Skip to main content
Download PDF

Diagnostic accuracy of nucleic acid amplification tests (NAATs) in urine for genitourinary tuberculosis: a systematic review and meta-analysis

BMC Infectious Diseases volume 17, Article number: 390 (2017) Cite this article

Abstract

Background

Genitourinary tuberculosis is the third most common form of extrapulmonary tuberculosis. Diagnosis is difficult because of unspecific clinical manifestations and low accuracy of conventional tests. Unfortunately, the delayed diagnosis impacts the urinary tract severely. Nucleic acid amplification tests yield fast results, and among these, new technologies can also detect drug resistance. There is lack of consensus regarding the use of these tests in genitourinary tuberculosis; we therefore aimed to assess the accuracy of nucleic acid amplification tests in the diagnosis of genitourinary tuberculosis and to evaluate the heterogeneity between studies.

Methods

We did a systematic review and meta-analysis of research articles comparing the accuracy of a reference standard and a nucleic acid amplification test for diagnosis of urinary tract tuberculosis. We searched Medline, EMBASE, Web of Science, LILACS, Cochrane Library, and Scopus for articles published between Jan 1, 1990, and Apr 14, 2016. Two investigators identified eligible articles and extracted data for individual study sites. We analyzed data in groups with the same index test. Then, we generated pooled summary estimates (95% CIs) for sensitivity and specificity by use of random-effects meta-analysis when studies were not heterogeneous.

Results

We identified eleven relevant studies from ten articles, giving information on PCR, LCR and Xpert MTB/RIF tests. All PCR studies were “in-house” tests, with different gene targets and had several quality concerns therefore we did not proceed with a pooled analysis. Only one study used LCR. Xpert studies were of good quality and not heterogeneous, pooled sensitivity was 0·87 (0·66–0·96) and specificity was 0·91 (0·84–0·95).

Conclusion

PCR studies were highly heterogeneous. Among Xpert MTB/RIF studies, specificity was favorable with an acceptable confidence interval, however new studies can update meta-analysis and get more precise estimates. Further high-quality studies are urgently needed to improve diagnosis of genitourinary tuberculosis.

Protocol registration

PROSPERO CRD42016039020.

Peer Review reports

Background

Tuberculosis (TB) is still one of the world’s biggest threats to public health. In 2014, TB killed 1.5 million people and was the single infectious disease leading cause of death worldwide [1]. The World Health Organization (WHO) has estimated that only in 2014 there were 9·6 million new TB cases, 12% of which were HIV-positive [1].

Extrapulmonary TB (EPTB) accounts for approximately 10 to 20% of all the TB affected population. Genitourinary TB (GUTB) is the third most common location of EPTB, after pleural and lymph node involvement [2]. In addition, concomitant GUTB and pulmonary TB is found in 2–10% and in 15–20% of patients in developed and developing countries, respectively [2].

GUTB is defined as the infection by Mycobacterium tuberculosis of the urinary tract, the male genitalia or the female genitalia, however, most authors refer to GUTB for reporting only the first [3,4,5]. The infection occurs in most cases after long periods of latent infection followed by hematogenous spread to the kidneys, epididymis or fallopian tubes; prostate seeding has also been reported but is extremely rare. Other genitourinary organs are affected by local spread [5,6,7].

Clinical manifestations of GUTB are non-specific, depending on the organs affected and the severity of their involvement, and can mimic several urologic and gynecologic diseases [8]. Diagnosing GUTB is difficult and often overlooked. Conventional diagnostic tests include urine microscopy and culture. Since GUTB is usually paucibacillary [9], most samples evaluated by microscopy are negative. Although culture is the gold standard for diagnosing GUTB, currently available culture methods, including both solid and liquid platforms are insensitive for diagnosing GUTB, and show a number of disadvantages including long-turn around times, high contamination rates and cost [10].

Furthermore, delayed diagnosis may result in worse outcomes, such as infertility, non-functioning unilateral kidney, contracted bladder and even renal failure [8]. Case series reported that 26·9% of patients on average had a non-functioning unilateral kidney, 7.4%, renal failure and 10% had contracted bladder at the time of diagnosis of genitourinary tuberculosis [2, 4].

Nucleic acid amplification tests (NAATs), have been developed to overcome the limitations of conventional tests in TB [11,12,13,14,15]. These can yield results in between 2 to 48 h [16] NAATs can be classified as commercial or in-house (“home-brew”) tests [16] They can be classified, as well, by their mechanism: polymerase chain reaction (PCR) tests, ligase chain reaction(LCR) tests and more recent technologies (variants of PCR) such as Xpert MTB/RIF and Genotype® MTBDRplus [17, 18].

While the use of NAAT has been well studied in pulmonary TB, their use in GUTB lacks enough high-quality evidence [11, 19, 20]. Therefore, we conducted this systematic review to determine the accuracy of NAAT performed in urine for diagnosing GUTB and to identify the factors related to heterogeneity between studies of the result.

Methods

We developed our study in accordance with the Cochrane’s group guidelines and methods for systematic reviews and meta-analyses of diagnostic tests [21]. The protocol was registered at PROSPERO database with the number CRD42016039020.

Search strategy and selection criteria

We searched Medline, EMBASE, Scopus, Lilacs, Cochrane Library and Web of Science for studies in all languages published from January 1, 1990, to April 14, 2016 (see Additional file 1: Appendix S1 for search terms).

We included all the studies that compared a NAAT in urine for diagnosis of GUTB with the gold standard. These studies should have presented data in the form of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) In case insufficient data were reported, authors were contacted via email to provide additional information regarding their published reports.

We included studies that used a microbiological or a broad standard reference as gold standard. We define the microbiological standard reference as a positive mycobacterial culture on solid or liquid media for one or more specimens from each patient. We define a broad standard reference as either a microbiological standard reference or clinical manifestations of the disease with clinical response after appropriate treatment. We excluded 3 studies that showed results fewer than ten patients, as such results are at high risk of bias. A total of 13 patients were excluded using this criterion. We also excluded studies that had a case-control design, as these may lead to overestimation of accuracy in diagnostic studies.

Two reviewers (CA and VO) independently screened the collected citations for relevance and revised full-text articles with pre-specified eligibility criteria; disagreements about study selection were resolved by consultation with a third reviewer (CU-G). The results of the literature search are presented in a flowchart following the PRISMA guidelines (Fig. 1) [22].

Fig. 1
figure 1

Selection of studies reporting on the use of a NAAT for GUTB diagnosis in urine

Full size image

Data extraction and quality appraisal

CA and VO extracted data and resolved disagreements by consensus (Additional file 1). We assessed study quality with the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2) [23]. QUADAS-2 consists of four domains: patient selection, index test, reference standard, and flow and timing. We assessed all domains for the potential for risk of bias and the first three domains for concerns regarding applicability. We used questions, called signaling questions, for each domain to form judgments about the risk of bias. As recommended, we first developed guidance on how to evaluate each signaling question and interpret this information tailored to this review. Then, one review author (CA) piloted the tool with four of the included studies. Based on experience gained from the pilot study, we finalized the tool (see Additional file 1: Appendix S2 for quality appraisal). Two authors (CA and VO) independently assessed the methodological quality of the included studies with the finalized tool; disagreements about methodological quality were resolved by consultation with a third reviewer (CU-G).

Statistical analysis

We used standard methods recommended for meta-analyses of diagnostic studies [21] Analyses were done using the R statistical programming language version 3 [24, 25].

To minimize projected heterogeneity, we decided a priori to analyze data in certain subgroups: studies using PCR, studies using LCR and studies using Xpert MTB/RIF. For each subgroup, we assessed heterogeneity visually with forest plots and summary receiver-operating characteristic (SROC) curves with 95% prediction regions, and statistically with χ2 statistics. Regarding the forest plots, we used a statistic continuity correction of 5% applied to the reported sensitivity and specificity. This correction affected studies with large confidence intervals and extreme values, we then used these results for further analysis as recommended for meta-analyses of diagnostic studies [21]. We generated pooled summary estimates and differences for sensitivity and specificity with 95% confidence intervals by using a bivariate summary ROC curve [26]. It is assumed that sensitivity and specificity vary across studies because of differences in study populations, sampling errors, and differences in implicit thresholds applied to the data to separate patients. Thus, a random-effect model was applied to account for between-study heterogeneity.

Results

We identified 386 citations, of which 54 were potentially relevant based on the title and abstract (Fig. 1). After full-text review, ten articles were included in our analysis, providing data of eleven studies addressing different NAATs.

Study characteristics

Of the eleven studies selected, eight studies used PCR, one study used LCR and, two studies used Xpert MTB/RIF. All of the PCR studies were “in-house” tests. The average size of each study was 248 (specimens or subjects), with a range of 20 to 1000. Table 1 outlines the main characteristics of these studies.

Table 1 Description of studies in the systematic review and reported measures of test accuracy
Full size table

Overall, nine studies were done in middle-income countries were TB burden is high. Only one study collected data retrospectively. Furthermore, a microbiological reference standard was used in eight studies, one used a broad reference standard and the other two used different combinations of clinical manifestations and culture.

Each of the studies that used PCR had a unique protocol for running the test. Six studies used a single PCR technique and two used a nested PCR technique; additionally, the gene target and primers were also different in each one.

Quality appraisal

Figure 2 shows the results of the quality evaluation using the QUADAS-2 tool; 64% of the studies, all of which used PCR, were considered to be at high risk of bias in the index test and reference standard domains (see Additional file 1: Appendix S2 and Table S1 for detailed information). Within the index test and reference standard domain, studies were considered to be at high risk of bias because they did not blind operators to results of the reference standard or index test respectively, furthermore the protocols used in the “in-house” NAATs were not standardized and variations in technical aspects could have introduced bias.

Fig. 2
figure 2

Summary of Quality Assessment tool for Diagnostic Accuracy tests (QUADAS-2). The point estimates of sensitivity and specificity from each study are shown as solid circles. Error bars are 95% confidence intervals. A statistical continuity correction of 5% has been applied

Full size image

For this latter reason, all the PCR studies were also considered as having a high concern of applicability in the index test domain. Additionally, 3 studies were considered as high concern of applicability in the reference standard domain because they used other than microbiological or broad reference standard.

Summary measures

Figure 3 shows the diagnostic accuracy of the studies distributed in three groups, PCR, LCR and Xpert MTB/RIF with a calculated confidence interval (CI) of 95% and a statistic continuity correction of 5%.

Fig. 3
figure 3

Forest plots of the diagnostic accuracy of PCR, LCR and Xpert MTB/RIF for the diagnosis of GUTB. Each triangle represents a study in the meta-analysis and the circle the summary estimate. The light line is the confidence interval

Full size image

Within the studies that used PCR there was a wide heterogeneity by assessing the forest plot (Fig. 3). Because the low study quality of these studies, the different protocols and variants of PCR used, and the visual heterogeneity assessed in the forest plots, we decided not to proceed with a summary measure. Regarding LCR, only one study addressed this test.

The two studies that used Xpert MTB/RIF showed good quality, and heterogeneity was further assessed by χ2 test. Table 2 shows the summary measures of these studies. We also plotted a SROC summary curve (Fig. 4). The summary sensitivity and specificity was 0·87 (0·66–0·96) and 0·91 (0·84–0·95) respectively.

Table 2 Summary measures of test accuracy of Xpert MTB/RIF studies
Full size table
Fig. 4
figure 4

Summary Receiver-Operating Characteristic (SROC) curves for Xpert MTB/RIF assays

Full size image

Discussion

Our systematic review indicates that Xpert MTB/RIF performance in urine samples for GUTB diagnosis showed sensitivity and specificity rates ranging from 0·83 to 0·95 and 0·79 to 0·99, respectively. While these results show acceptable diagnostic accuracy, a proper interpretation of results should take into account the wide confidence intervals of the outcome variables, several sources of heterogeneity, such as quality issues, different target genes, different protocols, and different number of specimens analyzed. It should be noted that Xpert MTB/RIF is a WHO recommended initial diagnostic test for all patients with sign and symptoms of TB [10].

Within the diverse protocols followed for PCR tests, possible sources of false positive and negative results are contamination and inhibition respectively. Contamination is usually caused by amplification products that get aerosolized and interfere with subsequent assays, this is especially true for nested PCR methods, in which repeated amplifications are done [27]. If not controlled, in a relatively short time, laboratory reagents, equipment, and even ventilation systems get contaminated with these products [27]. Metabolites, drugs, and other body fluid substances may produce inhibition of PCR; in urine, the most critical inhibitor is urea, which can lead to polymerase degradation in a concentration-dependent manner [28]. While doing PCR demands a controlled protocol to be followed carefully to avoid both inhibitors and contaminants, Xpert MTB/RIF does not face the same problem [29].

The pooled estimates of Xpert MTB/RIF studies in our analysis, showed satisfactory sensitivity and specificity: 0·87 and 0·91 respectively. However, only specificity had a narrow CI. Clinically it can be translated into the test correctly reporting 91% of patients without the disease as true negative. Because this meta-analysis only comprised two studies, further research is required to upgrade and get more precise pooled estimates. Additionally, positive predictive values (PPV) and negative predictive values (NPV) which depend on the prevalence of the disease should be reported for different clinical settings as they have more practical utility.

Information of drug resistance among patients with GUTB is lacking [1, 2, 30, 31]. In this context, none of the Xpert MTB/RIF studies analyzed rifampicin resistance, possibly because they were done in high-income countries, where multi-drug resistant (MDR) tuberculosis is not usually a concern. One study in China used a different test, GenoType® MTBDRplus, which evaluates isoniazid and rifampicin resistance, in an outpatient setting [32]. They found that one-third of their patients had drug-resistant GUTB, and one-fourth had MDR-GUTB. Subsequent Xpert MTB/RIF or GenoType® MTBDRplus studies must help characterize drug resistance epidemiology while evaluating the clinical utility of these tests in different MDR prevalence settings.

We identified two main limitations on studies, the reference standard and the number of specimens analyzed were not the same for all, and they did not report if test accuracy changed in patients with comorbidities.

In the studies that used positive urine culture as reference standard, there is chance that true positives cases with only clinical response after treatment were not detected but tagged as false positives; rendering lower specificity. Additionally, some studies analyzed more than one specimen per patient. While there is a consensus based on an expert opinion for using three consecutive morning samples for culture or NAATs, there is no study that addresses this particular issue [33] Increasing the number of specimens can increase sensitivity at the cost of lowering specificity. In addition,it is still unknown if this strategy is cost-effective.

Comorbidities within the populations studied were not reported in any of the articles selected. We were especially concerned about HIV patients, as it has been shown that accuracy of diagnostic tests varies in this population. In HIV-infected patients, those presenting with pulmonary TB typically have a paucibacillary disease [34, 35]. However, in tuberculous meningitis, two studies reported higher Xpert MTB/RIF accuracy in those patients infected with HIV [36, 37]. We suggest that authors consider HIV-status in future research in GUTB patients.

Strengths and weaknesses of the study

We identified some weaknesses in our review. We could not assess male and female genital tuberculosis through this review because they lack a clear reference standard and thus, there would be a high concern of bias in the analysis [20]. We did not evaluate the effect of adding NAATs to other tests, and the net effect of NAATs on positive predictive values or negative predictive values since these depend on the prevalence of the disease. Furthermore, we could not explore the effect of issues such as expertise with NAATs equipment, and laboratory infrastructure because of poor reporting.

Our study has several strengths: an exhaustive search strategy (including conference abstracts, and no language restriction), with more than 2000 patients evaluated and a deep critical quality appraisal, exploring heterogeneity in accordance with published guidelines, and following PRISMA recommendations for systematic reviews [22].

Limitations

The lack of quality reporting in diagnostic interferes with critical appraisal and replication of studies [38]. We encourage authors to follow the Standards for Reporting Diagnostic Accuracy (STARD) in order to achieve transparency, completeness and excellence of reporting [39]. The STARD was updated in 2015 [40], and comprises a list of 30 essential items for high-quality diagnostic accuracy studies; it has also incorporated evidence about sources of bias and variability in diagnostic accuracy.

Conclusion

GUTB is an overlooked disease, an unspecific clinical picture and limited conventional tests account for this problem. Diagnosis is difficult and frequently delayed, leading to major impact on the urinary tract system.

All PCR studies were highly heterogeneous as they involved different gene targets, different protocols and had several quality concern issues. Consequently, no meta-analysis could be done. Xpert MTB/RIF studies, in contrast, were high quality reported and were not heterogeneous. Our analysis showed a favorable specificity. Studies with high-quality reporting are urgently needed to improve diagnosis of GUTB.

Abbreviations

EPTB:

Extrapulmonary tuberculosis

GUTB:

Genitourinary tuberculosis

LCR:

Ligase chain reaction

MDR:

Multi-drug resistant

NAAT:

Nucleic acid amplification test

NPV:

Negative predictive values

PCR:

Polymerase chain reaction

PPV:

Positive predictive values

QUADAS-2:

Quality Assessment Tool for Diagnostic Accuracy Studies

SROC:

Summary receiver-operating curve

STARD:

Standards for Reporting Diagnostic Accuracy

TB:

Tuberculosis

UGTB:

Urogenital tuberculosis

WHO:

World Health Organization

References

  1. World Health Organization (WHO). Global Tuberculosis Report. Ginebra: WHO. 2015:2015.

  2. Figueiredo AA, Lucon AM, Junior RF, Srougi M. Epidemiology of urogenital tuberculosis worldwide. Int J Urol. 2008;15:827–32.

    Article  PubMed  Google Scholar 

  3. Kulchavenya E. Urogenital tuberculosis: definition and classification. Ther Adv Infect Dis. 2014;2:117–22.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Figueiredo AA, Lucon AM. Urogenital tuberculosis: update and review of 8961 cases from the world literature. Rev Urol. 2008;10:207–17.

    PubMed  PubMed Central  Google Scholar 

  5. Molina RL, Diouf K, Nour NM. Tuberculosis and the obstetrician-gynecologist: a global perspective. Rev Obstet Gynecol. 2013;6:174–81.

    PubMed  PubMed Central  Google Scholar 

  6. Abbara A, Davidson RN. Etiology and management of genitourinary tuberculosis. Nat Rev Urol. 2011;8:678–88.

    Article  CAS  PubMed  Google Scholar 

  7. Bultitude MF. Campbell-Walsh Urology tenth edition. BJU Int. 2012;109:E10.

    Article  Google Scholar 

  8. Kapoor R, Ansari MS, Mandhani A, Gulia A. Clinical presentation and diagnostic approach in cases of genitourinary tuberculosis. Indian J. Urol. 2008;24:401–5.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Tostmann A, Kik SV, Kalisvaart NA, Sebek MM, Verver S, Boeree MJ, et al. Tuberculosis transmission by patients with smear-negative pulmonary tuberculosis in a large cohort in the Netherlands. Clin. Infect. Dis. 2008;47:1135–42.

    Article  PubMed  Google Scholar 

  10. World Health Organization. Policy framework for implementing new tuberculosis diagnostics. Geneva: World Health Organization; 2011.

    Google Scholar 

  11. Ling DI, Flores LL, Riley LW, Pai M. Commercial nucleic-acid amplification tests for diagnosis of pulmonary tuberculosis in respiratory specimens: meta-analysis and meta-regression. PLoS One. 2008;3:e1536.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Adelman MW, Kurbatova E, Wang YF, Leonard MK, White N, McFarland DA, et al. Cost analysis of a nucleic acid amplification test in the diagnosis of pulmonary tuberculosis at an urban hospital with a high prevalence of TB/HIV. PLoS One. 2014;9:e100649.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kobayashi M, Ray SM, Hanfelt J, Wang YF. Diagnosis of tuberculosis by using a nucleic acid amplification test in an urban population with high HIV prevalence in the United States. PLoS One. 2014;9:e107552.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Taegtmeyer M, Beeching NJ, Scott J, Seddon K, Jamieson S, Squire SB, et al. The clinical impact of nucleic acid amplification tests on the diagnosis and management of tuberculosis in a British hospital. Thorax. 2008;63:317–21.

    Article  CAS  PubMed  Google Scholar 

  15. Marks SM, Cronin W, Venkatappa T, Maltas G, Chon S, Sharnprapai S, et al. The health-system benefits and cost-effectiveness of using Mycobacterium tuberculosis direct nucleic acid amplification testing to diagnose tuberculosis disease in the United States. Clin Infect Dis Off Publ Infect Dis Soc Am. 2013;57:532–42.

    Article  Google Scholar 

  16. Centers for Disease Control and Prevention (CDC). Updated guidelines for the use of nucleic acid amplification tests in the diagnosis of tuberculosis. MMWR Morb. Mortal. Wkly. Rep. 2009;58:7–10.

  17. Celik C, Gozel MG, Bakici MZ, Berk S, Ozsahin SL, Gulturk E. Applicability of Xpert MTB/RIF assay for routine diagnosis of tuberculosis: a four-year single-center experience. Turk J Med Sci. 2015;45:1329–34.

    Article  CAS  PubMed  Google Scholar 

  18. Theron G, Peter J, Richardson M, et al. The diagnostic accuracy of the GenoType® Mtbdrsl assay for the detection of resistance to second-line anti-tuberculosis drugs. Cochrane Database Syst Rev. 2014;10:4–123.

    Google Scholar 

  19. Shah HN, Badlani GH. Genitourinary tuberculosis. An Update Curr Bladder Dysfunct Rep. 2013;8:186–96.

    Article  Google Scholar 

  20. Bose M. Female genital tract tuberculosis: how long will it elude diagnosis? Indian J Med Res. 2011;134:13–4.

    PubMed  PubMed Central  Google Scholar 

  21. Macaskill P, Gatsonis C, Deeks JJ, Harbord RM, Takwoingi Y. Analysing and presenting results. In: Deeks J, Bossuyt P, Gatsonis C, eds. Cochrane handbook for systematic reviews of diagnostic test accuracy. Version 0.9.0. London: The Cochrane Collaboration; 2010.

    Google Scholar 

  22. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155:529–36.

    Article  PubMed  Google Scholar 

  24. Core Team R. R Foundation for Statistical Computing: Vienna, Austria, R: A Language and Environment for Statistical Computing. 2016; Available at http://www.R-project.org

  25. Doebler P. Meta-Analysis of Diagnostic Accuracy with mada. 2012; Available at https://cran.r-project.org/package=mada

  26. Reitsma JB, Glas AS, Rutjes AWS, Scholten RJPM, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58:982–90.

    Article  PubMed  Google Scholar 

  27. Aslanzadeh J, Preventing PCR. Amplification carryover contamination in a clinical laboratory. Ann Clin Lab Sci. 2004;34:389–96.

    CAS  PubMed  Google Scholar 

  28. Schrader C, Schielke A, Ellerbroek L, Johne R. PCR inhibitors – occurrence, properties and removal. J Appl Microbiol. 2012;113:1014–26.

    Article  CAS  PubMed  Google Scholar 

  29. Banada PP, Sivasubramani SK, Blakemore R, Boehme C, Perkins MD, Fennelly K, et al. Containment of bioaerosol infection risk by the Xpert MTB/RIF assay and its applicability to point-of-care settings. J Clin Microbiol. 2010;48:3551–7.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Zajaczkowski T. Genitourinary tuberculosis: historical and basic science review: past and present. Cent Eur J Urol. 2012;65:182–7.

    Article  Google Scholar 

  31. Merchant S, Bharati A, Merchant N. Tuberculosis of the genitourinary system-urinary tract tuberculosis: renal tuberculosis-part I. Indian J Radiol Imaging. 2013;23:46–63.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ye Y, Hu X, Shi Y, Zhou J, Zhou Y, Song X, et al. Clinical Features and Drug-Resistance Profile of Urinary Tuberculosis in South-Western China: a Cross-sectional Study. Medicine (Baltimore). 2016;95:e3537.

    Article  CAS  Google Scholar 

  33. Diagnostic Standards and Classification of Tuberculosis in Adults and Children. This official statement of the American Thoracic Society and the Centers for Disease Control and Prevention was adopted by the ATS Board of Directors, July 1999. This statement was endorsed by the Council of the Infectious Disease Society of America, September 1999. Am J Respir Crit Care Med 2000; 161: 1376–1395.

  34. Swaminathan S, Padmapriyadarsini C, Narendran G. HIV-associated tuberculosis: clinical update. Clin Infect Dis. 2010;50:1377–86.

    Article  CAS  PubMed  Google Scholar 

  35. Steingart KR, Schiller I, Horne DJ, Pai M, Boehme CC, Dendukuri N. Xpert® MTB/RIF assay for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst Rev. 2014;1:CD009593.

    Google Scholar 

  36. Patel VB, Theron G, Lenders L, et al. Diagnostic accuracy of quantitative PCR (Xpert MTB/RIF) for Tuberculous meningitis in a high burden setting: a prospective study. PLoS Med. 2013;10 doi:10.1371/journal.pmed.1001536.

  37. Nhu NTQ, Heemskerk D, Thu DDA, Chau TTH, Mai NTH, Nghia HDT, et al. Evaluation of GeneXpert MTB/RIF for diagnosis of Tuberculous meningitis. J Clin Microbiol. 2014;52:226–33.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Korevaar DA, Enst WA van, Spijker R, Bossuyt PMM, Hooft L. Reporting quality of diagnostic accuracy studies: a systematic review and meta-analysis of investigations on adherence to STARD. Evid Based Med 2014;19:47–54.

  39. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig LM, et al. The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration. Ann Intern Med. 2003;138:W1–12.

    Article  PubMed  Google Scholar 

  40. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015;351:h5527.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Khan F, Cheema F, Khan M. Accuracy of urinary PCR as compared with urine culture for early diagnosis of genitourinary tuberculosis. Pak J Med Health Sci. 2013;7:675–8.

    Google Scholar 

  42. Garcia-Elorriaga G, Gracida-Osorno C, Carrillo-Montes G, Gonzalez-Bonilla C. Clinical usefulness of the nested polymerase chain reaction in the diagnosis of extrapulmonary tuberculosis. Salud Publica Mex. 2009;51:240–5.

    Article  PubMed  Google Scholar 

  43. Khosravi AD, Hashemzadeh M, Ghorbani A, Seghatoleslami S. Comparison of nested-PCR technique and culture method in detection of Mycobacterium tuberculosis from patients suspected to genitourinary tuberculosis. Afr J Biotechnol. 2010;9:2151–5.

    CAS  Google Scholar 

  44. Raghavendran M, Venugopal A. MP24-02 PCR in GENITO urinary tuberculosis. J Urol. 2016;195:e270–1.

    Article  Google Scholar 

  45. Hemal AK, Gupta NP, Rajeev TP, Kumar R, Dar L, Seth P. Polymerase chain reaction in clinically suspected genitourinary tuberculosis: comparison with intravenous urography, bladder biopsy, and urine acid fast bacilli culture. Urology. 2000;56:570–4.

    Article  CAS  PubMed  Google Scholar 

  46. van Vollenhoven P, Heyns CF, de Beer PM, Whitaker P, van Helden PD, Victor T. Polymerase chain reaction in the diagnosis of urinary tract tuberculosis. Urol Res. 1996;24:107–11.

    Article  PubMed  Google Scholar 

  47. Moussa OM, Eraky I, El-Far MA, Osman HG, Ghoneim MA. Rapid diagnosis of genitourinary tuberculosis by polymerase chain reaction and non-radioactive DNA hybridization. J Urol. 2000;164:584–8.

    Article  CAS  PubMed  Google Scholar 

  48. Gamboa F, Dominguez J, Padilla E, Manterola JM, Gazapo E, Lonca J, et al. Rapid diagnosis of extrapulmonary tuberculosis by ligase chain reaction amplification. J Clin Microbiol. 1998;36:1324–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Hillemann D, Rusch-Gerdes S, Boehme C, Richter E. Rapid molecular detection of extrapulmonary tuberculosis by the automated GeneXpert MTB/RIF system. J Clin Microbiol. 2011;49:1202–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Tortoli E, Russo C, Piersimoni C, Mazzola E, Dal Monte P, Pascarella M, et al. Clinical validation of Xpert MTB/RIF for the diagnosis of extrapulmonary tuberculosis. Eur Respir J. 2012;40:442–7.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The team wants to give special thanks to Seungseo Choi, a medical student from Facultad de Medicina Alberto Hurtado at UPCH, for his translation of Korean hits and manuscripts and to Maria Rueda, MD for her meaningful suggestions on the paper.

Funding

This study did not receive any funding. CU-G received support from the Program for Advanced Research Capacities for AIDS in Peru (PARACAS) at Universidad Peruana Cayetano Heredia (D43TW00976301) from Fogarty International Center at the U.S. National Institute of Health (NIH).

Availability of data and materials

The datasets generated during and analysed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Authors’ contributions

CA and CU-G designed the study. CA and VO screened studies for inclusion. CA and CU-G wrote the report and analyzed data. CU-G, VO, CS, MM and LZ critically interpreted data and revised the report. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information

Authors and Affiliations

  1. Facultad de Medicina Alberto Hurtado, Universidad Peruana Cayetano Heredia, Av. Honorio Delgado 430, Urb. Ingeniería, S.M, P, Lima, Perú

    Carlos Altez-Fernandez, Victor Ortiz & Cesar Ugarte-Gil

  2. Department of Urology, Wake Forest University, Winston Salem, NC, USA

    Majid Mirzazadeh

  3. Servicio de Urología, Hospital Nacional Cayetano Heredia, Lima, Perú

    Luis Zegarra

  4. Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru

    Carlos Seas & Cesar Ugarte-Gil

  5. Departamento de Enfermedades Infecciosas, Tropicales y Dermatológicas, Hospital Nacional Cayetano Heredia, Lima, Peru

    Carlos Seas

  6. Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK

    Cesar Ugarte-Gil

  7. Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA

    Cesar Ugarte-Gil

Authors
  1. Carlos Altez-Fernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Victor Ortiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Majid Mirzazadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luis Zegarra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Carlos Seas
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cesar Ugarte-Gil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlos Altez-Fernandez.

Additional file

Additional file 1:

Complete search strategy and details on quality evaluation. (DOCX 18 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Altez-Fernandez, C., Ortiz, V., Mirzazadeh, M. et al. Diagnostic accuracy of nucleic acid amplification tests (NAATs) in urine for genitourinary tuberculosis: a systematic review and meta-analysis. BMC Infect Dis 17, 390 (2017). https://0-doi-org.brum.beds.ac.uk/10.1186/s12879-017-2476-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s12879-017-2476-8

Keywords

BMC Infectious Diseases

ISSN: 1471-2334

Contact us