Performance of a stool-based quantitative PCR assay for the diagnosis of tuberculosis in adolescents and adults: a multinational, prospective diagnostic accuracy study.

BACKGROUND: Despite increasing availability of rapid molecular tests for the diagnosis of tuberculosis in high-burden settings, many people with tuberculosis are undiagnosed. Reliance on sputum as the primary specimen for tuberculosis diagnostics contributes to this diagnostic gap. We evaluated the diagnostic accuracy and additive yield of a novel stool quantitative PCR (qPCR) assay for the diagnosis of tuberculosis in three countries in Africa with high tuberculosis burdens. METHODS: We undertook a prospective diagnostic accuracy study in Eswatini, Mozambique, and Tanzania from Sept 21, 2020, to Feb 2, 2023, to compare the diagnostic accuracy for tuberculosis of a novel stool qPCR test with the current diagnostic standard for Mycobacterium tuberculosis DNA detection from sputum and stool, Xpert-MTB/RIF Ultra (Xpert Ultra). Sputum, stool, and urine samples were provided by a cohort of participants, aged 10 years or older, diagnosed with tuberculosis. Participants with tuberculosis (cases) were enrolled within 72 h of treatment initiation for tuberculosis diagnosed clinically or following laboratory confirmation. Participants without tuberculosis (controls) consisted of household contacts of the cases who did not develop tuberculosis during a 6-month follow-up. The performance was compared with a robust composite microbiological reference standard (CMRS). FINDINGS: The cohort of adolescents and adults (n=408) included 268 participants with confirmed or clinical tuberculosis (cases), 147 (55%) of whom were living with HIV, and 140 participants (controls) without tuberculosis. The sensitivity of the novel stool qPCR was 93·7% (95% CI 87·4-97·4) compared with participants with detectable growth on M tuberculosis culture, and 88·1% (81·3-93·0) compared with sputum Xpert Ultra. The stool qPCR had an equivalent sensitivity as sputum Xpert Ultra (94·8%, 89·1-98·1) compared with culture. Compared with the CMRS, the sensitivity of the stool qPCR was higher than the current standard for tuberculosis diagnostics on stool, Xpert Ultra (80·4%, 73·4-86·2 vs 73·5%, 66·0-80·1; p=0·025 on paired comparison). The qPCR also identified 17-21% additional tuberculosis cases compared to sputum Xpert Ultra or sputum culture. In controls without tuberculosis, the specificity of the stool qPCR was 96·9% (92·2-99·1). INTERPRETATION: In this study, a novel qPCR for the diagnosis of tuberculosis from stool specimens had a higher accuracy in adolescents and adults than the current diagnostic PCR gold standard on stool, Xpert-MTB/RIF Ultra, and equivalent sensitivity to Xpert-MTB/RIF Ultra on sputum. FUNDING: National Institutes of Health (NIH) Allergy and Infectious Diseases, and NIH Fogarty International Center.

A stool based qPCR for the diagnosis of TB in children and people living with HIV in Uganda, Eswatini and Mozambique (Stool4TB): a protocol for a multicenter diagnostic evaluation.

BACKGROUND: Tuberculosis (TB) is a major cause of mortality worldwide. Children and people living with HIV (PLHIV) have an increased risk of mortality, particularly in the absence of rapid diagnosis. The main challenges of diagnosing TB in these populations are due to the unspecific and paucibacillary disease presentation and the difficulty of obtaining respiratory samples. Thus, novel diagnostic strategies, based on non-respiratory specimens could improve clinical decision making and TB outcomes in high burden TB settings. We propose a multi-country, prospective diagnostic evaluation study with a nested longitudinal cohort evaluation to assess the performance of a new stool-based qPCR, developed by researchers at Baylor College of Medicine (Houston, Texas, USA) for TB bacteriological confirmation with promising results in pilot studies. METHODS: The study will take place in high TB/HIV burden countries (Mozambique, Eswatini and Uganda) where we will enroll, over a period of 30 months, 650 PLHIV (> 15) and 1295 children under 8 years of age (irrespective of HIV status) presenting pressumptive TB. At baseline, all participants will provide clinical history, complete a physical assessment, and undergo thoracic chest X-ray imaging. To obtain bacteriological confirmation, participants will provide respiratory samples (1 for adults, 2 in children) and 1 stool sample for Xpert Ultra MTB/RIF (Cepheid, Sunnyvale, CA, USA). Mycobacterium tuberculosis (M.tb) liquid culture will only be performed in respiratory samples and lateral flow lipoarabinomannan (LF-LAM) in urine following WHO recommendations. Participants will complete 2 months follow-up if they are not diagnosed with TB, and 6 months if they are. For analytical purposes, the participants in the pediatric cohort will be classified into "confirmed tuberculosis", "unconfirmed tuberculosis" and "unlikely tuberculosis". Participants of the adult cohort will be classified as "bacteriologically confirmed TB", "clinically diagnosed TB" or "not TB". We will assess accuracy of the novel qPCR test compared to bacteriological confirmation and Tb diagnosis irrespective of laboratory results. Longitudinal qPCR results will be analyzed to assess its use as treatment response monitoring. DISCUSSION: The proposed stool-based qPCR is an innovation because both the strategy of using a non-sputum based sample and a technique specially designed to detect M.tb DNA in stool. PROTOCOL REGISTRATION DETAILS: ClinicalTrials.gov Identifier: NCT05047315.

Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis.

BACKGROUND: Many children with pulmonary tuberculosis remain undiagnosed and untreated with related high morbidity and mortality. Recent advances in childhood tuberculosis algorithm development have incorporated prediction modelling, but studies so far have been small and localised, with limited generalisability. We aimed to evaluate the performance of currently used diagnostic algorithms and to use prediction modelling to develop evidence-based algorithms to assist in tuberculosis treatment decision making for children presenting to primary health-care centres. METHODS: For this meta-analysis, we identified individual participant data from a WHO public call for data on the management of tuberculosis in children and adolescents and referral from childhood tuberculosis experts. We included studies that prospectively recruited consecutive participants younger than 10 years attending health-care centres in countries with a high tuberculosis incidence for clinical evaluation of pulmonary tuberculosis. We collated individual participant data including clinical, bacteriological, and radiological information and a standardised reference classification of pulmonary tuberculosis. Using this dataset, we first retrospectively evaluated the performance of several existing treatment-decision algorithms. We then used the data to develop two multivariable prediction models that included features used in clinical evaluation of pulmonary tuberculosis-one with chest x-ray features and one without-and we investigated each model's generalisability using internal-external cross-validation. The parameter coefficient estimates of the two models were scaled into two scoring systems to classify tuberculosis with a prespecified sensitivity target. The two scoring systems were used to develop two pragmatic, treatment-decision algorithms for use in primary health-care settings. FINDINGS: Of 4718 children from 13 studies from 12 countries, 1811 (38·4%) were classified as having pulmonary tuberculosis: 541 (29·9%) bacteriologically confirmed and 1270 (70·1%) unconfirmed. Existing treatment-decision algorithms had highly variable diagnostic performance. The scoring system derived from the prediction model that included clinical features and features from chest x-ray had a combined sensitivity of 0·86 [95% CI 0·68-0·94] and specificity of 0·37 [0·15-0·66] against a composite reference standard. The scoring system derived from the model that included only clinical features had a combined sensitivity of 0·84 [95% CI 0·66-0·93] and specificity of 0·30 [0·13-0·56] against a composite reference standard. The scoring system from each model was placed after triage steps, including assessment of illness acuity and risk of poor tuberculosis-related outcomes, to develop treatment-decision algorithms. INTERPRETATION: We adopted an evidence-based approach to develop pragmatic algorithms to guide tuberculosis treatment decisions in children, irrespective of the resources locally available. This approach will empower health workers in primary health-care settings with high tuberculosis incidence and limited resources to initiate tuberculosis treatment in children to improve access to care and reduce tuberculosis-related mortality. These algorithms have been included in the operational handbook accompanying the latest WHO guidelines on the management of tuberculosis in children and adolescents. Future prospective evaluation of algorithms, including those developed in this work, is necessary to investigate clinical performance. FUNDING: WHO, US National Institutes of Health.