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Because TB control is still hampered by the limitations of diagnostic tools, diagnostic uncertainty is common. The decision to offer treatment is based on clinical decision-making. The therapeutic threshold, test threshold and test-treatment threshold can guide in making these decisions. This review summarizes the literature on methods to estimate the therapeutic threshold that have been applied for TB. Only five studies estimated the threshold for the diagnosis of TB. The therapeutic threshold can be estimated by prescriptive methods, based on calculations, and by descriptive methods, deriving the threshold from observing clinical practice. Test and test-treatment thresholds can be calculated using the therapeutic threshold and the characteristics of an available diagnostic test. Estimates of the therapeutic threshold for pulmonary TB from intuitive descriptive approaches (20%–50%) are higher than theoretical prescriptive calculations (2%–3%). In conclusion, estimates of the therapeutic threshold for pulmonary TB depend on the method used. Other methods exist within the field of decision-making that have yet to be implemented or adapted as tools to estimate the TB therapeutic threshold. Because clinical decision-making is a core element of TB management, it is necessary to find a new, clinician-friendly way to unbiasedly estimate context-specific, agreed upon therapeutic thresholds. [ABSTRACT FROM AUTHOR]

Diagnostic accuracy studies in pulmonary tuberculosis (PTB) are complicated by the lack of a perfect reference standard. This limitation can be handled using latent class analysis (LCA), assuming independence between diagnostic test results conditional on the true unobserved PTB status. Test results could remain dependent, however, e.g. with diagnostic tests based on a similar biological basis. If ignored, this gives misleading inferences. Our secondary analysis of data collected during the first year (May 2018 –May 2019) of a community-based multi-morbidity screening program conducted in the rural uMkhanyakude district of KwaZulu Natal, South Africa, used Bayesian LCA. Residents of the catchment area, aged ≥15 years and eligible for microbiological testing, were analyzed. Probit regression methods for dependent binary data sequentially regressed each binary test outcome on other observed test results, measured covariates and the true unobserved PTB status. Unknown model parameters were assigned Gaussian priors to evaluate overall PTB prevalence and diagnostic accuracy of 6 tests used to screen for PTB: any TB symptom, radiologist conclusion, Computer Aided Detection for TB version 5 (CAD4TBv5≥53), CAD4TBv6≥53, Xpert Ultra (excluding trace) and culture. Before the application of our proposed model, we evaluated its performance using a previously published childhood pulmonary TB (CPTB) dataset. Standard LCA assuming conditional independence yielded an unrealistic prevalence estimate of 18.6% which was not resolved by accounting for conditional dependence among the true PTB cases only. Allowing, also, for conditional dependence among the true non-PTB cases produced a 1.1% plausible prevalence. After incorporating age, sex, and HIV status in the analysis, we obtained 0.9% (95% CrI: 0.6, 1.3) overall prevalence. Males had higher PTB prevalence compared to females (1.2% vs. 0.8%). Similarly, HIV+ had a higher PTB prevalence compared to HIV- (1.3% vs. 0.8%). The overall sensitivity for Xpert Ultra (excluding trace) and culture were 62.2% (95% CrI: 48.7, 74.4) and 75.9% (95% CrI: 61.9, 89.2), respectively. Any chest X-ray abnormality, CAD4TBv5≥53 and CAD4TBv6≥53 had similar overall sensitivity. Up to 73.3% (95% CrI: 61.4, 83.4) of all true PTB cases did not report TB symptoms. Our flexible modelling approach yields plausible, easy-to-interpret estimates of sensitivity, specificity and PTB prevalence under more realistic assumptions. Failure to fully account for diagnostic test dependence can yield misleading inferences. [ABSTRACT FROM AUTHOR]

Background: The Democratic Republic of the Congo has confronted 13 outbreaks of Ebola virus disease since 1976. Rapid diagnostic tests (RDTs) detecting viral antigens have been developed to circumvent difficulties encountered with RT-PCR for diagnosis in remote low-resource settings, but there is still uncertainty about their performance characteristics and usability during outbreaks. We aimed to assess the field performance of three antigen detection RDTs compared with the gold-standard Cepheid GeneXpert Ebola assay results.Methods: We conducted a retrospective, multicentre observational study using complete and de-identified databases of five mobile laboratories (managed by the Institut National de Recherche Biomédicale) to assess the performance of three Ebola virus disease RDTs (QuickNavi-Ebola, OraQuick Ebola Rapid Antigen Test, and Coris EBOLA Ag K-SeT rapid test) run on blood samples of patients with suspected Ebola virus disease in direct comparison with the Cepheid GeneXpert Ebola assay reference test during the 2018-20 outbreak in the eastern Democratic Republic of the Congo. We estimated the sensitivity and specificity of each test through generalised linear mixed models against the GeneXpert Ebola assay reference test and corrected for cycle threshold value and random site effects.Findings: 719 (7·9%) of 9157 samples had a positive GeneXpert Ebola assay result. The QuickNavi-Ebola RDT had a sensitivity of 87·4% (95% CI 63·6-96·8) around the mean cycle threshold value and a specificity of 99·6% (99·3-99·8). The OraQuick Ebola Rapid Antigen Test had a sensitivity of 57·4% (95% CI 38·8-75·8) and specificity of 98·3% (97·5-99·0), and the Coris EBOLA Ag K-SeT rapid test had a sensitivity of 38·9% (23·0-63·6) against the GeneXpert Ebola assay reference and specificity of 97·4% (85·3-99·6). The QuickNavi-Ebola RDT showed a robust performance with good sensitivity, particularly with increasing viral loads (ie, low cycle threshold values), and specificity.Interpretation: The three RDTs evaluated did not achieve the desired sensitivity and specificity of the WHO target product profile. Although the RDTs cannot triage and rule out Ebola virus infection among clinical suspects, they can still help to sort people with suspected Ebola virus disease into high-risk and low-risk groups while waiting for GeneXpert Ebola assay reference testing.Funding: None.Translation: For the French translation of the abstract see Supplementary Materials section. [ABSTRACT FROM AUTHOR]