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      Reimagining the status quo: How close are we to rapid sputum-free tuberculosis diagnostics for all?

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          Summary

          Rapid, accurate, sputum-free tests for tuberculosis (TB) triage and confirmation are urgently needed to close the widening diagnostic gap. We summarise key technologies and review programmatic, systems, and resource issues that could affect the impact of diagnostics. Mid-to-early-stage technologies like artificial intelligence-based automated digital chest X-radiography and capillary blood point-of-care assays are particularly promising. Pitfalls in the diagnostic pipeline, included a lack of community-based tools. We outline how these technologies may complement one another within the context of the TB care cascade, help overturn current paradigms (eg, reducing syndromic triage reliance, permitting subclinical TB to be diagnosed), and expand options for extra-pulmonary TB. We review challenges such as the difficulty of detecting paucibacillary TB and the limitations of current reference standards, and discuss how researchers and developers can better design and evaluate assays to optimise programmatic uptake. Finally, we outline how leveraging the urgency and innovation applied to COVID-19 is critical to improving TB patients’ diagnostic quality-of-care.

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          Most cited references109

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          Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use

          Pulse oximetry is routinely used to non-invasively monitor oxygen saturation levels. A low oxygen level in the blood means low oxygen in the tissues, which can ultimately lead to organ failure. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous oxygen saturation time series variability analysis. The primary objective of this research was to identify, implement and validate key digital oximetry biomarkers (OBMs) for the purpose of creating a standard and associated reference toolbox for continuous oximetry time series analysis. We review the sleep medicine literature to identify clinically relevant OBMs. We implement these biomarkers and demonstrate their clinical value within the context of obstructive sleep apnea (OSA) diagnosis on a total of n = 3806 individual polysomnography recordings totaling 26,686 h of continuous data. A total of 44 digital oximetry biomarkers were implemented. Reference ranges for each biomarker are provided for individuals with mild, moderate, and severe OSA and for non-OSA recordings. Linear regression analysis between biomarkers and the apnea hypopnea index (AHI) showed a high correlation, which reached \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline R ^2 = 0.82$$\end{document} R ¯ 2 = 0.82 . The resulting python OBM toolbox, denoted “pobm”, was contributed to the open software PhysioZoo (physiozoo.org). Studying the variability of the continuous oxygen saturation time series using pbom may provide information on the underlying physiological control systems and enhance our understanding of the manifestations and etiology of diseases, with emphasis on respiratory diseases.
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            Swabs Collected by Patients or Health Care Workers for SARS-CoV-2 Testing

            To the Editor: The early medical response to the Covid-19 pandemic in the United States was limited in part by the availability of testing. Health care workers collected a swab sample from the patients’ oropharynx or nasopharynx according to testing guidelines for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. This procedure potentially increased the risk of transmission of the virus to health care workers who lacked sufficient personal protective equipment (PPE). 1 In other clinical conditions, 2,3 it is faster to obtain a tongue, nasal, or mid-turbinate sample than a nasopharyngeal sample, with less potential for the patient to sneeze, cough, or gag. In addition, recent data support the validity of non-nasopharyngeal samples for detection of SARS-CoV-2. 4,5 Collection by the patient reduces high exposure of the health care worker to the virus and preserves limited PPE. We obtained swab samples from the nasopharynx and from at least one other location in 530 patients with symptoms indicative of upper respiratory infection who were seen in any one of five ambulatory clinics in the Puget Sound region of Washington. Patients were provided with instructions and asked to collect tongue, nasal, and mid-turbinate samples, in that order. A nasopharyngeal sample was then collected from the patient by a health care worker. All samples were submitted to a reference laboratory for reverse-transcriptase–polymerase-chain-reaction (RT-PCR) testing that yielded qualitative results (positive or negative) and cycle threshold (Ct) values for positive samples only (additional details are provided in the Methods section in the Supplementary Appendix, available with the full text of this letter at NEJM.org). Our study was powered on the basis of a one-sided test to determine whether the sensitivities of the non-nasopharyngeal swabs collected by the patients themselves were significantly greater than 90%. We calculated that 48 patients with positive nasopharyngeal samples would be needed for the study, assuming a true sensitivity of 98% with 80% power. Pairwise analyses were conducted to compare each sample collected by the patient with the nasopharyngeal sample collected by a health care worker. Of the 501 patients with both tongue and nasopharyngeal samples, both swabs tested negative in 450 patients, both swabs tested positive in 44, the nasopharyngeal swab was positive and the tongue swab was negative in 5, and the tongue swab was positive and the nasopharyngeal swab was negative in 2. Of the 498 patients with both nasal and nasopharyngeal samples, both swabs were negative in 447, both swabs were positive in 47, the nasopharyngeal swab was positive and the nasal swab was negative in 3, and the nasal swab was positive and the nasopharyngeal swab was negative in 1. Of the 504 patients with both mid-turbinate and nasopharyngeal samples, both swabs were negative in 452, both swabs were positive in 50, and the nasopharyngeal swab was positive and the mid-turbinate swab was negative in 2; none of these patients had a positive mid-turbinate swab and a negative nasopharyngeal swab. When a nasopharyngeal sample collected by a health care worker was used as the comparator, the estimated sensitivities of the tongue, nasal, and mid-turbinate samples collected by the patients were 89.8% (one-sided 97.5% confidence interval [CI], 78.2 to 100.0), 94.0% (97.5% CI, 83.8 to 100.0), and 96.2% (97.5% CI, 87.0 to 100.0), respectively. Although the estimated sensitivities of the nasal and mid-turbinate samples were greater than 90%, all the confidence intervals for the sensitivity of the samples collected by the patients contained 90%. Despite the lack of statistical significance, both the nasal and mid-turbinate samples may be clinically acceptable on the basis of estimated sensitivities above 90% and the 87% lower bound of the confidence interval for the sensitivity of the mid-turbinate sample being close to 90%. Ct values from the RT-PCR tests showed Pearson correlations between the positive results from the nasopharyngeal swab and the positive results from the tongue, nasal, and mid-turbinate swabs of 0.48, 0.78, and 0.86, respectively. Figure 1 shows the Ct values for the sites from the patient-collected swab samples relative to those for the nasopharyngeal swab samples, with a linear regression fit superimposed on the scatterplot. For patients with positive test results from both the nasopharyngeal swab and a tongue, nasal, or mid-turbinate swab, the Ct values for the swabs collected by the patient were less than the Ct values for the nasopharyngeal swab 18.6%, 50.0%, and 83.3% of the time, respectively, indicating that the viral load may be higher in the middle turbinate than in the nasopharynx and equivalent between the nose and the nasopharynx (additional details are provided in the Methods section in the Supplementary Appendix). Our study shows the clinical usefulness of tongue, nasal, or mid-turbinate samples collected by patients as compared with nasopharyngeal samples collected by health care workers for the diagnosis of Covid-19. Adoption of techniques for sampling by patients can reduce PPE use and provide a more comfortable patient experience. Our analysis was cross-sectional, performed in a single geographic region, and limited to single comparisons with the results of nasopharyngeal sampling, which is not a perfect standard test. Despite these limitations, we think that patient collection of samples for SARS-CoV-2 testing from sites other than the nasopharynx is a useful approach during the Covid-19 pandemic.
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              The South African Tuberculosis Care Cascade: Estimated Losses and Methodological Challenges

              Abstract Background While tuberculosis incidence and mortality are declining in South Africa, meeting the goals of the End TB Strategy requires an invigorated programmatic response informed by accurate data. Enumerating the losses at each step in the care cascade enables appropriate targeting of interventions and resources. Methods We estimated the tuberculosis burden; the number and proportion of individuals with tuberculosis who accessed tests, had tuberculosis diagnosed, initiated treatment, and successfully completed treatment for all tuberculosis cases, for those with drug-susceptible tuberculosis (including human immunodeficiency virus (HIV)–coinfected cases) and rifampicin-resistant tuberculosis. Estimates were derived from national electronic tuberculosis register data, laboratory data, and published studies. Results The overall tuberculosis burden was estimated to be 532005 cases (range, 333760–764480 cases), with successful completion of treatment in 53% of cases. Losses occurred at multiple steps: 5% at test access, 13% at diagnosis, 12% at treatment initiation, and 17% at successful treatment completion. Overall losses were similar among all drug-susceptible cases and those with HIV coinfection (54% and 52%, respectively, successfully completed treatment). Losses were substantially higher among rifampicin- resistant cases, with only 22% successfully completing treatment. Conclusion Although the vast majority of individuals with tuberculosis engaged the public health system, just over half were successfully treated. Urgent efforts are required to improve implementation of existing policies and protocols to close gaps in tuberculosis diagnosis, treatment initiation, and successful treatment completion.
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                Author and article information

                Contributors
                Journal
                EBioMedicine
                EBioMedicine
                EBioMedicine
                Elsevier
                2352-3964
                23 March 2022
                April 2022
                23 March 2022
                : 78
                : 103939
                Affiliations
                [a ]Division of Infectious Diseases, Beth Israel Deaconess Medical Center & Harvard Medical School, Boston, USA
                [b ]ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
                [c ]Centro de Investigação em Saude de Manhiça, Maputo, Mozambique.
                [d ]FIND, the global alliance for diagnostics, Geneva, Switzerland
                [e ]Department of Global Health and Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centers, Amsterdam, Netherlands
                [f ]DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
                Author notes
                [* ]Corresponding author. gtheron@ 123456sun.ac.za
                Article
                S2352-3964(22)00123-2 103939
                10.1016/j.ebiom.2022.103939
                9043971
                35339423
                adf856d3-1a21-4493-bead-5e164982f4ba
                © 2022 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 4 January 2022
                : 14 February 2022
                : 28 February 2022
                Categories
                Review

                tuberculosis,diagnosis,active disease,non-sputum,point-of-care

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