Biomarkers That Correlate with Active Pulmonary Tuberculosis Treatment Response: a Systematic Review and Meta-analysis.

Current WHO recommendations for monitoring treatment response in adult pulmonary tuberculosis (TB) are sputum smear microscopy and/or culture conversion at the end of the intensive phase of treatment. These methods either have suboptimal accuracy or a long turnaround time. There is a need to identify alternative biomarkers to monitor TB treatment response. We conducted a systematic review of active pulmonary TB treatment monitoring biomarkers. We screened 9,739 articles published between 1 January 2008 and 31 December 2020, of which 77 met the inclusion criteria. When studies quantitatively reported biomarker levels, we meta-analyzed the average fold change in biomarkers from pretreatment to week 8 of treatment. We also performed a meta-analysis pooling the fold change since the previous time point collected. A total of 81 biomarkers were identified from 77 studies. Overall, these studies exhibited extensive heterogeneity with regard to TB treatment monitoring study design and data reporting. Among the biomarkers identified, C-reactive protein (CRP), interleukin-6 (IL-6), interferon gamma-induced protein 10 (IP-10), and tumor necrosis factor alpha (TNF-α) had sufficient data to analyze fold changes. All four biomarker levels decreased during the first 8 weeks of treatment relative to baseline and relative to previous time points collected. Based on limited data available, CRP, IL-6, IP-10, and TNF-α have been identified as biomarkers that should be further explored in the context of TB treatment monitoring. The extensive heterogeneity in TB treatment monitoring study design and reporting is a major barrier to evaluating the performance of novel biomarkers and tools for this use case. Guidance for designing and reporting treatment monitoring studies is urgently needed.

Tuberculosis treatment monitoring tests during routine practice: study design guidance.

SCOPE: The current tools for tuberculosis (TB) treatment monitoring, smear microscopy and culture, cannot accurately predict poor treatment outcomes. Research into new TB treatment monitoring tools (TMTs) is growing, but data are unreliable. In this article, we aim to provide guidance for studies investigating and evaluating TB TMT for use during routine clinical care. Here, a TB TMT would guide treatment during the course of therapy, rather than testing for a cure at the regimen's end. This article does not cover the use of TB TMTs as surrogate endpoints in the clinical trial context. METHODS: Guidelines were initially informed by experiences during a systematic review of TB TMTs. Subsequently, a small content expert group was consulted for feedback on initial recommendations. After revision, feedback from substantive experts across sectors was sought. QUESTIONS ADDRESSED BY THE GUIDELINE AND RECOMMENDATIONS: The proposed considerations and recommendations for studies evaluating TB TMTs for use during the treatment in routine clinical care fall into eight domains. We provide specific recommendations regarding study design and recruitment, outcome definitions, reference standards, participant follow-up, clinical setting, study population, treatment regimen reporting, and index tests and data presentation. Overall, TB TMTs should be evaluated in a manner similar to diagnostic tests, but TB TMT accuracy must be assessed at multiple timepoints throughout the treatment course, and TB TMTs should be evaluated in study populations who have already received a diagnosis of TB. Study design and outcome definitions must be aligned with the developmental phase of the TB TMT under evaluation. There is no reference standard for TB treatment response, so different reference standards and comparator tests have been proposed, the selection of which will vary depending on the developmental phase of the TMT under assessment. The use of comparator tests can assist in generating evidence. Clarity is required when reporting of timepoints, TMT read-outs, and analysis results. Implementing these recommendations will lead to higher quality TB TMT studies that will allow data to be meaningfully compared, thereby facilitating the development of novel tools to guide individual TB therapy and improve treatment outcomes.

Standard error estimation in meta-analysis of studies reporting medians.

We consider the setting of an aggregate data meta-analysis of a continuous outcome of interest. When the distribution of the outcome is skewed, it is often the case that some primary studies report the sample mean and standard deviation of the outcome and other studies report the sample median along with the first and third quartiles and/or minimum and maximum values. To perform meta-analysis in this context, a number of approaches have recently been developed to impute the sample mean and standard deviation from studies reporting medians. Then, standard meta-analytic approaches with inverse-variance weighting are applied based on the (imputed) study-specific sample means and standard deviations. In this article, we illustrate how this common practice can severely underestimate the within-study standard errors, which results in poor coverage for the pooled mean in common effect meta-analyses and overestimation of between-study heterogeneity in random effects meta-analyses. We propose a straightforward bootstrap approach to estimate the standard errors of the imputed sample means. Our simulation study illustrates how the proposed approach can improve the estimation of the within-study standard errors and consequently improve coverage for the pooled mean in common effect meta-analyses and estimation of between-study heterogeneity in random effects meta-analyses. Moreover, we apply the proposed approach in a meta-analysis to identify risk factors of a severe course of COVID-19.

Multicomponent strategy with decentralised molecular testing for tuberculosis in Uganda: a cost and cost-effectiveness analysis.

BACKGROUND: Decentralised molecular testing for tuberculosis could reduce missed diagnoses and losses to follow-up in high-burden settings. The aim of this study was to evaluate the cost and cost-effectiveness of the Xpert Performance Evaluation for Linkage to Tuberculosis Care (XPEL-TB) study strategy, a multicomponent strategy including decentralised molecular testing for tuberculosis, in Uganda. METHODS: We conducted a costing and cost-effectiveness analysis nested in a pragmatic cluster-randomised trial of onsite (decentralised) versus hub-and-spoke (centralised) testing for tuberculosis with Xpert MTB/RIF Ultra (Xpert) in 20 community health centres in Uganda. We collected empirical data on the cost of the XPEL-TB strategy (decentralised Xpert testing, workflow redesign, and performance feedback) and routine tuberculosis testing (onsite smear microscopy with specimen transport for centralised Xpert testing) from the health system perspective. Time-and-motion studies were performed to estimate activity-based service costs. Cost-effectiveness was assessed as the incremental cost (2019 US$) per tuberculosis diagnosis and per 14-day treatment initiation. FINDINGS: The XPEL-TB study ran from Oct 22, 2018, to March 1, 2020. Effectiveness and cost-effectiveness outcomes were assessed from Dec 1, 2018, to Nov 30, 2019 and included 4867 women and 3139 men. On a per-test basis, the cost of decentralised ($20·46, range $17·85-25·72) and centralised ($18·20, range $16·58-24·25) Xpert testing was similar. However, decentralised testing resulted in more patients receiving appropriate Xpert testing, so the per-patient cost of decentralised testing was higher: $20·28 (range $17·68-25·48) versus $9·59 (range $7·62-14·34). The XPEL-TB strategy was estimated to cost $1332 (95% uncertainty range $763-5558) per incremental tuberculosis diagnosis and $687 ($501-1207) per incremental patient initiating tuberculosis treatment within 14 days. Cost-effectiveness was reduced in sites performing fewer than 150-250 tests annually. INTERPRETATION: The XPEL-TB strategy facilitated higher rates of Xpert testing for tuberculosis at a similar per-test cost and modest incremental cost per tuberculosis diagnosis and treatment initiation. Decentralised Xpert testing, with appropriate implementation supports, should be scaled up to clinics with sufficient testing volume to support a single-module device. FUNDING: The National Heart, Lung, and Blood Institute.

Making cough count in tuberculosis care.

Cough assessment is central to the clinical management of respiratory diseases, including tuberculosis (TB), but strategies to objectively and unobtrusively measure cough are lacking. Acoustic epidemiology is an emerging field that uses technology to detect cough sounds and analyze cough patterns to improve health outcomes among people with respiratory conditions linked to cough. This field is increasingly exploring the potential of artificial intelligence (AI) for more advanced applications, such as analyzing cough sounds as a biomarker for disease screening. While much of the data are preliminary, objective cough assessment could potentially transform disease control programs, including TB, and support individual patient management. Here, we present an overview of recent advances in this field and describe how cough assessment, if validated, could support public health programs at various stages of the TB care cascade.

Facility-based directly observed therapy (DOT) for tuberculosis during COVID-19: A community perspective.

Facility-based directly observed therapy (DOT) has been the standard for treating people with TB since the early 1990s. As the commitment to promote a people-centred model of care for TB grows, the use of facility-based DOT has been questioned as issues of freedom, privacy, and human rights have been raised. The disruptions caused by the COVID-19 pandemic and ensuing lockdown measures have fast-tracked the need to find alternative methods to provide treatment to people with TB. In this study, we present quantitative and qualitative findings from a global community-based survey on the challenges of administering facility-based DOT during a pandemic as well as potential alternatives. Our results found that decreased access to transportation, the fear of COVID-19, stigmatization due to overlapping symptoms, and punitive measures against quarantine violations have made it difficult for persons with TB to receive treatment at facilities, particularly in low-resource settings. Potential replacements included greater focus on community-based DOT, home delivery of treatment, multi-month dispensing, and video DOT strategies. Our study highlights the need for TB programs to re-evaluate their approach to providing treatment to people with TB, and that these changes must be made in consultation with people affected by TB and TB survivors to provide a true people-centred model of care.

A novel blood-based assay for treatment monitoring of tuberculosis.

OBJECTIVES: A novel 3-gene host transcriptional signature (GBP5, DUSP3 and KLF2) has been validated for tuberculosis (TB) treatment monitoring using laboratory-based RNA sequencing platforms. The signature was recently translated by Cepheid into a prototype cartridge-based test that can be run on the GeneXpert instrument. In this study, we prospectively evaluated the change in the expression of the cartridge-based 3-gene signature following treatment initiation among pulmonary TB patients who were microbiologically cured at the end of treatment. RESULTS: The 3-gene signature expression level (TB score) changed significantly over time with respect to baseline among 31 pulmonary TB patients. The greatest increase in TB score occurred within the first month of treatment (median fold-increase in TB score: 1.08 [IQR 0.54-1.52]) and plateaued after 4 months of treatment (median TB score: 1.97 [IQR: 1.03-2.33]). The rapid and substantial increase of the TB score in the first month of treatment holds promise for the early identification of patients that respond to TB treatment. The plateau in TB score at 4 months may indicate early clearance of disease and could direct treatment to be shortened. These hypotheses need to be further explored with larger prospective treatment monitoring studies.

Can we predict the severe course of COVID-19 - a systematic review and meta-analysis of indicators of clinical outcome?

BACKGROUND: COVID-19 has been reported in over 40million people globally with variable clinical outcomes. In this systematic review and meta-analysis, we assessed demographic, laboratory and clinical indicators as predictors for severe courses of COVID-19. METHODS: This systematic review was registered at PROSPERO under CRD42020177154. We systematically searched multiple databases (PubMed, Web of Science Core Collection, MedRvix and bioRvix) for publications from December 2019 to May 31st 2020. Random-effects meta-analyses were used to calculate pooled odds ratios and differences of medians between (1) patients admitted to ICU versus non-ICU patients and (2) patients who died versus those who survived. We adapted an existing Cochrane risk-of-bias assessment tool for outcome studies. RESULTS: Of 6,702 unique citations, we included 88 articles with 69,762 patients. There was concern for bias across all articles included. Age was strongly associated with mortality with a difference of medians (DoM) of 13.15 years (95% confidence interval (CI) 11.37 to 14.94) between those who died and those who survived. We found a clinically relevant difference between non-survivors and survivors for C-reactive protein (CRP; DoM 69.10 mg/L, CI 50.43 to 87.77), lactate dehydrogenase (LDH; DoM 189.49 U/L, CI 155.00 to 223.98), cardiac troponin I (cTnI; DoM 21.88 pg/mL, CI 9.78 to 33.99) and D-Dimer (DoM 1.29mg/L, CI 0.9 to 1.69). Furthermore, cerebrovascular disease was the co-morbidity most strongly associated with mortality (Odds Ratio 3.45, CI 2.42 to 4.91) and ICU admission (Odds Ratio 5.88, CI 2.35 to 14.73). DISCUSSION: This comprehensive meta-analysis found age, cerebrovascular disease, CRP, LDH and cTnI to be the most important risk-factors that predict severe COVID-19 outcomes and will inform clinical scores to support early decision-making.

Can we predict the severe course of COVID-19 - a systematic review and meta-analysis of indicators of clinical outcome?

Background: COVID-19 has been reported in over 40million people globally with variable clinical outcomes. In this systematic review and meta-analysis, we assessed demographic, laboratory and clinical indicators as predictors for severe courses of COVID-19. Methods: We systematically searched multiple databases (PubMed, Web of Science Core Collection, MedRvix and bioRvix) for publications from December 2019 to May 31st 2020. Random-effects meta-analyses were used to calculate pooled odds ratios and differences of medians between (1) patients admitted to ICU versus non-ICU patients and (2) patients who died versus those who survived. We adapted an existing Cochrane risk-of-bias assessment tool for outcome studies. Results: Of 6,702 unique citations, we included 88 articles with 69,762 patients. There was concern for bias across all articles included. Age was strongly associated with mortality with a difference of medians (DoM) of 13.15 years (95% confidence interval (CI) 11.37 to 14.94) between those who died and those who survived. We found a clinically relevant difference between non-survivors and survivors for C-reactive protein (CRP; DoM 69.10, CI 50.43 to 87.77), lactate dehydrogenase (LDH; DoM 189.49, CI 155.00 to 223.98), cardiac troponin I (cTnI; DoM 21.88, CI 9.78 to 33.99) and D-Dimer (DoM 1.29mg/L, CI 0.9 - 1.69). Furthermore, cerebrovascular disease was the co-morbidity most strongly associated with mortality (Odds Ratio 3.45, CI 2.42 to 4.91) and ICU admission (Odds Ratio 5.88, CI 2.35 to 14.73). Discussion: This comprehensive meta-analysis found age, cerebrovascular disease, CRP, LDH and cTnI to be the most important risk-factors in predicting severe COVID-19 outcomes and will inform decision analytical tools to support clinical decision-making.

Diagnostic accuracy of 3 urine lipoarabinomannan tuberculosis assays in HIV-negative outpatients.

BACKGROUNDInadequate tuberculosis (TB) diagnostics are a major hurdle in the reduction of disease burden, and accurate point-of-care tests (POCTs) are urgently needed. We assessed the diagnostic accuracy of Fujifilm SILVAMP TB lipoarabinomannan (FujiLAM) POCT for TB diagnosis in HIV-negative outpatients and compared it with Alere Determine TB LAM Ag (AlereLAM) POCT and a laboratory-based ultrasensitive electrochemiluminescence LAM research assay (EclLAM).METHODSIn this multicenter diagnostic test accuracy study, we recruited HIV-negative adults with symptoms suggestive of pulmonary TB presenting to outpatient health care centers in Peru and South Africa. Urine samples were tested using FujiLAM, AlereLAM, and EclLAM, and the diagnostic accuracy was assessed against a microbiological reference standard (MRS) and a composite reference standard.RESULTSThree hundred seventy-two HIV-negative participants were included and the prevalence of microbiologically confirmed TB was 30%. Compared with the MRS, the sensitivities of AlereLAM, FujiLAM, and EclLAM were 10.8% (95% confidence interval [CI] 6.3%-18.0%), 53.2% (95% CI 43.9%-62.1%), and 66.7% (95% CI 57.5%-74.7%), respectively. The specificities of AlereLAM, FujiLAM, and EclLAM were 92.3% (95% CI 88.5%-95.0%), 98.9% (95% CI 96.7%-99.6%), and 98.1% (95% CI 95.6%-99.2%), respectively. Positive likelihood ratios of AlereLAM, FujiLAM, and EclLAM were 1.4, 46.2, and 34.8, respectively, and positive predictive values were 37.5%, 95.2%, and 93.7%, respectively.CONCLUSIONCompared with AlereLAM, FujiLAM detected 5 times more patients with TB in HIV-negative participants, had a high positive predictive value, and has the potential to improve rapid diagnosis of TB at the point-of-care. EclLAM demonstrated that additional sensitivity gains are possible, which highlights LAM's potential as a biomarker. Additional research is required to assess FujiLAM's performance in prospective cohorts, its cost-effectiveness, and its impact in real-world clinical settings.FUNDINGGlobal Health Innovative Technology Fund, the UK Department for International Development, the Dutch Ministry of Foreign Affairs, the Bill and Melinda Gates Foundation, the Australian Department of Foreign Affairs and Trade, the German Federal Ministry of Education and Research through Kreditanstalt für Wiederaufbau, and the NIH and National Institute of Allergy and Infectious Diseases.