Abstract
Background
This is a systematic review and meta-analysis of diagnostic test accuracy studies to assess the predictive value of both tuberculin skin test (TST) and interferon-gamma release assays (IGRA) for active tuberculosis (TB) among solid organ transplantation (SOT) recipients.
Methods
Medline, Embase, and the CENTRAL databases were searched from 1946 until June 30, 2022. Two independent assessors extracted data from studies. Sensitivity analyses were performed to investigate the effect of studies with high or low risk of bias. Methodological quality of each publication was assessed using QUADAS-2.
Results
A total of 43 studies (36 403 patients) with patients who were screened for latent TB infection (LTBI) and who underwent SOT were included: 18 were comparative and 25 noncomparative (19 TST, 6 QuantiFERON-TB Gold In-Tube [QFT-GIT]). For IGRA tests taken together, positive predictive value (PPV) and negative predictive value (NPV) were 1.2% and 99.6%, respectively. For TST, PPV was 2.13% and NPV was 95.5%. Overall, PPV is higher when TB burden is higher, regardless of test type, although still low in absolute terms. Incidence of active TB was similar between studies using LTBI prophylaxis (mean incidence 1.22%; 95% confidence interval [CI], .2179–2.221) and those not using prophylaxis (mean incidence 1.045%; 95% CI, 0.2731–1.817; P = .7717). Strengths of this study include the large number of studies available from multiple different countries; limitations include absence of gold standard for diagnosis of latent TB and low incidence of active TB.
Conclusions
We found both TST and IGRA had a low PPV and high NPV for the development of active TB posttransplant. Further studies are needed to better understand how to prevent active TB in the SOT population.
Keywords: IGRA, LTBI, SOT, TST
Screening for LTBI in SOT patients is widely recommended. This meta-analysis looked at the predictive value of TST and IGRA for active TB. We found both have a high negative predictive value but neither has a high positive predictive value.
Patients undergoing solid organ transplantation (SOT) are at a significantly increased risk for developing active tuberculosis (TB), with an incidence ranging from 4 to 30 times that of the general population in intermediate and high TB-burden countries [1]. Posttransplant TB mortality is high, estimated to range between 9% [2] and 30% [3]. Most cases of TB are due to reactivation of latent TB infections (LTBIs), which occurs most frequently within the first year of transplantation. Nevertheless, it has been reported that only one quarter of active TB cases after transplant occurred among patients with a positive tuberculin skin test (TST) before transplantation [4].
There are currently 2 main screening methods for LTBI, namely, tuberculin skin testing (TST) and interferon-gamma release assays (IGRA). The IGRA has several advantages over TST, including not requiring a return visit for reading as well as not being impacted by prior receipt of the Bacillus Calmette-Guérin (BCG) vaccine. Both tests are dependent on the underlying immune function of the host and can have false-negative results due to anergy in patients with end-stage organ disease who are the exact patient population who are SOT candidates. Current American Society of Transplantation guidelines recommend screening for LTBI in all transplant candidates, either by TST or IGRA. If positive, it is recommended to consider therapy for LTBI (ie, prophylaxis) after ruling out active TB [1]. The incidence of TST- or IGRA-positive patients who go on to develop active TB posttransplant is unknown. We therefore conducted this systematic review and meta-analysis of diagnostic test accuracy studies to assess the accuracy of both TST and IGRA in predicting posttransplantation active TB among SOT recipients. The primary objective of our study was to estimate the predictive accuracy of IGRA and TST for predicting active TB after transplantation, assessing the positive and negative predictive values of both tests. Our secondary aim was to compare the predictive values of IGRA versus TST.
METHODS
This systematic review and meta-analysis was conducted according to PRISMA for Diagnostic Test Accuracy guidance.
Search Strategy
A comprehensive search for relevant studies was conducted in Medline, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) until June 2022. Search terms for each database are detailed in Supplementary Appendix 1. Citations in retrieved articles, clinical practice guidelines, and review articles were also reviewed, and a gray literature search was done to address the possibility of publication bias.
Inclusion Criteria and Outcomes
Studies eligible for inclusion were studies of any design, including randomized controlled trials, prospective or retrospective cohort studies, and case-control studies. Case series including less than 10 participants and case reports were excluded. The index tests included were TST and the 2 IGRA tests—QuantiFERON gold (QIAGEN) and T-SPOT.TB test (Oxford Immunotec). The target condition identified by these tests is LTBI, according to the cutoffs defined for each test in individual studies. As for reference standard, no gold standard exists. Only studies providing complete information on their binary classification (ie, a 2×2 table(s) of data on true-positive, false-positive, true-negative, and false-negative results could be extracted) were included in the analysis. Studies were included if they included adult patients who were tested for LTBI and who underwent at least 1 solid organ transplant (lung, heart, kidney, liver, pancreas, small bowel). Studies that included other types of immunocompromised host (eg, recipients of stem cell transplants or patients with human immunodeficiency virus infection) data for SOT recipients only was extracted.
To assess the predictive values of the tests, the outcome of interest was newly diagnosed active TB appearing after transplantation, as defined in individual studies. Therefore, only patients who underwent transplant and had follow-up data available were included.
Data Extraction
Two reviewers independently reviewed the search results and retrieved publications that met the inclusion criteria. Two additional independent reviewers extracted data regarding test results as a 2 × 2 table according to 1 axis—test's results (TST/IGRA positive or negative)—and the other axis—active TB (yes/no). In addition, data were collected including study characteristics, patients baseline characteristics, TB exposure data (personal history or exposure, radiographic findings, residency, immigration, etc), and TB prophylaxis details (the term “prophylaxis” in this manuscript refers to any prophylactic regimen administered for LTBI). Outcomes, including test results and active TB cases, were also collected.
The reviewers also independently assessed the methodological quality of each publication using QUADAS-2 [5]. In cases of discrepancy, a third reviewer was included in the discussion to solve any disagreement. Corresponding authors of included studies were approached via email for additional data, whenever necessary.
Meta-Analysis and Statistical Methods
For each study, we estimated the chance of developing active TB after transplantation, after a positive index test (equaling the positive predictive value [PPV]), or after a negative index test (equaling 1 minus the negative predictive value [NPV]). The PPV of an index test was defined as the number of index test positive patients before transplantation who also developed active TB after transplantation, divided by all index test positive patients before transplantation. The chance of developing active TB after a negative index test was defined as the number of index test negative patients before transplantation who also developed active TB after transplantation, divided by all index test negative patients before transplantation. Because predictive values depend on the incidence of active TB in the relevant patient group, we also estimated the incidence of TB in each included study.
We summarized the estimates of predictive values in a bivariate binomial random effects meta-analysis, as described previously [6] using PROC NLMIXED in SAS 9.4. The different tests were analyzed in subgroups, and potential sources of heterogeneity were added as covariates to the meta-regression analyses to investigate whether they were associated with differences in predictive values. These potential sources of heterogeneity included TB burden in the country in which the study was conducted (high vs low burden based on the World Health Organization list [7], transplantation status [transplanted individuals vs candidates]). Incidence was summarized with a univariate binomial random effects meta-analysis, also using NLMIXED and SAS 9.4. Whenever possible, we also analyzed differences in predictive values between tests including only studies in the meta-analyses that directly compared 2 or more index tests.
Sensitivity analyses were performed to investigate the effect of studies with high or low risk of bias and studies excluding indeterminate results of IGRA tests. Because many studies reported no or very low numbers of active TB cases, it was not possible to estimate predictive sensitivity for all studies. For those studies that reported more than zero cases of active TB, we estimated both sensitivity and specificity. Sensitivity was defined as the proportion of positive test results before transplantation among those who developed active TB after transplantation. Specificity was defined as the proportion of negative test results before transplantation among those who did not develop active TB after transplantation. We summarized the estimates of sensitivity and specificity in a bivariate binomial random effects meta-analysis [8], using PROC NLMIXED in SAS 9.4.
RESULTS
The trial flow chart is presented in Figure 1. Overall, 43 studies (36 403 patients) were included (9–49): 18 were comparative (17 compared TST to either IGRA test, 1 compared QuantiFERON-TB Gold In-Tube [QFT-GIT] with T-SPOT.TB) [9], and 25 were noncomparative (19 TST, 6 QFT-GIT). One study was a randomized controlled trial [10], 15 were prospective, and 27 were retrospective cohort studies. Most studies included various solid organ transplant recipients, although 8 followed candidates for transplant. Eight studies were conducted in high TB-burden countries [11–18]. Overall, 344 patients had active TB and were tested for LTBI. Detailed data regarding included studies are described in Table 1.
Figure 1.
Study flow. PPD, purified protein derivative; SOT, solid organ transplantation; TB, tuberculosis.
Table 1.
Characteristics of Included Studies
| First Author, Year of Publication | Country or Region | TB Burdena | Design | LTBI Test | Participants | N | Universal Prophylaxisb | N (%) Received Prophylaxisc | Follow Up (Month) | N Active TB |
|---|---|---|---|---|---|---|---|---|---|---|
| Comparative Studies | ||||||||||
| Kim, 2013 [19] | South Korea | Low | Prospective | TST vs QFT-GIT | Kidney transplant recipients | 109 | No | NS | Mean 24.6 (SD 14.4) | 1 |
| Ahmadinejad, 2013 [20] | Iran | Low | Prospective | TST vs QFT-GIT | Solid organ transplant candidates | 164 | No | 100% (26/26) | Mean 18 (range 1–36) | 0 |
| Solid organ transplant recipients | 40 | 100% (4/4) | ||||||||
| Kim, 2013 [21] | South Korea | Low | Prospective | TST vs QFT-GIT | Kidney transplant recipients | 126 | No | 0% (0/56) | Median 12.9 (0.4–22.0) after transplantation | 0 |
| Sester, 2014 [22] | Europe | Low | Prospective | TST vs QFT-GIT vs T-SPOT.TB | Solid organ transplant recipients | 197 | No | NS | Median 21.6 (IQR, 24–36) | 0 |
| Sherkat, 2014 [23] | Iran | Low | Prospective | TST vs T-SPOT.TB | Kidney transplant candidates | 44 | No | 100% (10/10) | 12 (all patients) | 1 |
| Muñoz, 2015 [24] | Spain | Low | Prospective | TST vs QFT-GIT | Liver transplant recipients | 50 | No | 0% (0/26) | Median 47.5 (range 35–53.9) after transplantation | 1 |
| Torre-Cisneros, 2015 [10] | Spain | Low | Prospective (RCT) | TST vs IGRA | Liver transplant recipients | 64 | Yes | 100% (64/64) | Median 9.3 (range 1.7–18.0) | 0 |
| Kim, 2015 [25] | South Korea | Low | Prospective | TST vs T-SPOT.TB | Kidney transplant recipients | 312 | No | 100% (40/40) | Median 14.5 (IQR, 9.9–19.6) | 6 |
| Edathodu, 2017 [26] | Saudi Arabia | Low | Prospective | TST vs QFT-GIT | Kidney transplant candidates | 278 | No | 100% (53/53) | Median 25, mean 27 (range 2–58) | 0 |
| Kidney transplant recipients | 173 | |||||||||
| Ishikawa, 2017 [9] | Japan | Low | Prospective | QFT-GIT vs T-SPOT.TB | Kidney transplant recipients | 92 | No | NS | Median 33.1 (IQR, 31.5–35.1) after IGRA testing | 0 |
| Fitzpatrick, 2010 [27] | USA | Low | Retrospective | TST vs QFT-GIT | Solid organ transplant candidates | 83 | No | 100% (14/14) | Median 11.6 (range 2.2–25.5) | 0 |
| Goto, 2010 [28] | Japan | Low | Retrospective | TST vs QFT-GIT | Kidney transplant recipients | 100 | No | NS | 24 for QFT-GIT positive | 2 |
| Jafri, 2011 [29] | USA | Low | Retrospective | TST vs QFT | Liver transplant recipients | 420 | No | 60% (15/25) | Mean 34 for recipients with latent tuberculosis | 0 |
| Jeong, 2014 [30] | South Korea | Low | Retrospective | TST vs QFT-GIT | Kidney transplant recipients | 129 | No | NS | Median 8.4 (IQR, 6.8; range 1.1–29.7) | 2 |
| Sidhu, 2014 [31] | Canada | Low | Retrospective | TST vs QFT-GIT | Solid organ transplant candidates | 461 | No | 95% (189/200) | Mean 58.8, median 61.2 (a minimum of 12) | 0 |
| Solid organ transplant recipients | 123 | |||||||||
| Liu, 2014 [11] | China | High | Retrospective | TST vs IGRA | Liver or kidney transplant recipients | 1914 | No | 0% (0/12) | Median 74.4 (IQR, 31.2–141.6) after transplantation | 17 (tested, overall there were 45 active TB cases) |
| Jambaldorj, 2017 [32] | South Korea | Low | Retrospective | TST vs QFT-GIT | Kidney transplant recipients | 446 | No | 0% (0/18) | Median 30.2 | 3 |
| Moon, 2017 [33] | South Korea | Low | Retrospective | TST vs QFT-GIT | Liver transplant recipients | 277 | No | 50% (19/38) | Median 32.5 (range 1.5–74.2) | 7 |
| Noncomparative Studies | ||||||||||
| Ravi Shankar, 2005 [12] | India | High | Prospective | TST | Kidney transplant candidates | 108 | No | 0% (0/46) | Transplant recipients: Mean 23.34 (range 20–30) | 4 |
| Kidney transplant recipients | 79 | |||||||||
| Bravo, 2005 [34] | Spain | Low | Prospective | TST | Lung transplant recipients | 187 | Yes | 82% (50/61) of those with positive TST; 51% (95/187) of the entire cohort | Mean 18.9 (1.7–78.3) | 3 |
| Torre-Cisneros, 2009 [2] | Spain | Low | Prospective | TST | Solid organ transplant recipients | 4388 | No | 43% (147/338) | Median 12 (range 0–24) | 6 |
| Agarwal, 2010 [13] | India | High | Prospective | TST | Kidney transplant recipients | 200 | No | 0% (0/21) | TST negative: mean 33.4 (SD 21.9); TST positive: mean 24.0 (SD 13.4) | 25 |
| Lange, 2012 [35] | Germany | Low | Prospective | QFT-GIT | Solid organ transplant recipients | 233 | No | NS | Mean 28 | 1 |
| Poglajen, 2018 [36] | Slovenia | Low | Prospective | QFT-GIT | Heart transplant recipients | 140 | No | 0% (0/26) | 12 (all patients) | 0 |
| Apaydin, 2000 [37] | Turkey | Low | Retrospective | TST | Kidney transplant recipients | 274 | No | 39% (26/67) | Mean 37.2 (SD 18.5) and 52.4 (SD 34.0) for the groups with and without prophylaxis, respectively | 16 |
| Benito, 2002 [38] | Spain | Low | Retrospective | TST | Liver transplant recipients | 373 | No | 18% (16/89) | Median 49 (range 0.5–141) | 5 |
| Basiri, 2005 [39] | Iran | Low | Retrospective | TST | Kidney transplant recipients | 12 820 | No | NS | NS | 120 |
| Ribeiro, 2010 [14] | Brazil | High | Retrospective | TST | Kidney transplant candidates | 244 | No | 63% (26/41) | Median 8.2 | 0 |
| Kidney transplant recipients | NS | |||||||||
| Agoglia, 2011 [15] | Brazil | High | Retrospective | TST | Liver transplant recipients | 191 | No | 41% (17/41) | Median 63.6 | 2 |
| Mojahedi, 2011 [40] | Iran | Low | Retrospective | TST | Kidney transplant recipients | 508 | No | 100% (64/64) | Mean 54 (12–168) | 9 |
| Theodoropoulos, 2012 [41] | USA | Low | Retrospective | QFT-GIT | Solid organ transplant candidates | 694 | No | 73% (179/246) | Mean 11.7 after screening; 10.8 after transplantation | 3 |
| Solid organ transplant recipients | 142 | |||||||||
| Jung, 2012 [42] | South Korea | Low | Retrospective | TST | Kidney transplant recipients | 1097 | No | 0% (0/228) | Mean 53.0 (1.0–127.6) | 13 |
| 729 performed TST | ||||||||||
| Arreola-Guerra, 2012 [43] | Mexico | Low | Retrospective | TST | Kidney transplant recipients | 209 | No | 97% (58/60) | Mean 49.6 (range 3.6–290) | 1 |
| Joo, 2013 [44] | South Korea | Low | Retrospective | TST | Kidney transplant recipients | 2799 | No | 0% (0/185) | Mean 164 (SD 73.2) | 7 |
| Higuita, 2014 [45] | Colombia | Low | Retrospective | TST | Kidney transplant recipients | 641 | No | 100% (163/163) | 12 (all patients) | 11 |
| Meinerz, 2016 [16] | Brazil | High | Retrospective | TST | Kidney transplant recipients | 1737 | No | 62% (135/217) | Median 63.2 (range 1.2–174.8) after transplantation | 32 |
| 761 performed TST | ||||||||||
| Guirao-Arrabal, 2016 [46] | Spain | Low | Retrospective | TST | Lung transplant recipients | 369 | No | 43% (30/70) | Median 18.6 (range 0–221.2) | 5 (tested, 6 overall) |
| 296 performed TST | ||||||||||
| Daher Costa, 2017 [17] | Brazil | High | Retrospective | TST | Kidney transplant recipients | 1573 | No | 98% (175/179) | >6 (all patients) | 33 |
| Alpaydin, 2018 [47] | Turkey | Low | Retrospective | TST | Liver transplant recipients | 403 | No | 0% (0/28) | Median 60 (range 6–120) after transplantation | 3 |
| 108 performed TST | ||||||||||
| Hand, 2018 [48] | USA | Low | Retrospective | QFT-GIT | Liver transplant recipients | 148 | No | 15% (3/20) | Median 30 (IQR, 22–41), 18 (13–26), and 23 (17–30) following screening, for positive, intermediate, and negative QFT-GIT, respectively | 3 |
| Rafiei, 2019 [49] | Australia | Low | Retrospective | QFT-GIT | Kidney transplant recipients | 660 | No | 50% (1/2) | 3647 person-years | 2 |
| 28 performed QFT-GIT | ||||||||||
| Wigg, 2019 [50] | Australia | Low | Retrospective | QFT-GIT | Liver transplant recipients | 155 | No | 100% (8/8) | NS | 0 |
| Lauar, 2021 [18] | Brazil | High | Retrospective | TST | Liver transplant recipients | 429 | No | 34% (12/35) | Mean 38.4 (SD 19.2) after transplantation | 0 |
| 213 performed TST | ||||||||||
Abbreviations: IQR, interquartile range; LTBI, latent tuberculosis infection; NS, not significant; QFT-GIT, QuantiFERON-TB Gold In-Tube; RCT, randomized controlled trial; SD, standard deviation; TB, tuberculosis; TST, tuberculin skin test.
Predictive Value of Screening Tests in Predicting Active Tuberculosis
To address the question of what is the chance of active TB posttransplantation with either a positive or a negative test result (regardless of specific test type), what is the chance of active TB posttransplantation, we compiled 34 studies for TST, 21 for QFT, and 5 studies for T-SPOT, all including one 2 × 2 table per study. Positive predictive value and NPV of any screening test against incidence during the follow-up time of the individual study are provided in Figure 2A and B, respectively. For IGRA tests taken together, PPV and NPV were 1.2% and 99.6%, respectively. For QFT and T-SPOT separately, PPV was 0.86% and 1.59% and NPV was 99.6% and 97.6%, respectively. For TST, PPV was 2.13% and NPV was 95.5%. The incidence of active TB was calculated and was demonstrated to be higher in studies using TST compared with IGRA studies (TST - 1.65%, 95% confidence interval [CI] = 0.96%–2.8%; QFT - 0.5, 95% CI = 0.21–1.18; T-SPOT - 0.88, 95% CI = 9.54–1.43).
Figure 2.
Positive predictive value (PPV) (A) and negative predictive value (NPV) (B) (y-axis) of any screening test against incidence (x-axis).
Results according to TST cutoff used (5 or 10 mm) are provided in Table 2. As expected, PPV was 2 times higher using the 10-mm cutoff (2.4%) compared to the 5-mm cutoff (1.2%). The NPV was 97.3% using the 10-mm cutoff and 99.4% using the 10-mm cutoff.
Table 2.
Direct Comparisons Between TST (Any Cutpoint) and IGRA
| First Author | Test | Cutoff | PPV | NPV | Test | Cutoff | PPV | NPV | Delta_PPV | Delta_NPV |
|---|---|---|---|---|---|---|---|---|---|---|
| Jafri | TST | 5 | 0.00 | 1.00 | IGRA_QFT | 0.35 | 0.09 | 1.00 | −0.09 | 0.00 |
| Sidhu | TST | 5 | 0.00 | 1.00 | IGRA_QFT | NA | 0.00 | 1.00 | 0.00 | 0.00 |
| Torre-Cisneros | TST | 5 | 0.00 | 1.00 | IGRA_QFT | NA | 0.00 | 1.00 | 0.00 | 0.00 |
| Munoz | TST | 5 | 0.04 | 1.00 | IGRA_QFT | NA | 0.05 | 1.00 | 0.00 | 0.00 |
| Kim | TST | 10 | 0.00 | 0.99 | IGRA_TSPOT | NA | 0.04 | 1.00 | −0.04 | −0.01 |
| Kim SY | TST | 10 | 0.00 | 1.00 | IGRA_QFT | 0.35 | 0.00 | 1.00 | 0.00 | 0.00 |
| Ahmadinejad | TST | 10 | 0.00 | 0.99 | IGRA_QFT | NA | 0.00 | 0.99 | 0.00 | 0.00 |
| Ahmadinejad | TST | 10 | 0.00 | 0.97 | IGRA_QFT | NA | 0.00 | 0.97 | 0.00 | 0.00 |
| Sherkat | TST | 10 | 0.13 | 1.00 | IGRA_TSPOT | NA | 0.17 | 1.00 | −0.04 | 0.00 |
| Jambaldorj | TST | 10 | 0.00 | 0.95 | IGRA_QFT | 0.35 | 0.06 | 0.98 | −0.06 | −0.03 |
| Moon | TST | 10 | 0.02 | 0.98 | IGRA_QFT | 0.35 | 0.03 | 0.98 | −0.01 | 0.00 |
| Fitzpatrick | TST | NA | 0.00 | 1.00 | IGRA_QFT | NA | 0.00 | 1.00 | 0.00 | 0.00 |
| Sester | TST | NA | 0.00 | 1.00 | IGRA_QFT | NA | 0.00 | 0.99 | 0.00 | 0.01 |
Abbreviations: IGRA, interferon gamma release assay; NA, not applicable; NPV, negative predictive value; PPV, positive predictive value; QFT, QuantiFERON-TB; TST, tuberculin skin test.
Predictive Value of Tuberculin Skin Test Versus Interferon-Gamma Release Assays
Data from 13 studies reporting both TST and IGRA tests results were available for this analysis. The comparison is reported in Table 2, presented by TST cutoff. Only 2 of the IGRA studies reported T-SPOT; hence, QFT-GIT and T-SPOT test reports are combined. The NPV of both tests is similarly high and higher for TST when the lower cutoff (5 mm) is used. The PPV is low for both tests, as expected by the low pretest probability.
Investigation of Heterogeneity
Tuberculosis Burden
As expected, low-burden countries had a lower incidence of 0.75% (95% CI, 0.21%–1.29%) compared to 1.95% (95% CI, 0.49%–3.41%) in high-burden settings. This difference was nonstatistically significant (P = .0641). Predictive value of screening tests according to type of test and TB-burden settings are presented in Table 3. Tuberculosis burden significantly affected PPV (P = .0025) but not NPV (P = .1482). Overall, PPV is higher when TB burden is higher, regardless of test type, although still low in absolute terms (Table 3).
Table 3.
Predictive Values of Screening Tests According to TB Burden
| a: Predictive Values of Screening Tests According to TB Burden | ||||
|---|---|---|---|---|
| Low Burden (Estimate, 95% Confidence Interval); Incidence | Low Burden—Incidence | High Burden (Estimate, 95% Confidence Interval); Incidence | High Burden—Incidence | |
| PPV IGRA | 0.0042 (0.0015–0.0116) | 0.00586 (0.001429– 0.01030) | 0.01721 (0.001322–0.03310) | 0.009031 (0.002659–0.01540 |
| PPV TST | 0.01151 (0.002840– 0.02018) | 0.01369 (0.002674– 0.0247) | 0.04616 (0.01448–0.07784) | 0.02100 (0.005654– 0.03635 |
| NPV IGRA | 0.9973 (0.9950–0.9995) | 0.00586 (0.001429– 0.01030) | 0.9951 (0.9909–0.9993) | 0.009031 (0.002659– 0.01540) |
| NPV TST | 0.9883 (0.9809–0.9956) | 0.01369 (0.002674– 0.0247) | 0.9791 (0.9659–0.9923) | 0.02100 (0.005654 –0.03635) |
Abbreviations: IGRA, interferon gamma release assay; NPV, negative predictive value; PPV, positive predictive value; TB, tuberculosis; TST, tuberculin skin test.
Prophylaxis Status (Latent Tuberculosis Infection Prophylaxis Yes/No)
Incidence of active TB was similar between studies using prophylaxis (mean incidence 1.22%; 95% CI, 0.2179–2.221) and those not using prophylaxis for LTBI (mean incidence 1.045; 95% CI, 0.2731–1.817; P = .7717).
Predictive values of screening tests according to type of test and prophylaxis administration are presented in Table 4. Prophylaxis did not significantly affect either (PPV, P = .3439; NPV, P = .6457).
Table 4.
Predictive Values of Screening Tests According to TB Prophylaxis
| Prophylaxis (Estimate, 95% Confidence Interval) | No Prophylaxis (Estimate, 95% Confidence Interval) | |
|---|---|---|
| PPV IGRA | 0.005667 (−0.00173 to 0.01307 | 0.009435 (0.001259 to 0.01761) |
| PPV TST | 0.01725 (0.003864 to 0.03064) | 0.02850 (0.005281 to 0.05172) |
| NPV IGRA | 0.9974 (0.9944 to 1.0004) | 0.9968 (0.9943 to 0.9993) |
| NPV TST | 0.9866 (0.9774 to 0.9958) | 0.9834 (0.9719 to 0.9948) |
Abbreviations: IGRA, interferon gamma release assay; NPV, negative predictive value; PPV, positive predictive value; TB, tuberculosis; TST, tuberculin skin test.
Transplant Status (Transplanted or Candidate)
In studies evaluating candidates for transplant (n = 8), we found an incidence of 1.280 (0–0.3731); and in those including only transplant recipients, the incidence was significantly higher, at 1.423 (0.6722–2.173) (P = .0164). No significant difference in the PPV was demonstrated between candidates and transplant recipients (Table 5).
Table 5.
Predictive Values of Screening Tests According to Transplant Status
| Candidates (Estimate, 95% Confidence Interval) | Transplanted (Estimate, 95% Confidence Interval) | |
|---|---|---|
| PPV IGRA | 0.004704 (0.0009 to 0.0245) | 0.01021 (0.001077 to 0.01933) |
| PPV TST | 0.01059 (−0.00608 to 0.02725) | 0.02281 (0.008762–0.03687) |
| NPV IGRA | 0.9982 (0.9959 to 1.0006) | 0.9964 (0.9935 to 0.9993) |
| NPV TST | 0.9920 (0.9819 to 1.0020) | 0.9838 (0.9753 to 0.9923) |
Abbreviations: IGRA, interferon gamma release assay; NPV, negative predictive value; PPV, positive predictive value; TST, tuberculin skin test.
Sensitivity and Specificity of Each Test
Twenty-nine studies were included in the analysis of sensitivity and specificity of TST, demonstrating low sensitivity (30.9%; 95% CI, 21.8%–41.7%) and relatively high specificity (77.9%; 95% CI, 72.7%–82.5%). Ten studies were included in the analysis of sensitivity and specificity of IGRA-QFT, showing similar results (sensitivity 37.5%, 95% CI = 11.7%–63.1%; specificity 79.9%, 95% CI = 71.5%–86.3%). For T-SPOT assay, only 3 studies were included, demonstrating sensitivity of 82.3% (95% CI, 10.7%–99.5%) and specificity of 73.5% (95% CI, 61.5%–82.8%). (Supplementary Figure 1). QUADAS-2 scoring was performed for all studies as adopted for our review (Supplementary Table 1). Because all studies but 5 were not scored as low risk of bias, we did not perform sensitivity analysis according to quality assessment. See Supplementary Table 2 for detailed scoring.
DISCUSSION
Patients undergoing SOT are at significantly increased risk of developing active TB compared with the general population. Moreover, active TB is associated with poorer outcomes including 15% graft loss and up to 20%–30% mortality [1]. As a result, both the American Society of Transplant and the European Society for Clinical Microbiology and Infectious Diseases (ESCMID) recommend pretransplant screening for LTBI [18]. Screening for LTBI using either the TST, T.SPOT, or IGRA has been the cornerstone of LTBI diagnosis for many years. However, our analysis showed that relying solely on these diagnostic tests may not be the optimal strategy for LTBI screening in this population. We found a low PPV (<3%) of any LTBI screening test to predict active TB. The NPV was over 95% for any test. The low PPV was consistent regardless of TB burden (although most studies were conducted in low-burden countries), prophylaxis use for LTBI (administered or not), and transplant status (transplant recipients or candidates for transplant). Low PPV in any TB-burden setting may also be explained by the fact that even in high-burden countries, the incidence of TB may not be equally distributed across the whole population, and potentially transplant recipients’ exposure risk to TB does not reflect the entire country's TB burden.
Our study also found no differences in the performance characteristics between TST and QFT-GIT. Both tests demonstrated low sensitivity (30.9% and 37.5%, respectively) and good specificity (77.9% for both tests) in predicting active TB. The T.SPOT demonstrated better sensitivity (82.3%) with similar specificity (73.5%), although a limited number of studies was included for this analysis.
Our findings are in contrast with the conventional wisdom that IGRA is more specific due to a lack of cross-reactivity with patients who received BCG vaccination. It should be noted that TST was primarily used in high prevalence countries. Therefore, the low prevalence in countries using IGRA may have impacted its predictive value. Nevertheless, these countries are not expected to routinely use IGRA, due to cost considerations. We did find that a positive screening test was predictive of an increased risk of active TB, but the absolute number of cases (344) was low.
Previous meta-analyses in various populations have similarly demonstrated the limited value of LTBI screening tests for predicting progression to active TB, although with variable results. Auguste et al [51] performed a systematic review and meta-analysis including various populations, aiming to compare the predictive ability of TST versus IGRA for active TB. Only 4 studies were included evaluating immunocompromised patients, showing predictive ability of positive versus negative tests, although the absolute PPV was low. No significant difference was demonstrated between TST and IGRA in this meta-analysis [51]. Diel et al [52] performed a similar meta-analysis including any patient population, and they reported a PPV of 2.7% for IGRA versus 1.5% for TST, with a high NPV for both. The PPV increased when performing an analysis of high-risk population [51]. Rangaka et al [53] included 15 studies evaluating IGRA compared with TST, and they concluded that none of the tests have high accuracy for predicting active tuberculosis. The low PPV in these meta-analysis, similar to our results of PPV <3%, is probably due, at least in part, to the low pretest probability for active TB [54]. The low PPV in our meta-analysis implies that a positive test translates to very low risk of active TB (less than 3%). However, the high NPV (∼98%) implies that a transplant recipient with a negative test has less than 2% chance for active TB posttransplantation. This may mean that regardless of a positive or negative test, the risk for active TB is very low (<3%).
Our study had some limitations that merit further consideration. First, there are limitations that are common to any studies of LTBI diagnosis. There is no true gold standard for the diagnosis of LTBI; therefore, different studies use different markers to define a case of suspected LTBI, including contact with a case of active TB, prior history of TB, radiographic evidence of prior TB, or some combination thereof. In addition, there is heterogeneity in the cutoff value used for a positive TST. Moreover, most of the studies included in this meta-analysis provided prophylaxis for patients testing positive for LTBI, limiting an assessment of the accuracy of screening tests for predicting the likelihood of active TB posttransplant. The assessment is further limited by lack of data regarding compliance to prophylaxis and results among those not completing the prophylactic course. Of note, even in studies in which patients did not receive prophylaxis, there were still low rates of active TB. This ties into an additional limitation that the outcome of interest, active TB, even in this patient population is rare. Another limitation may be the design of original studies. None of the studies randomized patients to screening versus no screening or one test versus the other. These interventions should be considered for future research, although, as stated above, the outcome of active TB is expected to be scarce. In addition, none of the included studies evaluated the 4-Tube QuantiFERON (QuantiFERON Gold Plus), which has been proposed to lead to less indeterminate results [53]. As opposed to sensitivity and specificity, the predictive value is affected by the prevalence of the examined phenomenon in the population. Accordingly, the PPV/NPV of screening tool for LTBI to predict active TB will probably differ between countries and/or areas. Additional studies from high-burden countries are needed to evaluate the PPV in these countries.
Although the goal is to minimize as many preventable illnesses as possible, currently available screening tests for LTBI are subject to several limitations that can arise specifically in the pre-SOT population. Completing the prophylaxis course for LTBI may be challenging among SOT recipients, limited by drug-drug interactions and hepatotoxicity. Hence, unnecessary therapy should be avoided.
It has been proposed that abnormal radiographic imaging in SOT candidates from an endemic area may be at higher risk of developing posttransplant active TB, with computed tomography (CT) of the chest being more sensitive than chest x-ray [47]. Introduction of chest CT to the decision-making algorithm regarding antituberculous prophylaxis should be tested.
CONCLUSIONS
In summary, current screening tests for LTBI in candidates for transplant or transplant recipients provide high NPV but low PPV of less than 3% for active TB. These results were demonstrated for both TST and IGRA tests. Other strategies for risk stratification, probably incorporating epidemiological data, chest imaging, as well as screening tests, should be studied to provide decisions on prophylaxis.
Supplementary Material
Acknowledgments
Contributor Information
Dafna Yahav, Infectious Diseases Unit, Sheba Medical Center, Ramat-Gan, Israel; Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
Melissa R Gitman, Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Ili Margalit, Infectious Diseases Unit, Sheba Medical Center, Ramat-Gan, Israel; Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
Tomer Avni, Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel; Medicine A, Rabin Medical Center, Beilinson Hospital, Petah-Tikva, Israel.
Mariska M G Leeflang, Department of Epidemiology and Data Science, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
Shahid Husain, Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Canada.
Supplementary Data
Supplementary materials are available at Open Forum Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
References
- 1. Subramanian AK, Theodoropoulos NM. Infectious Diseases Community of Practice of the American Society of Transplantation. Mycobacterium tuberculosis infections in solid organ transplantation: guidelines from the Infectious Diseases Community of Practice of the American Society of Transplantation. Clin Transplant 2019; 33:e13513. [DOI] [PubMed] [Google Scholar]
- 2. Torre-Cisneros J, Doblas A, Aguado JM, et al. Tuberculosis after solid-organ transplant: incidence, risk factors, and clinical characteristics in the RESITRA (Spanish Network of Infection in Transplantation) cohort. Clin Infect Dis 2009; 48:1657–65. [DOI] [PubMed] [Google Scholar]
- 3. Singh N, Paterson DL. Mycobacterium tuberculosis infection in solid-organ transplant recipients: impact and implications for management. Clin Infect Dis 1998; 27:1266–77. [DOI] [PubMed] [Google Scholar]
- 4. Munoz P, Rodriguez C, Bouza E. Mycobacterium tuberculosis infection in recipients of solid organ transplants. Clin Infect Dis 2005; 40:581–7. [DOI] [PubMed] [Google Scholar]
- 5. Whiting PF, Rutjes AWS, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155:529–36. [DOI] [PubMed] [Google Scholar]
- 6. Leeflang MMG, Deeks JJ, Rutjes AWS, Reitsma JB, Bossuyt PMM. Bivariate meta-analysis of predictive values of diagnostic tests can be an alternative to bivariate meta-analysis of sensitivity and specificity. J Clin Epidemiol 2012; 65:1088–97. [DOI] [PubMed] [Google Scholar]
- 7. World Health Organization . WHO global lists of high burden countries for TB, multidrug/rifampicin-resistant TB (MDR/RR-TB) and TB/HIV, 2021–2025. Geneva: World Health Organization; 2021. [Google Scholar]
- 8. Macaskill P. Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 1.0. The Cochrane Collaboration, 2010. Chapter 10: Analysing and Presenting Results 2010. Available at: https://methods.cochrane.org/sites/methods.cochrane.org.sdt/files/uploads/Chapter%2010%20-%20Version%201.0.pdf. [Google Scholar]
- 9. Ishikawa S, Igari H, Akutsu N, et al. Comparison of interferon-γ release assays, QuantiFERON TB-GIT and T-spot.TB, in renal transplantation. J Infect Chemother 2017; 23:468–73. [DOI] [PubMed] [Google Scholar]
- 10. Torre-Cisneros J, San-Juan R, Rosso-Fernández CM, et al. Tuberculosis prophylaxis with levofloxacin in liver transplant patients is associated with a high incidence of tenosynovitis: safety analysis of a multicenter randomized trial. Clin Infect Dis 2015; 60:1642–9. [DOI] [PubMed] [Google Scholar]
- 11. Liu J, Yan J, Wan Q, Ye Q, Huang Y. The risk factors for tuberculosis in liver or kidney transplant recipients. BMC Infect Dis 2014; 14:387. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Shankar MS, Aravindan AN, Sohal PM, et al. The prevalence of tuberculin sensitivity and anergy in chronic renal failure in an endemic area: tuberculin test and the risk of post-transplant tuberculosis. Nephrol Dial Transplant 2005; 20:2720–4. [DOI] [PubMed] [Google Scholar]
- 13. Agarwal SK, Gupta S, Bhowmik D, Mahajan S. Tuberculin skin test for the diagnosis of latent tuberculosis during renal replacement therapy in an endemic area: a single center study. Indian J Nephrol 2010; 20:132–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Ribeiro AR, Gazzana MB, Vicari AR, et al. Evaluation of tuberculin skin test in kidney transplant candidates: 2695. Transplantation 2010; 90:674. [Google Scholar]
- 15. Agoglia L, Balbi E, Halpern M, et al. Tuberculosis in liver transplant recipients: prophylaxis in an endemic area. Transplant Proc 2011; 43:199–202. [DOI] [PubMed] [Google Scholar]
- 16. Meinerz G, da Silva CK, Goldani JC, Garcia VD, Keitel E. Epidemiology of tuberculosis after kidney transplantation in a developing country. Transpl Infect Dis 2016; 18:176–82. [DOI] [PubMed] [Google Scholar]
- 17. Costa SD, de Sandes-Freitas TV, Jacinto CN, et al. Tuberculosis after kidney transplantation is associated with significantly impaired allograft function. Transpl Infect Dis 2017; 19:e12750. [DOI] [PubMed] [Google Scholar]
- 18. Lauar ID, Faria LC, Romanelli RMC, Clemente WT. Latent tuberculosis: risk factors, screening and treatment in liver transplantation recipients from an endemic area. World J Transplant 2021; 11:512–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Kim JS, Cho JH, Park GY, et al. Comparison of QuantiFERON-TB Gold with tuberculin skin test for detection of latent tuberculosis infection before kidney transplantation. Transplant Proc 2013; 45:2899–902. [DOI] [PubMed] [Google Scholar]
- 20. Ahmadinejad Z, Azmoudeh Ardalan F, Razzaqi M, Davoudi S, Jafarian A. QuantiFERON-TB gold in-tube test for diagnosis of latent tuberculosis (TB) infection in solid organ transplant candidates: a single-center study in an area endemic for TB. Transpl Infect Dis 2013; 15:90–5. [DOI] [PubMed] [Google Scholar]
- 21. Kim SY, Jung GS, Kim SK, et al. Comparison of the tuberculin skin test and interferon-γ release assay for the diagnosis of latent tuberculosis infection before kidney transplantation. Infection 2013; 41:103–10. [DOI] [PubMed] [Google Scholar]
- 22. Sester M, van Leth F, Bruchfeld J, et al. Risk assessment of tuberculosis in immunocompromised patients. A TBNET study. Am J Respir Crit Care Med 2014; 190:1168–76. [DOI] [PubMed] [Google Scholar]
- 23. Sherkat R, Yaran M, Shoaie P, et al. Concordance of the tuberculin skin test and T-SPOT(®).TB test results in kidney transplant candidates. J Res Med Sci 2014; 19(Suppl 1):S26–9. [PMC free article] [PubMed] [Google Scholar]
- 24. Muñoz L, Gomila A, Casas S, et al. Immunodiagnostic tests’ predictive values for progression to tuberculosis in transplant recipients: a prospective cohort study. Transplant Direct 2015; 1:e12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Kim SH, Lee SO, Park JB, et al. A prospective longitudinal study evaluating the usefulness of a T-cell-based assay for latent tuberculosis infection in kidney transplant recipients. Am J Transplant 2011; 11:1927–35. [DOI] [PubMed] [Google Scholar]
- 26. Edathodu J, Varghese B, Alrajhi AA, et al. Diagnostic potential of interferon-gamma release assay to detect latent tuberculosis infection in kidney transplant recipients. Transpl Infect Dis 2017; 19:e12675. [DOI] [PubMed] [Google Scholar]
- 27. Fitzpatrick MA, Caicedo JC, Stosor V, Ison MG. Expanded infectious diseases screening program for Hispanic transplant candidates. Transpl Infect Dis 2010; 12:336–41. [DOI] [PubMed] [Google Scholar]
- 28. Goto N, Toyoda M, Hiramitsu T, et al. How do we manage kidney transplant recipients with latent tuberculosis infection (LTBI)? Am J Transplant 2010; 10(Supplement 4):213–571. [Google Scholar]
- 29. Jafri SM, Singal AG, Kaul D, Fontana RJ. Detection and management of latent tuberculosis in liver transplant patients. Liver Transpl 2011; 17:306–14. [DOI] [PubMed] [Google Scholar]
- 30. Jeong JC, Koo TY, Jeon HJ, et al. Utility of QuantiFERON-TB assay for prediction of tuberculosis development in kidney transplant patients in an intermediate-tuberculosis-burden country: lack of evidence for enhanced prediction for short-term tuberculosis development. Transplant Proc 2014; 46:583–7. [DOI] [PubMed] [Google Scholar]
- 31. Sidhu A, Verma G, Humar A, Kumar D. Outcome of latent tuberculosis infection in solid organ transplant recipients over a 10-year period. Transplantation 2014; 98:671–5. [DOI] [PubMed] [Google Scholar]
- 32. Jambaldorj E, Han M, Jeong JC, et al. Poor predictability of QuantiFERON-TB assay in recipients and donors for tuberculosis development after kidney transplantation in an intermediate-TB-burden country. BMC Nephrol 2017; 18:88. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Moon HH, Park SY, Kim JM, et al. Isoniazid prophylaxis for latent tuberculosis infections in liver transplant recipients in a tuberculosis-endemic area. Ann Transplant 2017; 22:338–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Bravo C, Roldán J, Roman A, et al. Tuberculosis in lung transplant recipients. Transplantation 2005; 79:59–64. [DOI] [PubMed] [Google Scholar]
- 35. Lange B, Vavra M, Kern WV, Wagner D. Development of tuberculosis in immunocompromised patients with a positive tuberculosis-specific IGRA. Int J Tuberc Lung Dis 2012; 16:492–5. [DOI] [PubMed] [Google Scholar]
- 36. Poglajen G, Cerar A, Frljak S, et al. Preemptive tuberculosis prophylaxis vs. watchful waiting after heart transplantation. J Heart Lung Transplant 2018; 37:S178. [Google Scholar]
- 37. Apaydin S, Altiparmak MR, Serdengeçti K, Ataman R, Oztürk R, Erek E. Mycobacterium tuberculosis infections after renal transplantation. Scand J Infect Dis 2000; 32:501–5. [DOI] [PubMed] [Google Scholar]
- 38. Benito N, Sued O, Moreno A, et al. Diagnosis and treatment of latent tuberculosis infection in liver transplant recipients in an endemic area. Transplantation 2002; 74:1381–6. [DOI] [PubMed] [Google Scholar]
- 39. Basiri A, Moghaddam SM, Simforoosh N, et al. Preliminary report of a nationwide case-control study for identifying risk factors of tuberculosis following renal transplantation. Transplant Proc 2005; 37:3041–4. [DOI] [PubMed] [Google Scholar]
- 40. Mojahedi MJ, Feizzadeh B, Aghdam B, Hekmat R. The prevalence of tuberculosis in recipients of renal transplantation. Saudi J Kidney Dis Transpl 2011; 22:1266–8. [PubMed] [Google Scholar]
- 41. Theodoropoulos N, Lanternier F, Rassiwala J, et al. Use of the QuantiFERON-TB gold interferon-gamma release assay for screening transplant candidates: a single-center retrospective study. Transpl Infect Dis 2012; 14:1–8. [DOI] [PubMed] [Google Scholar]
- 42. Jung JY, Joo DJ, Lee CH, et al. Pre-transplant risk factors for tuberculosis after kidney transplant in an intermediate burden area. Int J Tuberc Lung Dis 2012; 16:248–54. [DOI] [PubMed] [Google Scholar]
- 43. Arreola-Guerra JM, Perez MC, Avila-Cervantes R, et al. Evaluation of a screening program for Tuberculosis in kidney transplant recipients in an endemic country. Am J Transplant 2012; 12:210–2.21992533 [Google Scholar]
- 44. Joo DJ, Kim BS, Kim SJ, et al. Risk factors and characteristics of post-transplant tuberculosis in an endemic area. Ann Transplant 2013; 18:163–73. [DOI] [PubMed] [Google Scholar]
- 45. Higuita LM, Nieto-Ríos JF, Daguer-Gonzalez S, et al. Tuberculosis in renal transplant patients: the experience of a single center in Medellín-Colombia, 2005–2013. J Bras Nefrol 2014; 36:512–8. [DOI] [PubMed] [Google Scholar]
- 46. Guirao-Arrabal E, Santos F, Redel-Montero J, et al. Risk of tuberculosis after lung transplantation: the value of pretransplant chest computed tomography and the impact of mTOR inhibitors and azathioprine use. Transpl Infect Dis 2016; 18:512–9. [DOI] [PubMed] [Google Scholar]
- 47. Özgen Alpaydın A, Özbilgin M, Abdulleyeva M, et al. Determinants and characteristics of tuberculosis in liver transplant recipients. Turk J Med Sci 2018; 48:1162–6. [DOI] [PubMed] [Google Scholar]
- 48. Hand J, Sigel K, Huprikar S, Hamula C, Rana M. Tuberculosis after liver transplantation in a large center in New York City: QuantiFERON®-TB gold-based pre-transplant screening performance and active tuberculosis post-transplant. Transpl Infect Dis 2018; 20:e12845. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Rafiei N, Williams J, Mulley WR, Trauer JM, Jenkin GA, Rogers BA. Mycobacterium tuberculosis: active disease and latent infection in a renal transplant cohort. Nephrology 2019; 24:569–74. [DOI] [PubMed] [Google Scholar]
- 50. Wigg AJ, Narayana SK, Anwar S, et al. High rates of indeterminate interferon-gamma release assays for the diagnosis of latent tuberculosis infection in liver transplantation candidates. Transpl Infect Dis 2019; 21:e13087. [DOI] [PubMed] [Google Scholar]
- 51. Auguste P, Tsertsvadze A, Pink J, et al. Comparing interferon-gamma release assays with tuberculin skin test for identifying latent tuberculosis infection that progresses to active tuberculosis: systematic review and meta-analysis. BMC Infect Dis 2017; 17:200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52. Diel R, Goletti D, Ferrara G, et al. Interferon-γ release assays for the diagnosis of latent Mycobacterium tuberculosis infection: a systematic review and meta-analysis. Eur Respir J 2011; 37:88–99. [DOI] [PubMed] [Google Scholar]
- 53. Rangaka MX, Wilkinson KA, Glynn JR, et al. Predictive value of interferon-γ release assays for incident active tuberculosis: a systematic review and meta-analysis. Lancet Infect Dis 2012; 12:45–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54. Shafeque A, Bigio J, Hogan CA, Pai M, Banaei N. Fourth-generation QuantiFERON-TB Gold Plus: what is the evidence? J Clin Microbiol 2020; 58:e01950-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.