Guidance for Studies Evaluating the Accuracy of Tuberculosis Triage Tests

Abstract

Approximately 3.6 million cases of active tuberculosis (TB) go potentially undiagnosed annually, partly due to limited access to confirmatory diagnostic tests, such as molecular assays or mycobacterial culture, in community and primary healthcare settings. This article provides guidance for TB triage test evaluations. A TB triage test is designed for use in people with TB symptoms and/or significant risk factors for TB. Triage tests are simple and low-cost tests aiming to improve ease of access and implementation (compared with confirmatory tests) and decrease the proportion of patients requiring more expensive confirmatory testing. Evaluation of triage tests should occur in settings of intended use, such as community and primary healthcare centers. Important considerations for triage test evaluation include study design, population, sample type, test throughput, use of thresholds, reference standard (ideally culture), and specimen flow. The impact of a triage test will depend heavily on issues beyond accuracy, primarily centered on implementation.

Keywords: diagnostics; study design guidance; target product profiles; triage; tuberculosis.

Figure 1.

Clinical pathway for tuberculosis (TB) triage test.

Figure 2.

Precision of accuracy estimates as function of sample size. The lines show the precision of accuracy estimates as function of sample size; accuracy point estimates are chosen according to the minimal target based on the target product profile, ie, sensitivity for TB: 90% (blue line) and specificity: 70% (red line). The y-axis shows total width of the 95% confidence interval ([CI], ie, upper limit of the 95% CI minus the lower limit of the 95% CI) for sensitivity and specificity for a given sample size. The x-axis shows the necessary number of patients with TB to achieve a given precision for the sensitivity estimate and the number of patients without TB to achieve a given precision for the specificity estimate. Of note, this figure should serve to highlight that sample size calculations always represent a reasonable compromise between precision of estimates versus the need to recruit more participants, because an optimal sample size is often hard to achieve.