Objective: This review aimed to identify proper respiratory-related sample types for adult and pediatric pulmonary tuberculosis (PTB), respectively, by comparing performance of Xpert MTB/RIF when using bronchoalveolar lavage (BAL), induced sputum (IS), expectorated sputum (ES), nasopharyngeal aspirates (NPAs), and gastric aspiration (GA) as sample.

Methods: Articles were searched in Web of Science, PubMed, and Ovid from inception up to 29 June 2020. Pooled sensitivity and specificity were calculated, each with a 95% confidence interval (CI). Quality assessment and heterogeneity evaluation across included studies were performed.

Results: A total of 50 articles were included. The respective sensitivity and specificity were 87% (95% CI: 0.84–0.89), 91% (95% CI: 0.90–0.92) and 95% (95% CI: 0.93–0.97) in the adult BAL group; 90% (95% CI: 0.88–0.91), 98% (95% CI: 0.97–0.98) and 97% (95% CI: 0.95–0.99) in the adult ES group; 86% (95% CI: 0.84–0.89) and 97% (95% CI: 0.96–0.98) in the adult IS group. Xpert MTB/RIF showed the sensitivity and specificity of 14% (95% CI: 0.10–0.19) and 99% (95% CI: 0.97–1.00) in the pediatric ES group; 80% (95% CI: 0.72–0.87) and 94% (95% CI: 0.92–0.95) in the pediatric GA group; 67% (95% CI: 0.62–0.72) and 99% (95% CI: 0.98–0.99) in the pediatric IS group; and 54% (95% CI: 0.43–0.64) and 99% (95% CI: 0.97–0.99) in the pediatric NPA group. The heterogeneity across included studies was deemed acceptable.

Conclusion: Considering diagnostic accuracy, cost and sampling process, ES was a better choice than other sample types for diagnosing adult PTB, especially HIV-associated PTB. GA might be more suitable than other sample types for diagnosing pediatric PTB. The actual choice of sample types should also consider the needs of specific situations.

Globally, there were 10 million new tuberculosis (TB) cases and 1.24 million deaths in 2018 alone [1]. A body of studies have confirmed that early diagnosis and treatment can prevent most TB deaths [2–4], and thus excellent diagnostic tools need to be developed. The Xpert MTB/RIF (Cepheid, Sunnyvale, CA, United States) was endorsed as a diagnostic test for use in TB endemic countries by World Health Organization (WHO) in 2010 [5]. A systematic review of Xpert MTB/RIF studies has reported different sensitivities of Xpert MTB/RIF, ranging from 25 to 100% [6]. A study has shown the sensitivity of Xpert MTB/RIF varies with the sample types, samples quality, and bacterial load of samples [7]. Thus, choosing proper sample types is critical to improve the diagnostic performance of Xpert MTB/RIF. Given the high prevalence of pulmonary TB (PTB) [8], we paid attention to the selection of respiratory-related sample types to better diagnose PTB.

The application of urine and stool for diagnosing PTB have been reported [9,10], however, considering high TB burden areas which are usually economically underdeveloped, high cost of Xpert MTB/RIF and relatively low detection rate in non-respiratory samples [11], respiratory-related sample become the first choice for detecting Mycobacterium tuberculosis (MTB) in clinical practice, at least for now. Expectorated sputum (ES), induced sputum (IS), bronchoalveolar lavage (BAL) fluid, gastric aspiration (GA), and nasopharyngeal aspirates (NPAs) are considered suitable samples to detect PTB and are regularly collected in clinical practice. ES is readily available, however, has low bacterial load. IS, sputum induced by the inhalation of hypertonic saline, usually has large sample volume and higher quality than ES [12]. BAL has been widely accepted as the most specific sample type used for accurate and timely diagnosis of lung diseases [13,14]. The WHO supports the collection of BAL to diagnose PTB when possible [15]. However, high cost, invasive sampling and the potential risk of hemorrhage, pneumothorax, laryngospasm and other adverse reactions limit its application [16]. It is also worth noting that in clinical practice, bronchoscopy and lavage are not considered as feasible in young children due to the potential risk for anesthesia and need of specialized procedural expertise, unless otherwise specified [17]. NPA is sampled after stimulation of the cough reflex via inserting a small feeding catheter into the nasopharynx [18]. Low operational requirements, less invasive sampling, and ready access enable NPA to become an alternative to BAL for pediatric PTB, but not for adult PTB due to significantly different airway microbial composition between NPA and BAL [17]. Young children, particularly who under 5 years, are unable to expectorate sputum and always swallow sputum in their stomach by mistake, thus GA is collected by a nasogastric tube during night in three consecutive mornings to detect MTB for pediatric PTB [19,20]. Nonetheless, MTB is less likely to be detected in GA than smear in adults, and therefore, and thus GA is not regarded as an option for detecting MTB for adult PTB [21]. Enough volume and consecutive samples of GA support repeat tests to improve the rate of detection [22].

Although there are many samples types to choose from, different samples vary greatly in cost, requirement for operators and operating environment, the degree of sampling invasiveness, quality, volume, and others. It is hard to assess which is the best choice for diagnosing PTB. In addition, few studies pay attention to assess the impact of different sample types on the performance of Xpert MTB/RIF when diagnosing PTB. Therefore, we undertook this systematic review and meta-analysis to compare the detection capacity of Xpert MTB/RIF when using ES, IS, and BAL as samples for diagnosing adult PTB, as well as ES, IS, GA, and NPA for diagnosing pediatric PTB. We aimed to summarize and curate reliable evidence for choosing the proper sample types to diagnose PTB patients at different ages.

Search strategy

We conducted a full search in Web of Science, PubMed, and Ovid on 29 June 2020, with the following terms: ‘tuberculosis’, ‘mycobacterium tuberculosis’, ‘TB’, ‘MTB’ AND ‘GeneXpert’, ‘Xpert’, ‘Xpert MTB/RIF’, ‘GX’ AND ‘gastric aspirate’, ‘gastric aspiration’, ‘gastric specimen’, ‘gastric lavage aspiration’, ‘GA’, ‘GLA’, ‘GS’, ‘bronchoalveolar lavage’, ‘bronchoalveolar lavage fluid’, ‘bronchoalveolar washing’, ‘BAL’, ‘BALF’, ‘BW’, ‘induced sputum’, ‘IS’, ‘expectorated sputum’, ‘ES’, ‘nasopharyngeal aspirates’, ‘NPA’. Potential studies were manually identified from reference lists of relevant articles. Any disagreements would be discussed by two independent authors and, if necessary, a third reviewer would arbitrate.

Inclusion and exclusion criteria

Included studies needed to: (i) be cross-sectional studies, cohort studies, or randomized controlled trials; (ii) focus on PTB patients with or without other diseases; (iii) use two diagnostic methods—Xpert MTB/RIF and culture, and take culture as the reference standard; (iv) collect GA, NPA from children, or BAL from adults, or IS or ES from both; (v) give clear information that participants were adults or children. Exclusion criteria were: (i) reviews, case reports, letters, conference abstracts, and animal experiments; (ii) articles not written in English; (iii) articles with incomplete basic data.

Quality assessment

Two independent reviewers assessed the quality of included studies according to the Revised Tool for Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 [23]. Both risk of bias and applicability concerns would be evaluated as ‘high’, ‘unclear’ or ‘low’. Advice of a third reviewer would be referred if necessary.

Data extraction and management

All data were collected and entered into Microsoft Excel version 2016. The basic information mainly comprised the first author, publication year, study population’s characteristics, and so on. Diagnostic information included true-positive, false-positive, true-negative, and false-negative. If necessary, we contacted the authors for more essential information, otherwise the trials with incomplete information would be excluded.

Heterogeneity

The threshold effect and non-threshold effect could result in heterogeneity. The heterogeneity caused by the threshold effect could be explored by performing the Spearman correlation coefficient or plotting summary receiver operating characteristic curves (sROCs). The threshold effect would exist if P-value of the Spearman correlation coefficient was less than 0.05 or the points in the plots had a curvilinear (shoulder arm) pattern. If Chi-square P-values were less than 0.10, heterogeneity might be caused by a non-threshold effect and a random-effects model would be chosen.

Sensitivity analysis

Sensitivity analysis was performed to determine the robustness of any treatment effect by removing low-quality studies.

Statistical analysis

The diagnostic performance of Xpert MTB/RIF was assessed by calculating the pooled sensitivity, specificity, and area under the curve (AUC), with a 95% confidence interval (CI). The forest plots and sROC were also plotted. We performed statistical analysis with Meta-DiSc 1.4 (XI Cochrane Colloquium, Barcelona, Spain) and Review Manager V.5.3 (The Cochrane Collaboration, Software Update, Oxford, U.K.).

Studies’ characteristics

We identified 50 articles: 17 [7,24–39] in the adult BAL group, 14 [35,40–52] in the adult ES group, 7 [35,45,50,53–56] in the adult IS group, 2 [57,58] in the pediatric ES group, 6 [57,59–63] in the pediatric GA group, and 9 [59,64–71] in the pediatric IS group from 1791 studies for the meta-analysis. A total of 2864 BAL samples, 18176 ES samples, and 3876 IS samples were collected from adults. A total of 593 ES samples, 1595 GA samples, 2759 IS samples, and 780 NPA samples were collected from children. More than 80% of all samples were preprocessed, including neutralization, centrifugation, or other processing. The volume of BAL, ES, and IS were 1.0–5 ml, ≥1.0 ml, and 0.5–2.0 ml, respectively. The volume of GA varied from 0.1 ml to more than 5 ml, while 0.5–1.5 ml IS was collected from children.

The flow diagram of articles included in this meta-analysis is presented in Figure 1. The main characteristics of 43 included articles are summarized in Table 1.

The flow diagram of articles included in this meta-analysis

Figure 1
The flow diagram of articles included in this meta-analysis
Figure 1
The flow diagram of articles included in this meta-analysis
Table 1
The main characteristics of included trials
Study (year)Country or AreaPatient recruitmentSample sizeSample processingSmear- positive rate (%)HIV- positive rate (%)Culture type
Adult BAL1 group 
Barnard, 2015 South Africa NA2 112 Decontamination and centrifugation 15 NA MGIT 
Gowda, 2018 India NA 60 Unprocessed 12 MGIT 
Jo, 2016 South Korea NA 320 Centrifugation MGIT and Ogawa 
Khalil, 2015 Pakistan NA 93 NA 37 NA LJ3 
Kilaru, 2019 India Inpatients and outpatients 56 Decontamination and centrifugation 38 MGIT 
Ko, 2016 Korea NA 249 NA NA MGIT and Ogawa 
Lee, 2013 Korea NA 132 Decontamination and centrifugation MGIT and Ogawa 
Lu, 2018 China NA 238 Decontamination NA MGIT and LJ 
Mok, 2016 Singapore Inpatients and outpatients 158 NA NA MGIT 
Pan, 2018 China NA 190 Decontamination 19 NA MGIT 
Prakash, 2018 Six countries Inpatients and outpatients 383 NA NA MGIT and LJ 
Sánchez-Cabral, 2020 Brazil NA 54 NA NA 41 MGIT and LJ 
Sharma, 2015* India NA 131 Decontamination and centrifugation NA NA MGIT and LJ 
Theron, 2013 South Africa NA 154 Centrifugation 10 30 MGIT 
To, 2018 China NA 237 NA NA NA 
Ullah, 2017 Pakistan NA 98 Decontamination 100 NA 
Silva, 2019 Brazil NA 199 NA NA NA MGIT and LJ 
Adult ES4 group 
Al-Darraji, 2013 Malaysia Prison 125 Decontamination and centrifugation NA 100 MGIT 
Boum, 2016 Uganda NA 1016 NA 20 66 MGIT 
Friedrich, 2011 South Africa NA 292 Decontamination and centrifugation 97 42 MGIT 
Geleta, 2015 Somali NA 227 Decontamination 26 NA MGIT and LJ 
LaCourse, 2016 Kenya NA 288 Decontamination NA 100 MGIT 
Luetkemeyer, 2016* American, Brazil, and Rio de Janeiro Inpatients and outpatients 992 Unprocessed 15 NA MGIT and solid media 
Madico, 2016 Mbarara and Uganda Inpatients 261 Decontamination 15 100 MGIT 
Pinyopornpanish, 2015 Thailand NA 57 NA 14 46 MGIT 
Rachow, 2011 Tanzania NA 126 Decontamination NA NA MGIT and LJ 
Rasheed, 2019 Pakistan NA 225 Unprocessed NA LJ 
Rice, 2017* American NA 637 Decontamination and centrifugation 30 NA MGIT and LJ 
Sahle, 2019 Ethiopia Prison 13803 NA 0.1 0.6 MGIT 
Sharma, 2015* India NA 71 Decontamination and centrifugation NA NA MGIT and LJ 
Yeong, 2020 Australia Inpatients 56 Decontamination 11 MGIT 
Adult IS5 group 
Acuna, 2017 Uganda Inpatients and outpatients 860 Decontamination 89 69 MGIT 
Luetkemeyer, 2016* America and Brazil NA 324 NA NA NA MGIT and solid media 
Mishra, 2020 South Africa NA 585 Decontamination 12 20 MGIT and LJ 
O’Grady, 2012 Zambia Inpatients 881 Centrifugation 18 71 MGIT 
Rice, 2017* America NA 653 Decontamination and centrifugation NA NA MGIT and LJ 
Sharma, 2015* India NA 71 Decontamination and centrifugation NA NA MGIT and LJ 
Sohn, 2014 Canada NA 502 Decontamination MGIT 
Pediatric ES group 
Bates, 2013* Zambia Inpatients 142 Decontamination and centrifugation 31 MGIT 
Reither, 2015 Tanzania and Uganda NA 451 Decontamination NA 44 MGIT and LJ 
Pediatric GA6 group 
Bates, 2013* Zambia Inpatients 788 Decontamination and centrifugation 30 MGIT 
Das, 2018* India and adjoining areas of Nepal Inpatients and outpatients 106 NA NA NA LJ 
Hasan, 2017 Pakistan NA 49 Decontamination and centrifugation NA NA MGIT 
Myo, 2018 Myanmar NA 231 Decontamination and centrifugation 13 LJ 
Pang, 2014 China NA 211 Decontamination and centrifugation NA MGIT 
Sharma 2020 India Outpatients 210 Decontamination NA MGIT 
Pediatric IS group 
Das, 2018* India and adjoining areas of Nepal Inpatients and outpatients Nebulization NA NA LJ 
LaCourse, 2014 Malawi NA 300 NA 17 MGIT 
Nicol, 2011 South Africa NA 452 Decontamination and centrifugation 24 MGIT 
Nicol, 2013 South Africa NA 115 Decontamination and centrifugation 15 MGIT 
Nicol, 2018 South Africa NA 367 Decontamination and centrifugation NA 19 MGIT and LJ 
Sekadde, 2013 Uganda Mixed 250 Digestion and decontamination 42 MGIT and LJ 
Togun, 2015 Gambia Outpatients 487 Digestion, decontamination, and centrifugation MGIT and LJ 
Zar, 2012* South Africa Inpatients 396 Decontamination and centrifugation 20 MGIT 
Zar, 2013* South Africa NA 384 Decontamination and centrifugation MGIT 
Pediatric NPA7 group 
Zar, 2012* South Africa Inpatients 396 Decontamination and centrifugation 20 MGIT 
Zar, 2013* South Africa NA 384 Decontamination and centrifugation NA NA MGIT 
Study (year)Country or AreaPatient recruitmentSample sizeSample processingSmear- positive rate (%)HIV- positive rate (%)Culture type
Adult BAL1 group 
Barnard, 2015 South Africa NA2 112 Decontamination and centrifugation 15 NA MGIT 
Gowda, 2018 India NA 60 Unprocessed 12 MGIT 
Jo, 2016 South Korea NA 320 Centrifugation MGIT and Ogawa 
Khalil, 2015 Pakistan NA 93 NA 37 NA LJ3 
Kilaru, 2019 India Inpatients and outpatients 56 Decontamination and centrifugation 38 MGIT 
Ko, 2016 Korea NA 249 NA NA MGIT and Ogawa 
Lee, 2013 Korea NA 132 Decontamination and centrifugation MGIT and Ogawa 
Lu, 2018 China NA 238 Decontamination NA MGIT and LJ 
Mok, 2016 Singapore Inpatients and outpatients 158 NA NA MGIT 
Pan, 2018 China NA 190 Decontamination 19 NA MGIT 
Prakash, 2018 Six countries Inpatients and outpatients 383 NA NA MGIT and LJ 
Sánchez-Cabral, 2020 Brazil NA 54 NA NA 41 MGIT and LJ 
Sharma, 2015* India NA 131 Decontamination and centrifugation NA NA MGIT and LJ 
Theron, 2013 South Africa NA 154 Centrifugation 10 30 MGIT 
To, 2018 China NA 237 NA NA NA 
Ullah, 2017 Pakistan NA 98 Decontamination 100 NA 
Silva, 2019 Brazil NA 199 NA NA NA MGIT and LJ 
Adult ES4 group 
Al-Darraji, 2013 Malaysia Prison 125 Decontamination and centrifugation NA 100 MGIT 
Boum, 2016 Uganda NA 1016 NA 20 66 MGIT 
Friedrich, 2011 South Africa NA 292 Decontamination and centrifugation 97 42 MGIT 
Geleta, 2015 Somali NA 227 Decontamination 26 NA MGIT and LJ 
LaCourse, 2016 Kenya NA 288 Decontamination NA 100 MGIT 
Luetkemeyer, 2016* American, Brazil, and Rio de Janeiro Inpatients and outpatients 992 Unprocessed 15 NA MGIT and solid media 
Madico, 2016 Mbarara and Uganda Inpatients 261 Decontamination 15 100 MGIT 
Pinyopornpanish, 2015 Thailand NA 57 NA 14 46 MGIT 
Rachow, 2011 Tanzania NA 126 Decontamination NA NA MGIT and LJ 
Rasheed, 2019 Pakistan NA 225 Unprocessed NA LJ 
Rice, 2017* American NA 637 Decontamination and centrifugation 30 NA MGIT and LJ 
Sahle, 2019 Ethiopia Prison 13803 NA 0.1 0.6 MGIT 
Sharma, 2015* India NA 71 Decontamination and centrifugation NA NA MGIT and LJ 
Yeong, 2020 Australia Inpatients 56 Decontamination 11 MGIT 
Adult IS5 group 
Acuna, 2017 Uganda Inpatients and outpatients 860 Decontamination 89 69 MGIT 
Luetkemeyer, 2016* America and Brazil NA 324 NA NA NA MGIT and solid media 
Mishra, 2020 South Africa NA 585 Decontamination 12 20 MGIT and LJ 
O’Grady, 2012 Zambia Inpatients 881 Centrifugation 18 71 MGIT 
Rice, 2017* America NA 653 Decontamination and centrifugation NA NA MGIT and LJ 
Sharma, 2015* India NA 71 Decontamination and centrifugation NA NA MGIT and LJ 
Sohn, 2014 Canada NA 502 Decontamination MGIT 
Pediatric ES group 
Bates, 2013* Zambia Inpatients 142 Decontamination and centrifugation 31 MGIT 
Reither, 2015 Tanzania and Uganda NA 451 Decontamination NA 44 MGIT and LJ 
Pediatric GA6 group 
Bates, 2013* Zambia Inpatients 788 Decontamination and centrifugation 30 MGIT 
Das, 2018* India and adjoining areas of Nepal Inpatients and outpatients 106 NA NA NA LJ 
Hasan, 2017 Pakistan NA 49 Decontamination and centrifugation NA NA MGIT 
Myo, 2018 Myanmar NA 231 Decontamination and centrifugation 13 LJ 
Pang, 2014 China NA 211 Decontamination and centrifugation NA MGIT 
Sharma 2020 India Outpatients 210 Decontamination NA MGIT 
Pediatric IS group 
Das, 2018* India and adjoining areas of Nepal Inpatients and outpatients Nebulization NA NA LJ 
LaCourse, 2014 Malawi NA 300 NA 17 MGIT 
Nicol, 2011 South Africa NA 452 Decontamination and centrifugation 24 MGIT 
Nicol, 2013 South Africa NA 115 Decontamination and centrifugation 15 MGIT 
Nicol, 2018 South Africa NA 367 Decontamination and centrifugation NA 19 MGIT and LJ 
Sekadde, 2013 Uganda Mixed 250 Digestion and decontamination 42 MGIT and LJ 
Togun, 2015 Gambia Outpatients 487 Digestion, decontamination, and centrifugation MGIT and LJ 
Zar, 2012* South Africa Inpatients 396 Decontamination and centrifugation 20 MGIT 
Zar, 2013* South Africa NA 384 Decontamination and centrifugation MGIT 
Pediatric NPA7 group 
Zar, 2012* South Africa Inpatients 396 Decontamination and centrifugation 20 MGIT 
Zar, 2013* South Africa NA 384 Decontamination and centrifugation NA NA MGIT 
*

These studies were divided into at least two groups.

a, BAL.

b, Not available.

c, Löwenstein–Jensen medium.

d, ES.

e, IS.

f, GA.

g, NPAs.

Risk of bias

The risk of bias of the included studies is shown in Table 2.

Table 2
The risk of bias of the included studies
StudyRisk of biasApplicability concerns
Patient selectionIndex testReference standardFlow and timingPatient selectionIndex testReference standard
Adult BAL1 group 
Barnard, 2015 unclear unclear low low low low low 
Gowda, 2018 unclear low low low low low low 
Jo, 2016 unclear low low low unclear low low 
Khalil, 2015 unclear unclear unclear unclear low low low 
Kilaru, 2019 low low low low low low low 
Ko, 2016 unclear unclear unclear unclear low low low 
Lee, 2013 unclear low low low low low low 
Lu, 2018 low low low low low low low 
Mok, 2016 unclear low low low low low low 
Pan, 2018 unclear low low low low low low 
Prakash, 2018 low low low low low low high 
Sánchez-Cabral, 2020 low low low low low low low 
Sharma, 2015* low low low low low low low 
Silva, 2019 unclear unclear low low low low low 
Theron, 2013 unclear low low low low low low 
To, 2018 unclear low low low unclear low low 
Ullah, 2017 unclear low low low high low low 
Adult ES2 group 
Al-Darraji, 2013 high low low low low low unclear 
Boum, 2016 low low low low low low low 
Friedrich, 2011 unclear low low low low low low 
Geleta, 2015 low low low low low low low 
LaCourse, 2016 high low low low low low low 
Luetkemeyer, 2016* low low low low low low low 
Madico, 2016 high low low low low low low 
Pinyopornpanish, 2015 low low low low low low low 
Rachow, 2011 low low low low low low low 
Rasheed, 2019 low low low low low low low 
Rice, 2017* low low low low low low low 
Sahle, 2019 low low low low low low unclear 
Sharma, 2015* low low low low low low low 
Yeong, 2020 low low low low low low low 
Adult IS3 group 
Acuna, 2017 high low low low low low low 
Luetkemeyer, 2016* unclear low unclear low low low low 
Mishra, 2020 low low low low low low low 
O’Grady, 2012 unclear low low low low low low 
Rice, 2017* unclear unclear unclear low low low low 
Sharma, 2015* low low low low low low low 
Sohn, 2014 low unclear unclear low low low low 
Pediatric ES group 
Bates, 2013* low low low low low low low 
Reither, 2015 low low low low low low low 
Pediatric GA4 group 
Bates, 2013* unclear low low low low low low 
Das, 2018* unclear unclear unclear low low low low 
Hasan, 2017 unclear low low low low low low 
Myo, 2018 unclear low low low low low low 
Pang, 2014 low low low low high low low 
Sharma, 2020 low low low low low low low 
Pediatric IS group 
Das, 2018* unclear unclear unclear low low low low 
LaCourse, 2014 low unclear unclear low high low low 
Nicol, 2011 low low low low low low low 
Nicol, 2013 low unclear unclear low low low low 
Nicol, 2018 low low unclear low low low low 
Sekadde, 2013 high low low low low low low 
Togun, 2015 low low low low high low low 
Zar, 2012* low low low low low low low 
Zar, 2013* low low low low low low low 
Pediatric NPA5 group 
Zar, 2012* low low low low low low low 
Zar, 2013* low low low low low low low 
StudyRisk of biasApplicability concerns
Patient selectionIndex testReference standardFlow and timingPatient selectionIndex testReference standard
Adult BAL1 group 
Barnard, 2015 unclear unclear low low low low low 
Gowda, 2018 unclear low low low low low low 
Jo, 2016 unclear low low low unclear low low 
Khalil, 2015 unclear unclear unclear unclear low low low 
Kilaru, 2019 low low low low low low low 
Ko, 2016 unclear unclear unclear unclear low low low 
Lee, 2013 unclear low low low low low low 
Lu, 2018 low low low low low low low 
Mok, 2016 unclear low low low low low low 
Pan, 2018 unclear low low low low low low 
Prakash, 2018 low low low low low low high 
Sánchez-Cabral, 2020 low low low low low low low 
Sharma, 2015* low low low low low low low 
Silva, 2019 unclear unclear low low low low low 
Theron, 2013 unclear low low low low low low 
To, 2018 unclear low low low unclear low low 
Ullah, 2017 unclear low low low high low low 
Adult ES2 group 
Al-Darraji, 2013 high low low low low low unclear 
Boum, 2016 low low low low low low low 
Friedrich, 2011 unclear low low low low low low 
Geleta, 2015 low low low low low low low 
LaCourse, 2016 high low low low low low low 
Luetkemeyer, 2016* low low low low low low low 
Madico, 2016 high low low low low low low 
Pinyopornpanish, 2015 low low low low low low low 
Rachow, 2011 low low low low low low low 
Rasheed, 2019 low low low low low low low 
Rice, 2017* low low low low low low low 
Sahle, 2019 low low low low low low unclear 
Sharma, 2015* low low low low low low low 
Yeong, 2020 low low low low low low low 
Adult IS3 group 
Acuna, 2017 high low low low low low low 
Luetkemeyer, 2016* unclear low unclear low low low low 
Mishra, 2020 low low low low low low low 
O’Grady, 2012 unclear low low low low low low 
Rice, 2017* unclear unclear unclear low low low low 
Sharma, 2015* low low low low low low low 
Sohn, 2014 low unclear unclear low low low low 
Pediatric ES group 
Bates, 2013* low low low low low low low 
Reither, 2015 low low low low low low low 
Pediatric GA4 group 
Bates, 2013* unclear low low low low low low 
Das, 2018* unclear unclear unclear low low low low 
Hasan, 2017 unclear low low low low low low 
Myo, 2018 unclear low low low low low low 
Pang, 2014 low low low low high low low 
Sharma, 2020 low low low low low low low 
Pediatric IS group 
Das, 2018* unclear unclear unclear low low low low 
LaCourse, 2014 low unclear unclear low high low low 
Nicol, 2011 low low low low low low low 
Nicol, 2013 low unclear unclear low low low low 
Nicol, 2018 low low unclear low low low low 
Sekadde, 2013 high low low low low low low 
Togun, 2015 low low low low high low low 
Zar, 2012* low low low low low low low 
Zar, 2013* low low low low low low low 
Pediatric NPA5 group 
Zar, 2012* low low low low low low low 
Zar, 2013* low low low low low low low 
*

The studies of these authors were divided into at least two groups.

a, BAL.

b, ES.

c, IS.

d, GA.

e, NPAs.

Among 17 studies in the adult BAL group, 5 articles were assessed as low risk and the rest had an unclear risk on the patient selection. For the index test, 13 publications had a low risk and 4 publications had an unclear risk. In the aspect of reference standard and flow and timing, all trials gave clear description about trial details, except for two [27,29]. A total of 14 studies had low concern for applicability, however, the risk of patient selection of 2 studies [37,72] were unclear and that of Ullah et al. [38] was high.

For 14 studies in the adult ES group, 10 studies were assessed as low risk on the patient selection, 3/14 were considered as high risk and the remaining was identified as unclear risk. All of them showed low risk on the index test, reference standard and flow, and timing. Low concern of patient selection and index test appeared in all 14 publications. All studies were evaluated as low risk on reference standard except for two [40,51].

For seven studies in the adult IS group, three articles had low risk of patient selection, three articles had unclear risk of patient selection and only one had high risk of patient selection. Two studies [50,56], which did not state whether the results from two methods were double-blind, assessed as unclear risk on the index test and reference standard. Besides, Luetkemeyer et al. [45] also had an unclear risk on reference standard. All articles in this group were considered as low risk on applicability concerns.

In the pediatric ES group, two studies showed low risk in risk of bias and applicability concerns.

In the pediatric GA group, two articles had low risk of patient selection while others were of unclear risk. Unclear risk of the index test and reference standard only appeared in one publication. Five trials had low risk of flow and timing and applicability concerns, and one [62] was evaluated as high risk for patient selection.

In the pediatric IS group, all studies had low risk of patient selection except two [59,68]. Das et al. [59], LaCourse et al. [64], and Nicol et al. [66] had unclear risk of the index test and reference standard. Five articles had low concern of applicability, while another two studies [64,69] were assessed as high risk of patient selection.

In the pediatric NPA group, two studies showed low risk of bias and applicability concerns.

Heterogeneity

The value of Spearman correlation coefficient showed that the threshold effects did not exist in the adult BAL group (Spearman correlation coefficient: 0.158; P=0.727), adult ES group (Spearman correlation coefficient: 0.251; P=0.387), adult IS group (Spearman correlation coefficient: 0.321; P=0.482), pediatric GA group (Spearman correlation coefficient: −0.257; P=0.623), and pediatric IS group (Spearman correlation coefficient: 0.200; P=0.606). Besides, the distribution of points in sROC curve also confirmed this. Due to the limited articles in the pediatric ES group and pediatric NPA group, Spearman correlation coefficient and sROC were unavailable.

The value of Chi-square P-values indicted heterogeneity was caused by non-threshold effects in the adult BAL group (Chi-square: 42.33; P<0.001), adult ES group (Chi-square: 52.15; P<0.001), adult IS group (Chi-square: 18.95; P=0.004), pediatric ES group (Chi-square: 13.89; P=0.002) and pediatric GA group (Chi-square: 47.45; P<0.001). However, non-threshold effects were not detected in the pediatric IS group (Chi-square: 8.40; P=0.396), and pediatric NPA group (Chi-square: 1.57; P=0.211). The sROC curves for Xpert MTB/RIF of five groups (adult BAL group, adult ES group, adult IS group, pediatric GA group, and pediatric IS group) are shown in Figure 2.

sROC curve for Xpert MTB/RIF of five groups

Figure 2
sROC curve for Xpert MTB/RIF of five groups

(A) sROC of adult BAL group; (B) sROC of adult ES group; (C) sROC of adult IS group; (D) sROC of pediatric GA group; (E) sROC of pediatric IS group.

Figure 2
sROC curve for Xpert MTB/RIF of five groups

(A) sROC of adult BAL group; (B) sROC of adult ES group; (C) sROC of adult IS group; (D) sROC of pediatric GA group; (E) sROC of pediatric IS group.

Results on diagnostic accuracy

The diagnostic performance of Xpert MTB/RIF was calculated, compared with culture. Xpert MTB/RIF showed the pooled sensitivity, pooled specificity, and AUC were 87% (95% CI: 0.84–0.89), 91% (95% CI: 0.90–0.92) and 95% (95% CI: 0.93–0.97) in the adult BAL group; 90% (95% CI: 0.88–0.91), 98% (95% CI: 0.97–0.98) and 97% (95% CI: 0.95–0.99) in the adult ES group; 86% (95% CI: 0.84–0.89), 97% (95% CI: 0.96–0.98) and 98% (95% CI: 0.94–0.99) in the adult IS group; 80% (95% CI: 0.72–0.87), 94% (95% CI: 0.92–0.95) and 95% (95% CI: 0.87–1.00) in the pediatric GA group; 67% (95% CI: 0.62–0.72), 99% (95% CI: 0.98–0.99) and 97% (95% CI: 0.92–1.00) in the pediatric IS group, respectively. The pooled sensitivity and pooled specificity were 14% (95% CI: 0.10–0.19) and 99% (95% CI: 0.97–1.00) in the pediatric ES group and 54% (95% CI: 0.43–0.64) and 99% (95% CI: 0.97–0.99) in the pediatric NPA group, respectively. Due to the limited articles in the pediatric ES group and pediatric NPA group, the AUC was unavailable.

The summary plots of Xpert MTB/RIF for adult and children are presented in Figures 3 and 4, respectively.

The summary plots of adult PTB

Figure 3
The summary plots of adult PTB

(A) Summary plots of adult BAL group; (B) summary plots of adult ES group; (C) summary plots of adult IS group. Abbreviations: FN, false-negative; FP, false-positive; TP, true-positive; TN, true-negative.

Figure 3
The summary plots of adult PTB

(A) Summary plots of adult BAL group; (B) summary plots of adult ES group; (C) summary plots of adult IS group. Abbreviations: FN, false-negative; FP, false-positive; TP, true-positive; TN, true-negative.

The summary plots of pediatric PTB

Figure 4
The summary plots of pediatric PTB

(A) Summary plots of pediatric ES group; (B) summary plots of pediatric GA group; summary plots of pediatric IS group; (D) summary plots of pediatric NPA group. Abbreviations: FN, false-negative; FP, false-positive; TN, true-negative; TP, true-positive.

Figure 4
The summary plots of pediatric PTB

(A) Summary plots of pediatric ES group; (B) summary plots of pediatric GA group; summary plots of pediatric IS group; (D) summary plots of pediatric NPA group. Abbreviations: FN, false-negative; FP, false-positive; TN, true-negative; TP, true-positive.

Subgroup analysis was carried out to determine the potential source of heterogeneity among included articles. The detailed information is presented in Supplementary Table S1.

Since we only included studies using Xpert MTB/RIF, all comparisons made were between the types of biological samples used. For adults, choosing ES as sample provided better diagnostic performance than BAL or IS. For children, GA showed superior diagnostic capacity to ES, IS, or NPA. Of note, GA also has its own disadvantages including invasive sampling and needing approximately three consecutive days for collecting samples. The actual choice needs to be decided according to specific situations.

It is particularly notorious that microbiology methods (including sputum microscopy and culture), immunological methods [interferon-γ release assay (IGRA) and T-cell spot (T-SPOT)] and molecular methods [polymerase chain reaction (PCR)] are widely applied worldwide before the Xpert MTB/RIF came to market. The celerity and convenience of sputum microscopy make it the most commonly used method in clinical practice, nonetheless low sensitivity (20–40%) is the fatal defect of the sputum microscopy [73]. Culture, as the reference standard for the diagnosis of TB, requires too long detection time and a highly equipped laboratory. Thus, culture is greatly limited in some developing regions typically facing a high TB burden. IGRA and T-SPOT are effective ways to screen latent TB infection. However, inoculating Bacille Calmette–Guérin (BCG), prior infection and inhalation of non-pathogenic mycobacterium can result in false positivity of IGRA and T-SPOT [74]. Thus, the clinical value of these two diagnostic methods is somewhat discounted in TB endemic areas. Although PCR is a relatively sensitive method to detect MTB, high requirement of operator skills and a laboratory environment also restrict its application. The emergency of Xpert MTB/RIF meets the urgent demand for rapid, simple, integrated, and fully automated detective methods of MTB detection. A myriad of randomized controlled trials (RCTs) have been carried out to assess the diagnostic performance of Xpert MTB/RIF and found the detection rate of MTB is dramatically improved by this novel molecular tool [75]. Apparent benefit brought by Xpert MTB/RIF to PTB patient individually and public health enables this molecular tool to be rapidly accepted and applied all over the world.

However, Xpert MTB/RIF still faces some challenges of inadequate sensitivity when diagnosing smear-negative PTB, pediatric PTB, HIV-associated PTB, and extra-PTB [76,77]. In order to increase diagnostic yield, the Xpert MTB/RIF Ultra (Ultra; Cepheid) appeared. Based on the same platform as the Xpert MTB/RIF, the Ultra harbors some improvements including using two PCR amplification targets (IS6110 and IS1081) and a melt curve analysis [78]. These two changes confer Ultra’s lower limit of detection than Xpert MTB/RIF and culture [78]. The following clinical trials have found that improper sample types and poor sample quality still have an adverse influence on the sensitivity of Ultra [79,80]. Obviously, no matter how sensitive the detection method is, choosing proper sample type and ensuring the quality of sample are crucial.

In our systematic review, choosing ES as sample provided better diagnostic performance (sensitivity, specificity, and AUC) than BAL or IS for adult PTB. Since both BAL and IS had a higher bacterial load than ES [81,82], they should theoretically provide better diagnostic performance than ES. However, our study showed opposite results. One reason is the smaller volume of IS, ranging from 0.5 to 2 ml, whereas the volume of ES ranges from 1 ml to more than 2 ml. This inconsistency may also be explained by the ratio of processed samples. The ratio of processed samples in the adult ES group was higher than that in the adult BAL group. In addition, we also found there was a high agreement in diagnosing adult PTB when using IS and BAL as sample. Considering cost, sampling method, acceptance and other factors, IS might be a better choice than BAL to diagnose PTB. Blau et al. [83] gave the interpretation of this results that sampling from one or two parts of lung led to poor repeatability and limited detection rate when taking BAL as sample to diagnose lung infection diseases. Garcia et al. [84] found the gene transcriptional patterns of MTB in BAL and sputum were similar, and additionally, sputum could reflect the physiological state of MTB in the lower airway of PTB patients. Conde et al. [85] conducted a trial and reconfirmed the higher diagnostic yield of taking IS (relative to BAL) as sample to diagnose PTB, in line with our results.

For pediatric PTB, similar specificity and AUC appeared when using IS and GA as sample, nonetheless, Xpert MTB/RIF showed much higher sensitivity when using GA as sample to diagnose pediatric PTB. Although GA is sampled by invasive ways and need approximately three consecutive days for collecting samples, the characteristics of lower probability of contamination, easy sample collection, and sufficient sample volume still make GA become an ideal sample type to increase detection rate for pediatric PTB. Ruiz Jimenez et al. [86] may agree with our results that GA is an excellent sample for diagnosing PTB, and IS should be considered as a supplementary sample type. However, Zar et al. [87] drew an opposite conclusion by conducting a prospective study, with IS for diagnosing PTB and GA as supplementary. The differences may result from different diagnostic tools and the variation in sample collection. The culture, used in their study [87], was unable to detect dead MTB which was caused by the neutralization of GA [88], while Xpert MTB/RIF used in our included studies can detect both dead and live MTB and is not affected by sample neutralization. Furthermore, sample collection, technician skill level, the time from sampling to testing and choice of pre-processing methods also influence finial accuracy [89]. Thus, it is worthy for us to pay attention to the standardization of GA sampling.

Subgroup analysis showed that for adult PTB, superior diagnostic performance appeared in the smear-positive subgroup whether using BAL, ES, or IS as samples. Xpert MTB/RIF showed similar sensitivity whether using smear-positive BAL, smear-positive ES or smear-positive IS, while the highest AUC (90%) was identified when using BAL to diagnose smear-negative PTB. BAL was sampled directly from the lesion region, which was more suitable and reliable for detecting PTB patients with low bacterial load [90,91]. In addition, outperformed capacity of Xpert MTB/RIF appeared when detecting MTB in IS or ES than in BAL for HIV-positive patients. Singer-Leshinsky et al. [92] obtained consistent results with ours that collecting IS from HIV patients may be more beneficial than others. Sample pre-procession steps, the number of samples, the immune status of patient, and the degree of disease severity are the potential confounding factors. More sound evidence is still lacking to support the value of ES in diagnosing HIV-associated adult PTB, which need to be further confirmed.

For pediatric PTB, we reconfirmed the strong association between smear status, bacterial load, and the diagnostic performance of Xpert MTB/RIF. Besides, testing for MTB in IS from children provided a little additional diagnostic yield from the prospective of specificity and AUC for Xpert MTB/RIF when diagnosing smear-negative PTB, however, Xpert MTB/RIF had higher likelihood to detect MTB in GA from these smear-negative children who really suffered from PTB. Maynard-Smith et al. [6] reported that detecting MTB in GA for sputum-scarce PTB was a good choice. However, Maynard-Smith et al. did not analyze the diagnostic accuracy of Xpert MTB/RIF when detecting MTB in IS from children and make further comparisons. The diagnostic value of GA and IS for smear-negative PTB for children still need to be explored. In the pediatric IS group, subgroup analysis showed that the pooled sensitivity was very heterogeneous between HIV-positive and HIV-negative subgroups and higher sensitivity was in the HIV-positive subgroup. This result was contrary to Connell et al. [93], however, supported by Nicol et al. [65] The smear status, disease severity and samples’ pre-processing may affect the results, and a further research should be warranted.

Our study made a rigorous and comprehensive analysis and comparison about the diagnostic performance of Xpert MTB/RIF using different sample types for PTB patients with different ages by including as many studies as possible. Furthermore, we performed reasonable subgroup analysis to identify the source of heterogeneity and potential factors. However, the present study also has some limitations. Limited articles in the pediatric ES group and pediatric NPA group restricted us from further analysis. Some factors including cost, the tolerance of patients and sample contamination rate should be taken into consideration for final choice of diagnostics. Most included articles only used one sample type to analyze the performance of Xpert MTB/RIF, and thus the comparison of different sample types may be influenced by a number of potential confounding factors such as patient characteristics and the process of sampling. Thus, more eligible trials are needed.

When considering diagnostic performance of Xpert MTB/RIF, sampling method and patients’ tolerance, using ES as sample might be a better choice to diagnose adult PTB than using BAL and IS. For pediatric PTB, GA was superior to other samples. However, the invasive sampling and relatively long time of collecting samples also should be considered. The actual choice of sample types needs to be decided according to specific situations.

All data generated or analyzed in the present study are contained and presented in this document.

The authors declare that there are no competing interests associated with the manuscript.

This work was supported by the National Science and Technology Major Project [grant number 2018ZX10715003-001].

Mengyuan Lyu designed the present study, conducted literature search, extracted the data, made data analysis and interpretation, drafted and revised the manuscript. Jian Zhou designed the study, conducted literature search, extracted the data, drafted and revised the manuscript. Yuhui Cheng conducted literature search and analyzed the data. Weelic Chong, Kang Wu, Teng Fang and Tianbo Fu extracted data. Binwu Ying gave administrative support. All authors have read and approved the final manuscript.

     
  • AUC

    area under the curve

  •  
  • BAL

    bronchoalveolar lavage

  •  
  • CI

    confidence interval

  •  
  • ES

    expectorated sputum

  •  
  • GA

    gastric aspiration

  •  
  • IGRA

    interferon-γ release assay

  •  
  • IS

    induced sputum

  •  
  • MTB

    Mycobacterium tuberculosis

  •  
  • NPA

    nasopharyngeal aspirate

  •  
  • PCR

    polymerase chain reaction

  •  
  • PTB

    pulmonary tuberculosis

  •  
  • sROC

    summary receiver operating characteristic

  •  
  • TB

    tuberculosis

  •  
  • T-SPOT

    T-cell spot

  •  
  • WHO

    World Health Organization

1.
WHO
(
2019
)
Global tuberculosis report 2019
.
2.
Chee
C.B.E.
,
Reves
R.
,
Zhang
Y.
and
Belknap
R.
(
2018
)
Latent tuberculosis infection: opportunities and challenges
.
Respirology
23
,
893
900
3.
Lawn
S.D.
,
Mwaba
P.
,
Bates
M.
,
Piatek
A.
,
Alexander
H.
,
Marais
B.J.
et al.
(
2013
)
Advances in tuberculosis diagnostics: the Xpert MTB/RIF assay and future prospects for a point-of-care test
.
Lancet Infect. Dis.
13
,
349
361
[PubMed]
4.
Yew
W.W.
and
Yoshiyama
T.
(
2018
)
Epidemiological, clinical and mechanistic perspectives of tuberculosis in older people
.
Respirology
23
,
567
575
5.
Walzl
G.
,
McNerney
R.
,
du Plessis
N.
,
Bates
M.
,
McHugh
T.D.
,
Chegou
N.N.
et al.
(
2018
)
Tuberculosis: advances and challenges in development of new diagnostics and biomarkers
.
Lancet Infect. Dis.
18
,
e199
e210
6.
Maynard-Smith
L.
,
Larke
N.
,
Peters
J.A.
and
Lawn
S.D.
(
2014
)
Diagnostic accuracy of the Xpert MTB/RIF assay for extrapulmonary and pulmonary tuberculosis when testing non-respiratory samples: a systematic review
.
BMC Infect. Dis.
14
,
709
7.
Theron
G.
,
Peter
J.
,
Meldau
R.
,
Khalfey
H.
,
Gina
P.
,
Matinyena
B.
et al.
(
2013
)
Accuracy and impact of Xpert MTB/RIF for the diagnosis of smear-negative or sputum-scarce tuberculosis using bronchoalveolar lavage fluid
.
Thorax
68
,
1043
1051
[PubMed]
8.
Goussard
P.
and
Gie
R.
(
2014
)
The role of bronchoscopy in the diagnosis and management of pediatric pulmonary tuberculosis
.
Expert Rev. Respir. Med.
8
,
101
109
[PubMed]
9.
Heydari
A.A.
,
Movahhede Danesh
M.R.
and
Ghazvini
K.
(
2014
)
Urine PCR evaluation to diagnose pulmonary tuberculosis
.
Jundishapur J. Microbiol.
7
,
e9311
[PubMed]
10.
Konno
A.
,
Narumoto
O.
,
Matsui
H.
,
Takeda
K.
,
Hirano
Y.
,
Shinfuku
K.
et al.
(
2019
)
The benefit of stool mycobacterial examination to diagnose pulmonary tuberculosis for adult and elderly patients
.
J. Clin. Tuberc. Other Mycobact. Dis.
16
,
100106
11.
Walters
E.
,
Demers
A.M.
,
van der Zalm
M.M.
,
Whitelaw
A.
,
Palmer
M.
,
Bosch
C.
et al.
(
2017
)
Stool culture for diagnosis of pulmonary tuberculosis in children
.
J. Clin. Microbiol.
55
,
3355
3365
[PubMed]
12.
Geldenhuys
H.D.
,
Kleynhans
W.
,
Buckerfield
N.
,
Tameris
M.
,
Gonzalez
Y.
,
Mahomed
H.
et al.
(
2012
)
Safety and tolerability of sputum induction in adolescents and adults with suspected pulmonary tuberculosis
.
Eur. J. Clin. Microbiol. Infect. Dis.
31
,
529
537
[PubMed]
13.
Heron
M.
,
Grutters
J.C.
,
ten Dam-Molenkamp
K.M.
,
Hijdra
D.
,
van Heugten-Roeling
A.
,
Claessen
A.M.
et al.
(
2012
)
Bronchoalveolar lavage cell pattern from healthy human lung
.
Clin. Exp. Immunol.
167
,
523
531
[PubMed]
14.
Meyer
K.C.
(
2007
)
Bronchoalveolar lavage as a diagnostic tool
.
Semin. Respir. Crit. Care Med.
28
,
546
560
[PubMed]
15.
World Health Organization
(
2013
)
Laboratory testing for Middle East respiratory syndrome coronavirus;
.
16.
Liu
X.
,
Hou
X.F.
,
Gao
L.
,
Deng
G.F.
,
Zhang
M.X.
,
Deng
Q.Y.
et al.
(
2018
)
Indicators for prediction of Mycobacterium tuberculosis positivity detected with bronchoalveolar lavage fluid
.
Infect. Dis. Poverty
7
,
22
17.
Kloepfer
K.M.
,
Deschamp
A.R.
,
Ross
S.E.
,
Peterson-Carmichael
S.L.
,
Hemmerich
C.M.
,
Rusch
D.B.
et al.
(
2018
)
In children, the microbiota of the nasopharynx and bronchoalveolar lavage fluid are both similar and different
.
Pediatr. Pulmonol.
53
,
475
482
[PubMed]
18.
Lu
A.Z.
,
Shi
P.
,
Wang
L.B.
,
Qian
L.L.
and
Zhang
X.B.
(
2017
)
Diagnostic value of nasopharyngeal aspirates in children with lower respiratory tract infections
.
Chin. Med. J. (Engl.)
130
,
647
651
[PubMed]
19.
Maciel
E.L.
,
Brotto
L.D.
,
Sales
C.M.
,
Zandonade
E.
and
Sant’anna
C.C.
(
2010
)
Gastric lavage in the diagnosis of pulmonary tuberculosis in children: a systematic review
.
Rev. Saude Publica
44
,
735
742
[PubMed]
20.
Stockdale
A.J.
,
Duke
T.
,
Graham
S.
and
Kelly
J.
(
2010
)
Evidence behind the WHO guidelines: hospital care for children: what is the diagnostic accuracy of gastric aspiration for the diagnosis of tuberculosis in children?
J. Trop. Pediatr.
56
,
291
298
[PubMed]
21.
Piccini
P.
,
Chiappini
E.
,
Tortoli
E.
,
de Martino
M.
and
Galli
L.
(
2014
)
Clinical peculiarities of tuberculosis
.
BMC Infect. Dis.
14
,
S4
[PubMed]
22.
Shingadia
D.
and
Novelli
V.
(
2003
)
Diagnosis and treatment of tuberculosis in children
.
Lancet Infect. Dis.
3
,
624
632
[PubMed]
23.
Whiting
P.F.
,
Rutjes
A.W.
,
Westwood
M.E.
,
Mallett
S.
,
Deeks
J.J.
,
Reitsma
J.B.
et al.
(
2011
)
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
.
Ann. Intern. Med.
155
,
529
536
[PubMed]
24.
Barnard
D.A.
,
Irusen
E.M.
,
Bruwer
J.W.
,
Plekker
D.
,
Whitelaw
A.C.
,
Deetlefs
J.D.
et al.
(
2015
)
The utility of Xpert MTB/RIF performed on bronchial washings obtained in patients with suspected pulmonary tuberculosis in a high prevalence setting
.
BMC Pulm. Med.
15
,
103
[PubMed]
25.
Gowda
N.C.
,
Ray
A.
,
Soneja
M.
,
Khanna
A.
and
Sinha
S.
(
2018
)
Evaluation of Xpert((R))Mycobacterium tuberculosis/rifampin in sputum-smear negative and sputum-scarce patients with pulmonary tuberculosis using bronchoalveolar lavage fluid
.
Lung India
35
,
295
300
26.
Jo
Y.S.
,
Park
J.H.
,
Lee
J.K.
,
Heo
E.Y.
,
Chung
H.S.
and
Kim
D.K.
(
2016
)
Discordance between MTB/RIF and real-time tuberculosis-specific polymerase chain reaction assay in bronchial washing specimen and its clinical implications
.
PloS one
11
,
e0164923
27.
Khalil
K.F.
and
Butt
T.
(
2015
)
Diagnostic yield of Bronchoalveolar Lavage gene Xpert in smear-negative and sputum-scarce pulmonary tuberculosis
.
J. Coll. Phys. Surg. Pak.
25
,
115
118
[PubMed]
28.
Kilaru
S.C.
,
Chenimilla
N.P.
,
Syed
U.
,
Momin
K.
,
Kilaru
H.
,
Patil
E.
et al.
(
2019
)
Role of Xpert MTB/RIF in Bronchoalveolar lavage fluid of sputum-scarce, suspected Pulmonary TB patients
.
J. Clin. Tuberc. Other Mycobact. Dis.
14
,
7
11
29.
Ko
Y.
,
Lee
H.K.
,
Lee
Y.S.
,
Kim
M.Y.
,
Shin
J.H.
,
Shim
E.J.
et al.
(
2016
)
Accuracy of Xpert((R)) MTB/RIF assay compared with AdvanSure TB/NTM real-time PCR using bronchoscopy specimens
.
Int. J. Tuberc. Lung Dis.
20
,
115
120
30.
Lee
H.Y.
,
Seong
M.W.
,
Park
S.S.
,
Hwang
S.S.
,
Lee
J.
,
Park
Y.S.
et al.
(
2013
)
Diagnostic accuracy of Xpert(R) MTB/RIF on bronchoscopy specimens in patients with suspected pulmonary tuberculosis
.
Int. J. Tuberc. Lung Dis.
17
,
917
921
31.
Lu
Y.
,
Zhu
Y.
,
Shen
N.
,
Tian
L.
and
Sun
Z.
(
2018
)
Evaluating the diagnostic accuracy of the Xpert MTB/RIF assay on bronchoalveolar lavage fluid: a retrospective study
.
Int. J. Infect. Dis.
71
,
14
19
32.
Mok
Y.
,
Tan
T.Y.
,
Tay
T.R.
,
Wong
H.S.
,
Tiew
P.Y.
,
Kam
J.W.
et al.
(
2016
)
Do we need transbronchial lung biopsy if we have bronchoalveolar lavage Xpert(R) MTB/RIF?
Int. J. Tuberc. Lung Dis.
20
,
619
624
33.
Pan
X.
,
Yang
S.
,
Deighton
M.A.
,
Qu
Y.
,
Hong
L.
and
Su
F.
(
2018
)
A comprehensive evaluation of Xpert MTB/RIF assay with bronchoalveolar lavage fluid as a single test or combined with conventional assays for diagnosis of pulmonary tuberculosis in China: a two-center prospective study
.
Front. Microbiol.
9
,
444
[PubMed]
34.
Sanchez-Cabral
O.
,
Santillan-Diaz
C.
,
Paul Flores-Bello
A.
,
Ivannia Herrera-Ortega
M.
,
Luis Sandoval-Gutierrez
J.
,
Santillan-Doherty
P.
et al.
(
2020
)
GeneXpert (R) MTB/RIF assay with transbronchial lung cryobiopsy for Mycobacterium tuberculosis diagnosis
.
Ann. Transl. Med.
8
,
351
35.
Sharma
S.K.
,
Kohli
M.
,
Yadav
R.N.
,
Chaubey
J.
,
Bhasin
D.
,
Sreenivas
V.
et al.
(
2015
)
Evaluating the diagnostic accuracy of Xpert MTB/RIF assay in pulmonary tuberculosis
.
PLoS ONE
10
,
e0141011
[PubMed]
36.
Silva
T.M.D.
,
Soares
V.M.
,
Ramos
M.G.
and
Santos
A.D.
(
2019
)
Accuracy of a rapid molecular test for tuberculosis in sputum samples, bronchoalveolar lavage fluid, and tracheal aspirate obtained from patients with suspected pulmonary tuberculosis at a tertiary referral hospital
.
J. Bras. Pneumol.
45
,
e20170451
37.
To
K.W.
,
Kam
K.M.
,
Chan
D.P.C.
,
Yip
W.H.
,
Chan
K.P.
,
Lo
R.
et al.
(
2018
)
Utility of GeneXpert in analysis of bronchoalveolar lavage samples from patients with suspected tuberculosis in an intermediate-burden setting
.
J. Infect.
77
,
296
301
[PubMed]
38.
Ullah
I.
,
Javaid
A.
,
Masud
H.
,
Ali
M.
,
Basit
A.
,
Ahmad
W.
et al.
(
2017
)
Rapid detection of Mycobacterium tuberculosis and rifampicin resistance in extrapulmonary tuberculosis and sputum smear-negative pulmonary suspects using Xpert MTB/RIF
.
J. Med. Microbiol.
66
,
412
418
[PubMed]
39.
Prakash
A.K.
,
Datta
B.
,
Tripathy
J.P.
,
Kumar
N.
,
Chatterjee
P.
and
Jaiswal
A.
(
2018
)
The clinical utility of cycle of threshold value of GeneXpert MTB/RIF (CBNAAT) and its diagnostic accuracy in pulmonary and extra-pulmonary samples at a tertiary care center in India
.
Indian J. Tuberc.
65
,
296
302
40.
Al-Darraji
H.A.
,
Abd Razak
H.
,
Ng
K.P.
,
Altice
F.L.
and
Kamarulzaman
A.
(
2013
)
The diagnostic performance of a single GeneXpert MTB/RIF assay in an intensified tuberculosis case finding survey among HIV-infected prisoners in Malaysia
.
PLoS ONE
8
,
e73717
[PubMed]
41.
Boum
Y.
II
,
Kim
S.
,
Orikiriza
P.
,
Acuna-Villaorduna
C.
,
Vinhas
S.
,
Bonnet
M.
et al.
(
2016
)
Diagnostic accuracy of the small membrane filtration method for diagnosis of pulmonary tuberculosis in a high-HIV-prevalence setting
.
J. Clin. Microbiol.
54
,
1520
1527
[PubMed]
42.
Friedrich
S.O.
,
Venter
A.
,
Kayigire
X.A.
,
Dawson
R.
,
Donald
P.R.
and
Diacon
A.H.
(
2011
)
Suitability of Xpert MTB/RIF and genotype MTBDRplus for patient selection for a tuberculosis clinical trial
.
J. Clin. Microbiol.
49
,
2827
2831
[PubMed]
43.
Geleta
D.A.
,
Megerssa
Y.C.
,
Gudeta
A.N.
,
Akalu
G.T.
,
Debele
M.T.
and
Tulu
K.D.
(
2015
)
Xpert MTB/RIF assay for diagnosis of pulmonary tuberculosis in sputum specimens in remote health care facility
.
BMC Microbiol.
15
,
220
[PubMed]
44.
LaCourse
S.M.
,
Cranmer
L.M.
,
Matemo
D.
,
Kinuthia
J.
,
Richardson
B.A.
,
John-Stewart
G.
et al.
(
2016
)
Tuberculosis case finding in HIV-infected pregnant women in Kenya reveals poor performance of symptom screening and rapid diagnostic tests
.
J. Acquir. Immune Defic. Syndr.
71
,
219
227
[PubMed]
45.
Luetkemeyer
A.F.
,
Firnhaber
C.
,
Kendall
M.A.
,
Wu
X.
,
Mazurek
G.H.
,
Benator
D.A.
et al.
(
2016
)
Evaluation of Xpert MTB/RIF versus AFB smear and culture to identify pulmonary tuberculosis in patients with suspected tuberculosis from low and higher prevalence settings
.
Clin. Infect. Dis.
62
,
1081
1088
46.
Madico
G.
,
Mpeirwe
M.
,
White
L.
,
Vinhas
S.
,
Orr
B.
,
Orikiriza
P.
et al.
(
2016
)
Detection and quantification of Mycobacterium tuberculosis in the sputum of culture-negative HIV-infected pulmonary tuberculosis suspects: a proof-of-concept study
.
PLoS ONE
11
,
e0158371
[PubMed]
47.
Pinyopornpanish
K.
,
Chaiwarith
R.
,
Pantip
C.
,
Keawvichit
R.
,
Wongworapat
K.
,
Khamnoi
P.
et al.
(
2015
)
Comparison of Xpert MTB/RIF assay and the conventional sputum microscopy in detecting Mycobacterium tuberculosis in Northern Thailand
.
Tuberc. Res. Treat.
2015
,
571782
48.
Rachow
A.
,
Zumla
A.
,
Heinrich
N.
,
Rojas-Ponce
G.
,
Mtafya
B.
,
Reither
K.
et al.
(
2011
)
Rapid and accurate detection of Mycobacterium tuberculosis in sputum samples by Cepheid Xpert MTB/RIF assay–a clinical validation study
.
PLoS ONE
6
,
e20458
[PubMed]
49.
Rasheed
W.
,
Rao
N.A.
,
Adel
H.
,
Baig
M.S.
and
Adil
S.O.
(
2019
)
Diagnostic accuracy of Xpert MTB/RIF in sputum smear-negative pulmonary tuberculosis
.
Cureus
11
,
e5391
50.
Rice
J.P.
and
Seifert
M.
(
2017
)
Performance of the Xpert MTB/RIF assay for the diagnosis of pulmonary tuberculosis and rifampin resistance in a low-incidence, high-resource setting
.
PLoS One
12
,
e0186139
51.
Sahle
E.T.
,
Blumenthal
J.
,
Jain
S.
,
Sun
S.
,
Young
J.
,
Manyazewal
T.
et al.
(
2019
)
Bacteriologically-confirmed pulmonary tuberculosis in an Ethiopian prison: prevalence from screening of entrant and resident prisoners
.
PLoS ONE
14
,
e0226160
52.
Yeong
C.
,
Byrne
A.L.
,
Cho
J.-G.
,
Sintchenko
V.
,
Crighton
T.
and
Marais
B.J.
(
2020
)
Use of GeneXpert MTB/RIF on a single pooled sputum specimen to exclude pulmonary tuberculosis among hospital inpatients placed in respiratory isolation
.
Int. J. Infect. Dis.
92
,
175
180
[PubMed]
53.
Acuna-Villaorduna
C.
,
Orikiriza
P.
,
Nyehangane
D.
,
White
L.F.
,
Mwanga-Amumpaire
J.
,
Kim
S.
et al.
(
2017
)
Effect of previous treatment and sputum quality on diagnostic accuracy of Xpert((R)) MTB/RIF
.
Int. J. Tuberc. Lung Dis.
21
,
389
397
54.
Mishra
H.
,
Reeve
B.W.P.
,
Palmer
Z.
,
Caldwell
J.
,
Dolby
T.
,
Naidoo
C.C.
et al.
(
2020
)
Xpert MTB/RIF Ultra and Xpert MTB/RIF for diagnosis of tuberculosis in an HIV-endemic setting with a high burden of previous tuberculosis: a two-cohort diagnostic accuracy study
.
Lancet Respir. Med.
8
,
368
382
[PubMed]
55.
O’Grady
J.
,
Bates
M.
,
Chilukutu
L.
,
Mzyece
J.
,
Cheelo
B.
,
Chilufya
M.
et al.
(
2012
)
Evaluation of the Xpert MTB/RIF assay at a tertiary care referral hospital in a setting where tuberculosis and HIV infection are highly endemic
.
Clin. Infect. Dis.
55
,
1171
1178
56.
Sohn
H.
,
Aero
A.D.
,
Menzies
D.
,
Behr
M.
,
Schwartzman
K.
,
Alvarez
G.G.
et al.
(
2014
)
Xpert MTB/RIF testing in a low tuberculosis incidence, high-resource setting: limitations in accuracy and clinical impact
.
Clin. Infect. Dis.
58
,
970
976
57.
Bates
M.
,
O’Grady
J.
,
Maeurer
M.
,
Tembo
J.
,
Chilukutu
L.
,
Chabala
C.
et al.
(
2013
)
Assessment of the Xpert MTB/RIF assay for diagnosis of tuberculosis with gastric lavage aspirates in children in sub-Saharan Africa: a prospective descriptive study
.
Lancet Infect. Dis.
13
,
36
42
[PubMed]
58.
Reither
K.
,
Manyama
C.
,
Clowes
P.
,
Rachow
A.
,
Mapamba
D.
,
Steiner
A.
et al.
(
2015
)
Xpert MTB/RIF assay for diagnosis of pulmonary tuberculosis in children: a prospective, multi-centre evaluation
.
J. Infect.
70
,
392
399
[PubMed]
59.
Das
A.
,
Anupurba
S.
,
Mishra
O.P.
,
Banerjee
T.
and
Tripathi
R.
(
2019
)
Evaluation of Xpert MTB/RIF assay for diagnosis of tuberculosis in children
.
J. Trop. Pediatr.
65
,
14
20
[PubMed]
60.
Hasan
Z.
,
Shakoor
S.
,
Arif
F.
,
Mehnaz
A.
,
Akber
A.
,
Haider
M.
et al.
(
2017
)
Evaluation of Xpert MTB/RIF testing for rapid diagnosis of childhood pulmonary tuberculosis in children by Xpert MTB/RIF testing of stool samples in a low resource setting
.
BMC Res. Notes
10
,
473
[PubMed]
61.
Myo
K.
,
Zaw
M.
,
Swe
T.L.
,
Kyaw
Y.Y.
,
Thwin
T.
,
Myo
T.T.
et al.
(
2018
)
Evaluation of Xpert (R) MTB/RIF assay as a diagnostic test for pulmonary tuberculosis in children in Myanmar
.
Int. J. Tuberc. Lung Dis.
22
,
1051
[PubMed]
62.
Pang
Y.
,
Wang
Y.
,
Zhao
S.
,
Liu
J.
,
Zhao
Y.
and
Li
H.
(
2014
)
Evaluation of the Xpert MTB/RIF assay in gastric lavage aspirates for diagnosis of smear-negative childhood pulmonary tuberculosis
.
Pediatr. Infect. Dis. J.
33
,
1047
1051
[PubMed]
63.
Sharma
S.
,
Shulania
A.
,
Achra
A.
,
Jeram
H.
,
Kansra
S.
and
Duggal
N.
(
2020
)
Diagnosis of pulmonary tuberculosis from gastric aspirate samples in nonexpectorating pediatric patients in a tertiary care hospital
.
Indian J. Pathol. Microbiol.
63
,
210
213
[PubMed]
64.
LaCourse
S.M.
,
Chester
F.M.
,
Preidis
G.
,
McCrary
L.M.
,
Arscott-Mills
T.
,
Maliwichi
M.
et al.
(
2014
)
Use of Xpert for the diagnosis of pulmonary tuberculosis in severely malnourished hospitalized Malawian children
.
Pediatr. Infect. Dis. J.
33
,
1200
1202
[PubMed]
65.
Nicol
M.P.
,
Workman
L.
,
Isaacs
W.
,
Munro
J.
,
Black
F.
,
Eley
B.
et al.
(
2011
)
Accuracy of the Xpert MTB/RIF test for the diagnosis of pulmonary tuberculosis in children admitted to hospital in Cape Town, South Africa: a descriptive study
.
Lancet Infect. Dis.
11
,
819
824
[PubMed]
66.
Nicol
M.P.
,
Spiers
K.
,
Workman
L.
,
Isaacs
W.
,
Munro
J.
,
Black
F.
et al.
(
2013
)
Xpert MTB/RIF testing of stool samples for the diagnosis of pulmonary tuberculosis in children
.
Clin. Infect. Dis.
57
,
e18
e21
67.
Nicol
M.P.
,
Workman
L.
,
Prins
M.
,
Bateman
L.
,
Ghebrekristos
Y.
,
Mbhele
S.
et al.
(
2018
)
Accuracy of Xpert Mtb/Rif ultra for the diagnosis of pulmonary tuberculosis in children
.
Pediatr. Infect. Dis. J.
37
,
E261
E263
[PubMed]
68.
Sekadde
M.P.
,
Wobudeya
E.
,
Joloba
M.L.
,
Ssengooba
W.
,
Kisembo
H.
,
Bakeera-Kitaka
S.
et al.
(
2013
)
Evaluation of the Xpert MTB/RIF test for the diagnosis of childhood pulmonary tuberculosis in Uganda: a cross-sectional diagnostic study
.
BMC Infect. Dis.
13
,
133
[PubMed]
69.
Togun
T.O.
,
Egere
U.
,
Sillah
A.K.
,
Ayorinde
A.
,
Mendy
F.
,
Tientcheu
L.
et al.
(
2015
)
Contribution of Xpert(R) MTB/RIF to the diagnosis of pulmonary tuberculosis among TB-exposed children in The Gambia
.
Int. J. Tuberc. Lung Dis.
19
,
1091
1097
,
i-ii
70.
Zar
H.J.
,
Workman
L.
,
Isaacs
W.
,
Munro
J.
,
Black
F.
,
Eley
B.
et al.
(
2012
)
Rapid molecular diagnosis of pulmonary tuberculosis in children using nasopharyngeal specimens
.
Clin. Infect. Dis.
55
,
1088
1095
71.
Zar
H.J.
,
Workman
L.
,
Isaacs
W.
,
Dheda
K.
,
Zemanay
W.
and
Nicol
M.P.
(
2013
)
Rapid diagnosis of pulmonary tuberculosis in African children in a primary care setting by use of Xpert MTB/RIF on respiratory specimens: a prospective study
.
Lancet Global Health
1
,
e97
e104
[PubMed]
72.
Jo
Y.S.
,
Park
J.-H.
,
Lee
J.K.
,
Heo
E.Y.
,
Chung
H.S.
and
Kim
D.K.
(
2016
)
Discordance between MTB/RIF and real-time tuberculosis-specific polymerase chain reaction assay in bronchial washing specimen and its clinical implications
.
PLoS ONE
11
,
e0164923
73.
Steingart
K.R.
,
Ng
V.
,
Henry
M.
,
Hopewell
P.C.
,
Ramsay
A.
,
Cunningham
J.
et al.
(
2006
)
Sputum processing methods to improve the sensitivity of smear microscopy for tuberculosis: a systematic review
.
Lancet Infect. Dis.
6
,
664
674
[PubMed]
74.
Hoft
D.F.
,
Brown
R.M.
and
Belshe
R.B.
(
2000
)
Mucosal bacille calmette-Guerin vaccination of humans inhibits delayed-type hypersensitivity to purified protein derivative but induces mycobacteria-specific interferon-gamma responses
.
Clin. Infect. Dis.
30
,
S217
S222
[PubMed]
75.
Albert
H.
,
Nathavitharana
R.R.
,
Isaacs
C.
,
Pai
M.
,
Denkinger
C.M.
and
Boehme
C.C.
(
2016
)
Development, roll-out and impact of Xpert MTB/RIF for tuberculosis: what lessons have we learnt and how can we do better?
Eur. Respir. J.
48
,
516
525
[PubMed]
76.
Dorman
S.E.
,
Schumacher
S.G.
,
Alland
D.
,
Nabeta
P.
,
Armstrong
D.T.
,
King
B.
et al.
(
2018
)
Xpert MTB/RIF Ultra for detection of Mycobacterium tuberculosis and rifampicin resistance: a prospective multicentre diagnostic accuracy study
.
Lancet Infect. Dis.
18
,
76
84
[PubMed]
77.
Detjen
A.K.
,
DiNardo
A.R.
,
Leyden
J.
,
Steingart
K.R.
,
Menzies
D.
,
Schiller
I.
et al.
(
2015
)
Xpert MTB/RIF assay for the diagnosis of pulmonary tuberculosis in children: a systematic review and meta-analysis
.
Lancet Respir. Med.
3
,
451
461
[PubMed]
78.
Chakravorty
S.
,
Simmons
A.M.
,
Rowneki
M.
,
Parmar
H.
,
Cao
Y.
,
Ryan
J.
et al.
(
2017
)
The New Xpert MTB/RIF Ultra: improving detection of Mycobacterium tuberculosis and resistance to rifampin in an assay suitable for point-of-care testing
.
mBio
8
,
e00812
17
[PubMed]
79.
Osei Sekyere
J.
,
Maphalala
N.
,
Malinga
L.A.
,
Mbelle
N.M.
and
Maningi
N.E.
(
2019
)
A comparative evaluation of the new genexpert MTB/RIF ultra and other rapid diagnostic assays for detecting tuberculosis in pulmonary and extra pulmonary specimens
.
Sci. Rep.
9
,
16587
[PubMed]
80.
Meldau
R.
,
Randall
P.
,
Pooran
A.
,
Limberis
J.
,
Makambwa
E.
,
Dhansay
M.
et al.
(
2019
)
Same-day tools, including Xpert Ultra and IRISA-TB, for rapid diagnosis of pleural tuberculosis: a prospective observational study
.
J. Clin. Microbiol.
57
,
[PubMed]
81.
Vashistha
R.
,
Somani
K.
,
Patel
A.
,
Maheshwari
M.
and
Datey
S.
(
2018
)
Clinical profile of abdominal tuberculosis in a tertiary care centre of central india-a descriptive observational study
.
J. Evol. Med. Dental Sci.
7
,
3699
3702
82.
Briceno
R.K.
,
Sergent
S.R.
,
Benites
S.M.
and
Alocilja
E.C.
(
2019
)
Nanoparticle-based biosensing assay for universally accessible low-cost TB detection with comparable sensitivity as culture
.
Diagnostics
9
,
222
[PubMed]
83.
Blau
H.
,
Linnane
B.
,
Carzino
R.
,
Tannenbaum
E.L.
,
Skoric
B.
,
Robinson
P.J.
et al.
(
2014
)
Induced sputum compared to bronchoalveolar lavage in young, non-expectorating cystic fibrosis children
.
J. Cyst. Fibros.
13
,
106
110
[PubMed]
84.
Garcia
B.J.
,
Loxton
A.G.
,
Dolganov
G.M.
,
Van
T.T.
,
Davis
J.L.
,
de Jong
B.C.
et al.
(
2016
)
Sputum is a surrogate for bronchoalveolar lavage for monitoring Mycobacterium tuberculosis transcriptional profiles in TB patients
.
Tuberculosis
100
,
89
94
[PubMed]
85.
Conde
M.B.
,
Soares
S.L.
,
Mello
F.C.
,
Rezende
V.M.
,
Almeida
L.L.
,
Reingold
A.L.
et al.
(
2000
)
Comparison of sputum induction with fiberoptic bronchoscopy in the diagnosis of tuberculosis: experience at an acquired immune deficiency syndrome reference center in Rio de Janeiro, Brazil
.
Am. J. Respir. Crit. Care Med.
162
,
2238
2240
[PubMed]
86.
Ruiz Jimenez
M.
,
Guillen Martin
S.
,
Prieto Tato
L.M.
,
Cacho Calvo
J.B.
,
Alvarez Garcia
A.
,
Soto Sanchez
B.
et al.
(
2013
)
Induced sputum versus gastric lavage for the diagnosis of pulmonary tuberculosis in children
.
BMC Infect. Dis.
13
,
222
[PubMed]
87.
Zar
H.J.
,
Hanslo
D.
,
Apolles
P.
,
Swingler
G.
and
Hussey
G.
(
2005
)
Induced sputum versus gastric lavage for microbiological confirmation of pulmonary tuberculosis in infants and young children: a prospective study
.
Lancet
365
,
130
134
[PubMed]
88.
Parashar
D.
,
Kabra
S.K.
,
Lodha
R.
,
Singh
V.
,
Mukherjee
A.
,
Arya
T.
et al.
(
2013
)
Does neutralization of gastric aspirates from children with suspected intrathoracic tuberculosis affect mycobacterial yields on MGIT culture?
J. Clin. Microbiol.
51
,
1753
1756
[PubMed]
89.
Mathew
J.L.
,
Vijayasekharan
D.
and
Singh
S.
(
2014
)
Is Xpert MTB/RIF assay in gastric lavage aspirate useful for diagnosis of smear-negative childhood pulmonary tuberculosis?
Indian Pediatr.
51
,
1007
1011
[PubMed]
90.
Ramirez
P.
,
Valencia
M.
and
Torres
A.
(
2007
)
Bronchoalveolar lavage to diagnose respiratory infections
.
Semin Respir. Crit. Care Med.
28
,
525
533
[PubMed]
91.
Zhang
G.P.
,
Chen
Q.
,
Liu
J.M.
,
Zhou
S.P.
,
Yu
X.J.
,
Lu
J.
et al.
(
2012
)
Application of bacterial cultures of bronchoalveolar lavage fluids in children with pulmonary infection
.
Zhongguo Dang Dai Er Ke Za Zhi
14
,
350
352
[PubMed]
92.
Singer-Leshinsky
S.
(
2016
)
Pulmonary tuberculosis: improving diagnosis and management
.
JAAPA
29
,
20
25
[PubMed]
93.
Connell
T.G.
,
Zar
H.J.
and
Nicol
M.P.
(
2011
)
Advances in the diagnosis of pulmonary tuberculosis in HIV-infected and HIV-uninfected children
.
J. Infect. Dis.
204
,
S1151
S1158
[PubMed]

Author notes

*

These authors contributed equally to this work.

This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

Supplementary data