeded the minimum standard (DOR = 10) for a very good test, its AUC (0.932) which exceeded 0.9, and its Youden index (J = 0.82), which exceeded 0.50 53 - 55 . Most of the studies investigating DST by nanopore sequencing, including those considered in our meta-analysis, used minION. Estimates for pooled sensitivity were imprecise; a study using DNA extraction from sputum, instead of the usual DNA extracts from culture, recorded low sensitivities ranging from 0 to 33% 37 . Among first-line DST, nanopore sequencing performed best in detecting pyrazinamide and rifampicin resistance. Pyrazinamide had the highest DOR (806.33) and AUC (0.98) despite having the lowest sensitivity (77.99) and PPV (89.40). Rifampicin also had the highest AUC, tied with pyrazinamide, due to its sensitivity (93.43) and PPV (96.43) which were highest among first-line drugs. Isoniazid resistance has been a challenge, with the lowest DOR (265.30) and second to the lowest AUC (0.91), primarily due to its specificity (95.82), which was lowest. Similarly, ethambutol had the lowest AUC (0.85) despite having the highest specificity (98.22). On the other hand, among second-line DST, nanopore sequencing excelled in detecting resistance to most second-line drugs, especially moxifloxacin. Moxifloxacin resistance had the highest DOR (8943.00), AUC (0.99) and PPV (100) despite having the lowest sensitivity (77.99). Notably, AUC was at its highest value (0.99) for all second-line drugs, except streptomycin. Streptomycin resistance detection had the lowest performance in DOR (106.94) and AUC (0.85) mainly due to its specificity (94.84) and NPV (91.27), which were also lowest. This may be attributed to still unknown resistance mechanisms, a finding consistent with previously reported conflicts with DST 39 . Furthermore, streptomycin resistance involves several genes (e.g., rpsL, rrs); the presence of multiple potential sites for mutations makes comprehensive detection more challenging. While streptomycin is a valuable option for the treatment of DR-TB, its usage is in decline due to the expanded acquisition and maintenance of resistant MTB isolates 56 . Mathematically, DOR is directly related to AUC; the higher the DOR, the higher the AUC 53 . However, it has been shown that AUC is maximized when the study diagnostic odds ratios are homogeneous 57 . This explains why even though among first-line drugs, the DOR of ethambutol resistance detection is second to the highest [DOR 744.07; 95% CI (21.46; 25799.73)], it has the lowest AUC (0.85). Furthermore, the DOR of isoniazid is lowest [DOR 265.30; 95% CI (17.80; 3953.16)] but it has the second highest AUC (0.91). Indeed, the precision and not only the point estimate of DOR affects AUC. It is also notable that the heterogeneity of most diagnostic measures is remarkable (I2 > 50%); however, this is common with meta-analyses of diagnostic accuracy research 58 . One of the primary weaknesses of nanopore sequencing technology, specifically MinION, in its early use, is its high error rate (20-35%) 59 . However, the change of assay chemistry from R7 to R9 by using an Escherichia coli pore protein CsgG, the shift from HMM-based base-caller to neural network-based base-caller, the refinement of statistical techniques to reduce structural noise, and the use of post-sequencing correction tools greatly improved the performance of nanopore sequencing technology, which is partly evidenced by the results of our study 60 , 61 . This study shows that nanopore sequencing technology is accurate and useful in the detection of both DS-MTB and DR-MTB strains in clinical specimens. This study has several limitations, all stemming from the small number of studies included in the actual meta-analysis. The effects of variations in reference standard and age of participants, among other pertinent variables, on the diagnostic accuracy measures could be accounted for by subgroup analysis or meta-regression. These tests were not done since the number of studies is less than 10 62 . Publication bias was also not investigated due to the same reason 62 . Additional studies will also be needed to provide more reliable estimates of the diagnostic accuracy of nanopore sequencing. The lower 95% Cl estimates of DOR in detecting resistance to pyrazinamide (5.67), streptomycin (7.90), and ofloxacin (9.10) were found to be <10, which is the minimum DOR for a very good test. Even though this imprecision may cast uncertainty in the performance of nanopore sequencing in detecting MTB isolates resistant for these three drugs, the use of nanopore sequencing for MTB DST is only emerging. More studies will also provide more reliable estimates of the diagnostic accuracy of nanopore sequencing in detecting resistance to ethionamide, ciprofloxacin, and capreomycin. In this context, it is notable that nanopore sequencing, just like other sequencing platforms, fares relatively poorly in detecting ethionamide resistance, which could be explained by the presence of unknown resistance mechanisms for ethionamide, which is also one of the primary reasons for overall conflicts with DST 39 . Furthermore, diagnostic accuracy studies on bedaquiline, pretomanid, and linezolid should be of top priority since statistical studies about these drugs are absent. CONCLUSION Nanopore sequencing GridION has high sensitivity and specificity, high PPV, moderately high NPV, and excellent DOR and AUC in detecting MTB. MinION excels in detecting pyrazinamide and rifampicin resistance and underperforms in detecting isoniazid and ethambutol resistance when compared to current phenotypic DST methods. However, additional studies are required to provide more precise estimates for MinION’s sensitivity in detecting drug-resistance and DOR in detecting resistance to pyrazinamide, streptomycin, and ofloxacin. Furthermore, studies on detecting resistance to bedaquiline, pretomanid, and linezolid are lacking. Author roles All authors meet the journal’s criteria for authorship. Individual contributions/author roles are listed below. Authors Contribution/s Timothy Hudson David Culasino Carandang: conceptualization, visualization, methodology, statistical analysis, validation, data curation, writing-original draft, writing-review and editing Dianne Jaula Cunanan: visualization, methodology, validation, data curation, writing-original draft, writing-review and editing Gail S. Co: methodology, validation, data curation, writing-original draft, writing-review and editing John David Pilapil: visualization, methodology, validation, data curation, writing-review and editing Juan Ignacio Garcia: writing-review and editing Blanca I. Restrepo: writing-review and editing Marcel Yotebieng: writing-review and editing Jordi B. Torrelles: writing-review and editing Kin Israel Notarte: conceptualization, visualization, methodology, validation, data curation, writing-review and editing Conflict of Interest None of the authors has a conflict of interest. Data Availability All data produced in the present work are contained in the manuscript SUPPLEMENTARY MATERIALS View this table: View inline View popup Supplementary Table 1. Full Search Strategy for MTB Detection Studies View this table: View inline View popup Supplementary Table 2. Full Search Strategy for MTB Drug Susceptibility Studies REFERENCES 1. ↵ World Health Organization . Global Tuberculosis Report 2021 . Geneva: World Health Organization ; 2021 . Retrieved 21 February 2024 from https://www.who.int/publications/i/item/9789240037021 2. ↵ World Health Organization (WHO) . ( 2024 , February 20). Tuberculosis . Retrieved 23 February 2024 from https://www.who.int/health-topics/tuberculosis#tab=tab_1 3. ↵ Singh , S. , Dey , B. , Sachdeva , K. S. , Kabra , S. K. , Chopra , K. K. , Chaudhary , V. K. , Sharma , P. , & Katoch , V. M . ( 2015 ). Challenges in Tuberculosis Diagnosis and Management: Recommendations of the Expert Panel . National Journal of Mycology , 6 ( 1-2 ), 1 – 11 . Retrieved 22 February 2024 from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4411802/ OpenUrl 4. ↵ Yang , J. , Ye , W. , Zhang , C. , Lin , W. , Mei , L. , Liu , S. , & Liu , J . ( 2023 ). Accuracy of Nanopore Sequencing as a Diagnostic Assay for Pulmonary Tuberculosis versus Smear , Culture and Xpert 5. ↵ Yang , J. , Ye , W. , Zhang , C. , Lin , W. , Mei , L. , Liu , S. , & Liu , J . ( 2023 ). Accuracy of Nanopore Sequencing as a Diagnostic Assay for Pulmonary Tuberculosis versus Smear, Culture and Xpert /Li, Z. (2022). The Value of GeneXpert MTB/RIF for Detection in Tuberculosis: A Bibliometrics-Based Analysis and Review . Journal of Analytical Methods in Chemistry, 2022, 2915018 . doi: 10.1155/2022/2915018 OpenUrl CrossRef 6. ↵ World Health Organization. ( 2013 ). Automated Real-Time Nucleic Acid Amplification Technology for Rapid and Simultaneous Detection of Tuberculosis and Rifampicin Resistance (Xpert MTB/RIF Assay for the Diagnosis of Pulmonary and Extrapulmonary TB in Adults and Children): Policy Update . Retrieved 22 February 2024 from https://iris.who.int/bitstream/handle/10665/112472/9789241506335_eng.pdf?sequence=1 7. ↵ Médecins Sans Frontières ( 2024 ). Tuberculosis: Appendix 1. Xpert assays. MSF medical guidelines . Retrieved 24 May 2024 at https://medicalguidelines.msf.org/en/viewport/TUB/english/appendix-3-xpert-mtb-rif-20323734.html ; 8. ↵ Bisimwa , B. C. , Nachega , J. B. , Warren , R. M. , Theron , G. , Metcalfe , J. Z. , Shah , M. , Diacon , A. H. , Sam-Agudu , N. A. , Yotebieng , M. , Bulabula , A. N. H. , Katoto , P. D. M. C. , Chirambiza , J. P. , Nyota , R. , Birembano , F. M. , Musafiri , E. M. , Byadunia , S. , Bahizire , E. , Kaswa , M. K. , Callens , S. , & Kashongwe , Z. M . ( 2021 ). Xpert Mycobacterium tuberculosis/Rifampicin-Detected Rifampicin Resistance is a Suboptimal Surrogate for Multidrug-resistant Tuberculosis in Eastern Democratic Republic of the Congo: Diagnostic and Clinical Implications . Clinical infectious diseases: an official publication of the Infectious Diseases Society of America , 73 ( 2 ), e362 – e370 . doi: 10.1093/cid/ciaa873 OpenUrl CrossRef 9. ↵ World Health Organization. ( 2013 ). Automated Real-Time Nucleic Acid Amplification Technology for Rapid and Simultaneous Detection of Tuberculosis and Rifampicin Resistance (Xpert MTB/RIF Assay for the Diagnosis of Pulmonary and Extrapulmonary TB in Adults and Children): Policy Update . Retrieved 22 February 2024 from https://iris.who.int/bitstream/handle/10665/112472/9789241506335_eng.pdf?sequence=1 10. ↵ World Health Organization. ( 2023 ). Use of targeted next-generation sequencing to detect drug-resistant tuberculosis: Rapid communication . Retrieved 23 February 2024 from https://iris.who.int/bitstream/handle/10665/371687/9789240076372-eng.pdf?sequence=1 11. ↵ Wang , Y. , Zhao , Y. , Bollas , A. , Wang , Y. , & Au , K. F . ( 2021 ). Nanopore sequencing technology, bioinformatics and applications . Nature biotechnology , 39 ( 11 ), 1348 – 1365 . doi: 10.1038/s41587-021-01108-x OpenUrl CrossRef PubMed 12. ↵ Page , M. J. , McKenzie , J. E. , Bossuyt , P. M. , Boutron , I. , Hoffmann , T. C. , Mulrow , C. D. , Shamseer , L. , Tetzlaff , J. M. , Akl , E. A. , Brennan , S. E. , Chou , R. , Glanville , J. , Grimshaw , J. M. , Hróbjartsson , A. , Lalu , M. M. , Li , T. , Loder , E. W. , Mayo-Wilson , E. , McDonald , S. , McGuinness , L. A. , … Moher , D . ( 2021 ). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews . BMJ (Clinical research ed .), 372 , n71 . doi: 10.1136/bmj.n71 OpenUrl FREE Full Text 13. ↵ Jain , M. , Olsen , H. E. , Paten , B. & Akeson , M . ( 2016 ). The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community . Genome Biol . 17 , 239 OpenUrl CrossRef PubMed 14. ↵ Mathes , T. , & Pieper , D . ( 2019 ). An algorithm for the classification of study designs to assess diagnostic, prognostic and predictive test accuracy in systematic reviews . Systematic reviews , 8 ( 1 ), 226 . doi: 10.1186/s13643-019-1131-4 ). OpenUrl CrossRef 15. ↵ Whiting , P. F. , Rutjes , A. W. , Westwood , M. E. , Mallett , S. , Deeks , J. J. , Reitsma , J. B. , Leeflang , M. M. , Sterne , J. A. , Bossuyt , P. M. , & QUADAS-2 Group ( 2011 ). QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of internal medicine , 155 ( 8 ), 529–536 . doi: 10.7326/0003-4819-155-8-201110180-00009 OpenUrl CrossRef PubMed 16. ↵ Deeks , J. J. , Macaskill , P. , & Irwig , L . ( 2005 ). The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed . Journal of clinical epidemiology , 58 ( 9 ), 882 – 893 . doi: 10.1016/j.jclinepi.2005.01.016 . OpenUrl CrossRef PubMed Web of Science 17. ↵ Shim SR , Kim SJ , Lee J . ( 2019 ). Diagnostic test accuracy: application and practice using R software . Epidemiol Health . 41 : e2019007 . doi: 10.4178/epih.e2019007 . Epub 2019 Mar 28. PMID: 30999739 ; PMCID: PMC6545496 . OpenUrl CrossRef PubMed 18. ↵ Deng , Q. , Cao , Y. , Wan , X. , Wang , B. , Sun , A. , Wang , H. , Wang , Y. , Wang , H. , & Gu , H . ( 2022 ). Nanopore-based metagenomic sequencing for the rapid and precise detection of pathogens among immunocompromised cancer patients with suspected infections . Frontiers in Cellular and Infection Microbiology , 12 . doi: 10.3389/fcimb.2022.943859 OpenUrl CrossRef 19. Fu , Y. , Chen , Q. , Xiong , M. , Zhao , J. , Shen , S. , Chen , L. , Pan , Y. , Li , Z. , & Li , Y . ( 2022 ). Clinical Performance of Nanopore Targeted Sequencing for Diagnosing Infectious Diseases . Microbiology Spectrum , 10 ( 2 ). doi: 10.1128/spectrum.00270-22 OpenUrl CrossRef 20. Guo , Y. , Li , H. , Chen , H. , Li , Z. , Ding , W. , Wang , J. , Yin , Y. , Jin , L. , Sun , S. , Jing , C. , & Wang , H . ( 2021 ). Metagenomic next-generation sequencing to identify pathogens and cancer in lung biopsy tissue . EBioMedicine , 73 , 103639 . doi: 10.1016/j.ebiom.2021.103639 OpenUrl CrossRef 21. Guo , Y. , Li , Z. , Li , L. , Li , S. , Sun , L. , Yang , X. , Dai , Y. , Gu , J. , Yang , L. , Liu , X. , Lu , B. , Han , J. , Chang , K. , Gu , L. , Yin , Y. , Sun , S. , Jing , C. , Chen , H. , Liu , M. , & Xu , H . ( 2023 ). A dual-process of targeted and unbiased Nanopore sequencing enables accurate and rapid diagnosis of lower respiratory infections . EBioMedicine , 98 , 104858 – 104858 . doi: 10.1016/j.ebiom.2023.104858 OpenUrl CrossRef 22. Lin , Q. , Yao , Y. , Li , X. , Zhang , S. , Guo , H. , Ma , X. , Chen , W. , Ru , C. , Wang , L. , Wang , B. , Ma , Q. , Zhu , J. , Lin , X. , Chen , Q. , Lou , H. , Chen , Q. , Chen , J. , Zeng , Z. , Zhou , J. , & Chen , Y . ( 2023 ). The application of nanopore targeted sequencing for pathogen diagnosis in bronchoalveolar lavage fluid of patients with pneumonia: a prospective multicenter study . Infectious Diseases , 56 ( 2 ), 128 – 137 . doi: 10.1080/23744235.2023.2276785 OpenUrl CrossRef 23. Liu , Z. , Yang , Y. , Wang , Q. , Wang , L. , Nie , W. , & Chu , N . ( 2023 ). Diagnostic value of a nanopore sequencing assay of bronchoalveolar lavage fluid in pulmonary tuberculosis . BMC Pulmonary Medicine , 23 ( 1 ). doi: 10.1186/s12890-023-02337-3 OpenUrl CrossRef 24. Luo , W. , He , Y. , Xu , J. , Zhang , S. , Li , C. , Jiangfeng Lv , Shen , Y. , Ou , Z. , & Dong , H. ( 2023 ). Comparison of Third-Generation Sequencing Technology and Traditional Microbiological Detection in Pathogen Diagnosis of Lower Respiratory Tract Infection . Discovery Medicine , 35 ( 176 ), 332 – 332 . doi: 10.24976/discov.med.202335176.34 OpenUrl CrossRef 25. Wang , M. , Fu , A. , Hu , B. , Shen , G. , Liu , R. , Zhao , W. , Jiang , S. , Cai , X. , Li , C. , Li , J. , Wu , Q. , Feng , K. , Gu , J. , Chen , J. , Shu , M. , Zhang , B. , Deng , Z. , Yu , L. , Li , Y. , & Liu , T . ( 2020 ). Same-Day Simultaneous Diagnosis of Bacterial and Fungal Infections in Clinical Practice by Nanopore Targeted Sequencing . MedRxiv (Cold Spring Harbor Laboratory ). doi: 10.1101/2020.04.08.20057604 OpenUrl Abstract / FREE Full Text 26. Yang , J. , Ye , W. , Zhang , C. , Lin , W. , Mei , L. , Liu , S. , & Liu , J . ( 2023 ). Accuracy of Nanopore Sequencing as a Diagnostic Assay for Pulmonary Tuberculosis versus Smear , Culture and Xpert MTB/RIF: A Head-to-Head Comparison. Tropical Medicine and Infectious Disease , 8 ( 9 ), 441 . doi: 10.3390/tropicalmed8090441 OpenUrl CrossRef 27. Yu , G. , Shen , Y. , Zhong , F. , Zhou , L. , Chen , G. , Fang , L. , Zhu , P. , Sun , L. , Zhao , W. , Yu , W. , & Ye , B . ( 2022 ). Diagnostic accuracy of nanopore sequencing using respiratory specimens in the diagnosis of pulmonary tuberculosis . International Journal of Infectious Diseases , 122 , 237 – 243 . doi: 10.1016/j.ijid.2022.06.001 OpenUrl CrossRef 28. Yu , G. , Shen , Y. , Yao , L. , & Xu , X . ( 2024 ). Evaluation of Nanopore Sequencing for Diagnosing Pulmonary Tuberculosis Using Negative Smear Clinical Specimens. Infection and Drug Resistance , Volume 17 , 673 – 682 . doi: 10.2147/idr.s442229 OpenUrl CrossRef 29. Zhang , J. , Gao , L. , Zhu , C. , Jin , J. , Song , C. , Dong , H. , Li , Z. , Wang , Z. , Chen , Y. , Yang , Z. , Tan , Y. , & Wang , L . ( 2022 ). Clinical value of metagenomic next-generation sequencing by Illumina and Nanopore for the detection of pathogens in bronchoalveolar lavage fluid in suspected community-acquired pneumonia patients . Frontiers in Cellular and Infection Microbiology , 12 . doi: 10.3389/fcimb.2022.1021320 OpenUrl CrossRef 30. ↵ Zhou , L. , Zou , X. , Hu , Q. , Hua , H. , & Qi , Q . ( 2024 ). Determination of the diagnostic accuracy of nanopore sequencing using bronchoalveolar lavage fluid samples from patients with sputum-scarce pulmonary tuberculosis . Journal of Infection and Chemotherapy , 30 ( 2 ), 98 – 103 . doi: 10.1016/j.jiac.2023.09.014 OpenUrl CrossRef 31. ↵ Cabibbe , A. M. , Spitaleri , A. , Battaglia , S. , Colman , R. E. , Suresh , A. , Uplekar , S. , Rodwell , T. C. , & Cirillo , D. M . ( 2020 ). Application of Targeted Next-Generation Sequencing Assay on a Portable Sequencing Platform for Culture-Free Detection of Drug-Resistant Tuberculosis from Clinical Samples . Journal of Clinical Microbiology , 58 ( 10 ). doi: 10.1128/jcm.00632-20 OpenUrl CrossRef 32. Cervantes , J. , Yokobori , N. , & Hong , B. Y . ( 2020 ). Genetic Identification and Drug-Resistance Characterization of Mycobacterium tuberculosis Using a Portable Sequencing Device. A Pilot Study. Antibiotics (Basel , Switzerland ), 9 ( 9 ), 548 . doi: 10.3390/antibiotics9090548 OpenUrl CrossRef 33. Chan , W. S. , Au , C. H. , Chung , Y. , Leung , H. C. M. , Ho , D. N. , Wong , E. Y. L. , Lam , T. W. , Chan , T. L. , Ma , E. S. K. , & Tang , B. S. F . ( 2020 ). Rapid and economical drug resistance profiling with Nanopore MinION for clinical specimens with low bacillary burden of Mycobacterium tuberculosis . BMC Research Notes , 13 ( 1 ). doi: 10.1186/s13104-020-05287-9 OpenUrl CrossRef 34. Gliddon , H. D. , Venturini , C. , Vanisha Munsamy , Heaney , J. , Pataillot-Meakin , T. , Nastouli , E. , Pym , A. S. , Steyn , C. , Pillay , D. , & McKendry , R. A . ( 2021 ). A Rapid Drug Resistance Genotyping Workflow for Mycobacterium tuberculosis, Using Targeted Isothermal Amplification and Nanopore Sequencing . Microbiology Spectrum , 9 ( 3 ). doi: 10.1128/spectrum.00610-21 OpenUrl CrossRef 35. Gómez-González , P. J. , Campino , S. , Phelan , J. E. , & Clark , T. G . ( 2022 ). Portable sequencing of Mycobacterium tuberculosis for clinical and epidemiological applications . Briefings in Bioinformatics , 23 ( 5 ). doi: 10.1093/bib/bbac256 OpenUrl CrossRef 36. ↵ Hall , M. B. , Marie Sylvianne Rabodoarivelo , Koch , A. , Anzaan Dippenaar , George , S. , Grobbelaar , M. , Warren , R. M. , Walker , T. M. , Cox , H. , Sébastien Gagneux , Crook , D. W. , Peto , T. , Niaina Rakotosamimanana , Lapierre , S. , & Iqbal , Z. ( 2023 ). Evaluation of Nanopore sequencing for Mycobacterium tuberculosis drug susceptibility testing and outbreak investigation: a genomic analysis . The Lancet Microbe , 4 ( 2 ), e84 – e92 . doi: 10.1016/s2666-5247(22)00301-9 OpenUrl CrossRef 37. ↵ Mariner-Llicer , C. , Goig , G. A. , Zaragoza-Infante , L. , Torres-Puente , M. , Villamayor , L. M. , Navarro , D. , Borrás , R. , Álvaro Chiner-Oms , & Comas , I . ( 2021 ). Accuracy of an amplicon-sequencing nanopore approach to identify variants in tuberculosis drug-resistance-associated genes . Microbial Genomics , 7 ( 12 ). doi: 10.1099/mgen.0.000740 OpenUrl CrossRef 38. Murphy , S. G. , Smith , C. , Lapierre , P. , Shea , J. , Patel , K. , Halse , T. A. , Dickinson , M. , Escuyer , V. , Rowlinson , M. , & Musser , K. A . ( 2023 ). Direct detection of drug-resistant Mycobacterium tuberculosis using targeted next generation sequencing . Frontiers in Public Health , 11 . doi: 10.3389/fpubh.2023.1206056 OpenUrl CrossRef 39. ↵ Smith , C. , Halse , T. A. , Shea , J. , Modestil , H. , Fowler , R. C. , Musser , K. A. , Escuyer , V. , & Lapierre , P . ( 2020 ). Assessing Nanopore Sequencing for Clinical Diagnostics: a Comparison of Next-Generation Sequencing (NGS) Methods for Mycobacterium tuberculosis . Journal of Clinical Microbiology , 59 ( 1 ). doi: 10.1128/jcm.00583-20 OpenUrl CrossRef 40. Tafess , K. , Ng , T. T. L. , Lao , H. Y. , Leung , K. S. S. , Tam , K. K. G. , Rajwani , R. , Tam , S. T. Y. , Ho , L. P. K. , Chu , C. M. K. , Gonzalez , D. , Sayada , C. , Ma , O. C. K. , Nega , B. H. , Ameni , G. , Yam , W. C. , & Siu , G. K. H . ( 2020 ). Targeted-Sequencing Workflows for Comprehensive Drug Resistance Profiling of Mycobacterium tuberculosis Cultures Using Two Commercial Sequencing Platforms: Comparison of Analytical and Diagnostic Performance, Turnaround Time, and Cost . Clinical Chemistry , 66 ( 6 ), 809 – 820 . doi: 10.1093/clinchem/hvaa092 OpenUrl CrossRef 41. ↵ Zhao , K. , Tu , C. , Chen , W. , Liang , H. , Zhang , W. , Wang , Y. , Jin , Y. , Hu , J. , Sun , Y. , Xu , J. , & Yu , Y . ( 2022 ). Rapid Identification of Drug-Resistant Tuberculosis Genes Using Direct PCR Amplification and Oxford Nanopore Technology Sequencing . Canadian Journal of Infectious Diseases and Medical Microbiology , 2022, 1–8 . doi: 10.1155/2022/7588033 OpenUrl CrossRef 42. Zhao , M. , Zhang , Y. , Chen , L. , Yan , X. , Xu , T. , Fu , M. , Han , Y. , Zhang , Y. , Zhang , B. , Cao , J. , Lin , J. , Shen , D. , Li , S. , Zhu , C. , & Zhao , W . ( 2024 ). Nanopore sequencing of infectious fluid is a promising supplement for gold-standard culture in real-world clinical scenario . Frontiers in Cellular and Infection Microbiology , 14 . doi: 10.3389/fcimb.2024.1330788 OpenUrl CrossRef 43. ↵ Liu , A. , Liu , S. , Lv , K. , Zhu , Q. , Wen , J. , Li , J. , Liang , C. , Huang , X. , Gong , C. , Sun , Q. , & Gu , H . ( 2024 ). Rapid detection of multidrug resistance in tuberculosis using nanopore-based targeted next-generation sequencing: a multicenter, double-blind study . Frontiers in microbiology , 15 , 1349715 . doi: 10.3389/fmicb.2024.1349715 OpenUrl CrossRef 44. Yu , M. C. , Hung , C. S. , Huang , C. K. , Wang , C. H. , Liang , Y. C. , & Lin , J. C . ( 2021 ). Integrative utility of long read sequencing-based whole genome analysis and phenotypic assay on differentiating isoniazid-resistant signature of Mycobacterium tuberculosis . Journal of biomedical science , 28 ( 1 ), 86 . doi: 10.1186/s12929-021-00783-x OpenUrl CrossRef 45. ↵ Yu , M. C. , Hung , C. S. , Huang , C. K. , Wang , C. H. , Liang , Y. C. , & Lin , J. C . ( 2022 ). Differential Impact of the rpoB Mutant on Rifampin and Rifabutin Resistance Signatures of Mycobacterium tuberculosis Is Revealed Using a Whole-Genome Sequencing Assay . Microbiology spectrum , 10 ( 4 ), e0075422 . doi: 10.1128/spectrum.00754-22 OpenUrl CrossRef 46. ↵ Nilgiriwala , K. , Rabodoarivelo , M.-S. , Hall , M. B. , Patel , G. , Mandal , A. , Mishra , S. , Andrianomanana , F. R. , Dingle , K. , Rodger , G. , George , S. , Crook , D. W. , Hoosdally , S. , Mistry , N. , Rakotosamimanana , N. , Iqbal , Z. , Grandjean Lapierre , S. , & Walker , T. M . ( 2023 ). Genomic Sequencing from Sputum for Tuberculosis Disease Diagnosis, Lineage Determination, and Drug Susceptibility Prediction . Journal of Clinical Microbiology , 61 ( 3 ), e0157822 . doi: 10.1128/jcm.01578-22 OpenUrl CrossRef 47. ↵ Sun , X. , Song , J. , Leng , X. , Li , F. , Wang , H. , He , J. , Zhai , W. , Wang , Z. , Wu , Q. , Li , Z. , & Ruan , X . ( 2023 ). A preliminary evaluation of targeted nanopore sequencing technology for the detection of Mycobacterium tuberculosis in bronchoalveolar lavage fluid specimens . Frontiers in Cellular and Infection Microbiology , 13 . doi: 10.3389/fcimb.2023.1107990 OpenUrl CrossRef 48. Votintseva , A. A. , Bradley , P. , Pankhurst , L. , Del Ojo Elias , C. , Loose , M. , Nilgiriwala , K. , Chatterjee , A. , Smith , E. G. , Sanderson , N. , Walker , T. M. , Morgan , M. R. , Wyllie , D. H. , Walker , A. S. , Peto , T. E. A. , Crook , D. W. , & Iqbal , Z . ( 2017 ). Same-Day Diagnostic and Surveillance Data for Tuberculosis via Whole-Genome Sequencing of Direct Respiratory Samples . Journal of Clinical Microbiology , 55 ( 5 ), 1285 – 1298 . doi: 10.1128/JCM.02483-16 OpenUrl Abstract / FREE Full Text 49. ↵ Zhang , H. , Wang , M. , Han , X. , Wang , T. , Lei , Y. , Rong , Y. , Xu , P. , Wang , Y. , & Gu , H . ( 2022 ). The application of targeted nanopore sequencing for the identification of pathogens and resistance genes in lower respiratory tract infections . Frontiers in Microbiology , 13 . doi: 10.3389/fmicb.2022.1065159 OpenUrl CrossRef 50. ↵ Cohen , J. F. , Korevaar, D. A., Altman, D. G., Bruns, D. E., Gatsonis, C. A., Hooft, L., Irwig, L., Levine, D., Reitsma, J. B., de Vet, H. C., & Bossuyt, P. M. ( 2016 ). STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration . BMJ open , 6 ( 11 ), e012799 . doi: 10.1136/bmjopen-2016-012799 OpenUrl Abstract / FREE Full Text 51. ↵ Çorbacıoğlu, Ş. K., & Aksel, G . ( 2023 ). Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value . Turkish journal of emergency medicine , 23 ( 4 ), 195 – 198 . doi: 10.4103/tjem.tjem_182_23 OpenUrl CrossRef 52. ↵ Trevethan R . ( 2017 ). Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice . Frontiers in public health , 5 , 307 . doi: 10.3389/fpubh.2017.00307 OpenUrl CrossRef PubMed 53. ↵ Glas , A. S. , Lijmer , J. G. , Prins , M. H. , Bonsel , G. J. , & Bossuyt , P. M . ( 2003 ). The diagnostic odds ratio: a single indicator of test performance . Journal of clinical epidemiology , 56 ( 11 ), 1129 – 1135 . doi: 10.1016/s0895-4356(03)00177-x OpenUrl CrossRef PubMed Web of Science 54. Blackman N. J . ( 2001 ). Systematic reviews of evaluations of diagnostic and screening tests . Odds ratio is not independent of prevalence. BMJ (Clinical research ed .), 323 ( 7322 ), 1188 . OpenUrl 55. ↵ Chen , F. , Xue , Y. , Tan , M. T. , & Chen , P . ( 2015 ). Efficient statistical tests to compare Youden index: accounting for contingency correlation . Statistics in medicine , 34 ( 9 ), 1560 – 1576 . doi: 10.1002/sim.6432 OpenUrl CrossRef PubMed 56. ↵ Rocha , et al. , 2021 ). Rocha, D. M. G. C., Viveiros, M., Saraiva, M., & Osório, N. S . (2021). The Neglected Contribution of Streptomycin to the Tuberculosis Drug Resistance Problem. Genes , 12 ( 12 ), 2003. doi: 10.3390/genes12122003 . OpenUrl CrossRef 57. ↵ Walter S. D . ( 2002 ). Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data . Statistics in medicine , 21 ( 9 ), 1237 – 1256 . doi: 10.1002/sim.1099 OpenUrl CrossRef PubMed Web of Science 58. ↵ Liu , J. , Zhang , Q. , Dong , Y. Q. , Yin , J. , & Qiu , Y. Q . ( 2022 ). Diagnostic accuracy of metagenomic next-generation sequencing in diagnosing infectious diseases: a meta-analysis . Scientific reports , 12 ( 1 ), 21032 . doi: 10.1038/s41598-022-25314-y OpenUrl CrossRef 59. ↵ World Health Organization. ( 2018 ). The use of next-generation sequencing technologies for the detection of mutations associated with drug resistance in Mycobacterium tuberculosis complex: technical guide. World Health Organization . https://iris.who.int/handle/10665/274443 . License: CC BY-NC-SA 3.0 IGO 60. ↵ Lu , H. , Giordano , F. , & Ning , Z . ( 2016 ). Oxford Nanopore MinION Sequencing and Genome Assembly . Genomics, proteomics & bioinformatics , 14 ( 5 ), 265 – 279 . doi: 10.1016/j.gpb.2016.05.004 OpenUrl CrossRef PubMed 61. ↵ Rang , F. J. , Kloosterman , W. P. , & de Ridder , J. ( 2018 ). From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy . Genome biology , 19 ( 1 ), 90 . doi: 10.1186/s13059-018-1462-9 OpenUrl CrossRef 62. ↵ Higgins JPT , Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors) . Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane , 2023 . Available from www.training.cochrane.org/handbook . View the discussion thread. Back to top Previous Next Posted June 06, 2024. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Diagnostic Accuracy of Nanopore Sequencing for Detecting Mycobacterium tuberculosis and Drug-Resistant Strains: A Systematic Review and Meta-Analysis Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Diagnostic Accuracy of Nanopore Sequencing for Detecting Mycobacterium tuberculosis and Drug-Resistant Strains: A Systematic Review and Meta-Analysis Timothy Hudson David Culasino Carandang , Dianne Jaula Cunanan , Gail S. Co , John David Pilapil , Juan Ignacio Garcia , Blanca I. Restrepo , Marcel Yotebieng , Jordi B. Torrelles , Kin Israel Notarte medRxiv 2024.06.05.24308511; doi: https://doi.org/10.1101/2024.06.05.24308511 Share This Article: Copy Citation Tools Diagnostic Accuracy of Nanopore Sequencing for Detecting Mycobacterium tuberculosis and Drug-Resistant Strains: A Systematic Review and Meta-Analysis Timothy Hudson David Culasino Carandang , Dianne Jaula Cunanan , Gail S. Co , John David Pilapil , Juan Ignacio Garcia , Blanca I. Restrepo , Marcel Yotebieng , Jordi B. Torrelles , Kin Israel Notarte medRxiv 2024.06.05.24308511; doi: https://doi.org/10.1101/2024.06.05.24308511 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Infectious Diseases (except HIV/AIDS) Subject Areas All Articles Addiction Medicine (572) Allergy and Immunology (864) Anesthesia (302) Cardiovascular Medicine (4448) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1515) Epidemiology (15240) Forensic Medicine (30) Gastroenterology (1130) Genetic and Genomic Medicine (6614) Geriatric Medicine (669) Health Economics (1000) Health Informatics (4550) Health Policy (1372) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1268) Infectious Diseases (except HIV/AIDS) (15928) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (668) Neurology (6622) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1147) Occupational and Environmental Health (957) Oncology (3343) Ophthalmology (977) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1695) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5459) Public and Global Health (9249) Radiology and Imaging (2205) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1197) Rheumatology (597) Sexual and Reproductive Health (715) Sports Medicine (530) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a028c1146f12e2c5',t:'MTc3OTkyNDA2OA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();