Commercial nucleic-acid amplification tests for diagnosis of pulmonary tuberculosis in respiratory specimens: meta-analysis and meta-regression

Abstract

Background: Hundreds of studies have evaluated the diagnostic accuracy of nucleic-acid amplification tests (NAATs) for tuberculosis (TB). Commercial tests have been shown to give more consistent results than in-house assays. Previous meta-analyses have found high specificity but low and highly variable estimates of sensitivity. However, reasons for variability in study results have not been adequately explored. We performed a meta-analysis on the accuracy of commercial NAATs to diagnose pulmonary TB and meta-regression to identify factors that are associated with higher accuracy.

Methodology/principal findings: We identified 2948 citations from searching the literature. We found 402 articles that met our eligibility criteria. In the final analysis, 125 separate studies from 105 articles that reported NAAT results from respiratory specimens were included. The pooled sensitivity was 0.85 (range 0.36-1.00) and the pooled specificity was 0.97 (range 0.54-1.00). However, both measures were significantly heterogeneous (p<.001). We performed subgroup and meta-regression analyses to identify sources of heterogeneity. Even after stratifying by type of commercial test, we could not account for the variability. In the meta-regression, the threshold effect was significant (p = .01) and the use of other respiratory specimens besides sputum was associated with higher accuracy.

Conclusions/significance: The sensitivity and specificity estimates for commercial NAATs in respiratory specimens were highly variable, with sensitivity lower and more inconsistent than specificity. Thus, summary measures of diagnostic accuracy are not clinically meaningful. The use of different cut-off values and the use of specimens other than sputum could explain some of the observed heterogeneity. Based on these observations, commercial NAATs alone cannot be recommended to replace conventional tests for diagnosing pulmonary TB. Improvements in diagnostic accuracy, particularly sensitivity, need to be made in order for this expensive technology to be worthwhile and beneficial in low-resource countries.

Figure 1. Study selection process.

Figure 2. Forest plot of sensitivity estimates and 95% CI.

Point estimates of sensitivity from each study are shown as solid circles. The solid lines represent the 95% confidence intervals (CI). Circles are proportional to study size. The pooled estimate is denoted by the diamond at the bottom.

Figure 3. Forest plot of specificity estimates and 95% CI.

Point estimates of specificity from each study are shown as solid circles. The solid lines represent the 95% confidence intervals (CI). Circles are proportional to study size. The pooled estimate is denoted by the diamond at the bottom.

Figure 4. SROC plot with best-fitting asymmetric curve.

Each solid circle represents each study in the meta-analysis. The curve is the regression line that summarizes the overall diagnostic accuracy. SROC = summary receiver operating characteristic; AUC = area under the curve; SE(AUC) = standard error of AUC; Q* = an index defined by the point on the SROC curve where the sensitivity and specificity are equal, which is the point closest to the top-left corner of the ROC space; SE(Q*) = standard error of Q* index.