Making cough count in tuberculosis care

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Abstract

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.

Abstract

Zimmer et al. discuss the importance of cough assessment in clinical management of tuberculosis (TB). They describe how acoustic epidemiology, which uses recording devices and artificial intelligence to detect, record and analyze cough, can be used in TB control and individual patient management.

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DNA Sequencing Predicts 1st-Line Tuberculosis Drug Susceptibility Profiles

Caroline Allix-Beguec, Irena Arandjelovic, Lijun Bi … (2018)

Background The World Health Organization recommends universal drug susceptibility testing for Mycobacterium tuberculosis complex to guide treatment decisions and improve outcomes. We assessed whether DNA sequencing can accurately predict antibiotic susceptibility profiles for first-line anti-tuberculosis drugs. Methods Whole-genome sequences and associated phenotypes to isoniazid, rifampicin, ethambutol and pyrazinamide were obtained for isolates from 16 countries across six continents. For each isolate, mutations associated with drug-resistance and drug-susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These were predicted to be pan-susceptible if predicted susceptible to isoniazid and to other drugs, or contained mutations of unknown association in genes affecting these other drugs. We simulated how negative predictive value changed with drug-resistance prevalence. Results 10,209 isolates were analysed. The greatest proportion of phenotypes were predicted for rifampicin (9,660/10,130; (95.4%)) and the lowest for ethambutol (8,794/9,794; (89.8%)). Isoniazid, rifampicin, ethambutol and pyrazinamide resistance was correctly predicted with 97.1%, 97.5% 94.6% and 91.3% sensitivity, and susceptibility with 99.0%, 98.8%, 93.6% and 96.8% specificity, respectively. 5,250 (89.5%) drug profiles were correctly predicted for 5,865/7,516 (78.0%) isolates with complete phenotypic profiles. Among these, 3,952/4,037 (97.9%) predictions of pan-susceptibility were correct. The negative predictive value for 97.5% of simulated drug profiles exceeded 95% where the prevalence of drug-resistance was below 47.0%. Conclusions Phenotypic testing for first-line drugs can be phased down in favour of DNA sequencing to guide anti- tuberculosis drug therapy.

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Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.

Stefan Jaeger, Sema Candemir, Sameer Antani … (2014)

The U.S. National Library of Medicine has made two datasets of postero-anterior (PA) chest radiographs available to foster research in computer-aided diagnosis of pulmonary diseases with a special focus on pulmonary tuberculosis (TB). The radiographs were acquired from the Department of Health and Human Services, Montgomery County, Maryland, USA and Shenzhen No. 3 People's Hospital in China. Both datasets contain normal and abnormal chest X-rays with manifestations of TB and include associated radiologist readings.

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Development of a symptom specific health status measure for patients with chronic cough: Leicester Cough Questionnaire (LCQ).

S S Birring, B Prudon, A. J. Carr … (2003)

Chronic cough is a common condition which has a significant impact on quality of life. Assessment and management are hampered by the absence of well validated outcome measures. The development and validation of the Leicester Cough Questionnaire (LCQ), a self-completed health related quality of life measure of chronic cough, is presented. Patients with chronic cough were recruited from outpatient clinics. The development of the LCQ consisted of three phases: phase 1 (item generation); phase 2 (item reduction, allocation of items to domains and validation of questionnaire); phase 3 (repeatability and responsiveness testing of final version of questionnaire). Phase 1: Literature review, multidisciplinary team meeting and 15 structured interviews with chronic cough patients generated 44 items (LCQ1) with a 7 point Likert response scale. Phase 2: 104 chronic cough outpatients completed the LCQ1 along with an importance rating for each item. The clinical impact factor method was used for item reduction to 19 items (LCQ2: final version). These items were divided into three domains (physical, psychological and social) following expert opinion. Internal reliability, as assessed using Cronbach's alpha coefficients, varied between 0.79 and 0.89. Concurrent validity was high when the LCQ2 (n=56) was compared with a cough visual analogue score (r=-0.72). There was a moderate relationship with response to the St George's Respiratory Questionnaire (r=-0.54) and SF36 total score (r=0.46). Phase 3: Two week repeatability (n=24) was high with intraclass correlation coefficients for domains varying between 0.88 and 0.96. Responsiveness in nine patients whose cough was successfully treated varied within domains from an effect size of 0.84 to 1.75. The LCQ is a valid, repeatable 19 item self-completed quality of life measure of chronic cough which is responsive to change. It should be a useful tool in clinical trials and longitudinal studies.

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Author and article information

Contributors

Simon Grandjean Lapierre:

ORCID: http://orcid.org/0000-0003-3646-1573

simon.grandjean.lapierre@umontreal.ca

Journal

Journal ID (nlm-ta): Commun Med (Lond)

Journal ID (iso-abbrev): Commun Med (Lond)

Title: Communications Medicine

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2730-664X

Publication date (Electronic): 6 July 2022

Publication date PMC-release: 6 July 2022

Publication date Collection: 2022

Volume: 2

Electronic Location Identifier: 83

Affiliations

[1 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Department of Epidemiology, Biostatistics and Occupational Health, , McGill University, ; Montreal, Canada

[2 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, McGill International TB Centre, ; Montreal, Canada

[3 ]GRID grid.11100.31, ISNI 0000 0001 0673 9488, School of Medicine, , Universidad Peruana Cayetano Heredia, ; Lima, Peru

[4 ]GRID grid.11100.31, ISNI 0000 0001 0673 9488, Instituto de Medicina Tropical Alexander von Humboldt, , Universidad Peruana Cayetano Heredia, ; Lima, Peru

[5 ]GRID grid.508695.7, Docturnal Pvt. Ltd., R&D, ; Hyderabad, India

[6 ]GRID grid.418309.7, ISNI 0000 0000 8990 8592, Bill & Melinda Gates Foundation, ; Seattle, WA USA

[7 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Medicine, Division of Pulmonary & Critical Care Medicine, , University of California, San Francisco, ; 1001 Potrero Avenue, San Francisco, CA 94110 USA

[8 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Center for Tuberculosis, , University of California, San Francisco, ; 1001 Potrero Avenue, San Francisco, CA 94110 USA

[9 ]GRID grid.410559.c, ISNI 0000 0001 0743 2111, Immunopathology Axis, , Centre de Recherche du Centre Hospitalier de l’Université de Montréal, ; 900 Rue Saint-Denis, Montréal, QC Canada

[10 ]GRID grid.14848.31, ISNI 0000 0001 2292 3357, Department of Microbiology, Infectious Diseases and Immunology, , Université de Montréal, ; 2900 Boulevard Edouard-Montpetit, Montréal, QC Canada

Author information

Alexandra J. Zimmer http://orcid.org/0000-0002-6047-0923

Adithya Cattamanchi http://orcid.org/0000-0002-6553-2601

Simon Grandjean Lapierre http://orcid.org/0000-0003-3646-1573

Article

Publisher ID: 149

DOI: 10.1038/s43856-022-00149-w

PMC ID: 9258463

PubMed ID: 35814294

SO-VID: 632a208c-1bb2-4dc6-8a90-263d8a1b76bd

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 15 September 2021

Date accepted : 21 June 2022

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Keywords: diagnostic markers,prognostic markers,tuberculosis

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Keywords: diagnostic markers, prognostic markers, tuberculosis

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Making cough count in tuberculosis care

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Point-Of-Care Testing for Tuberculosis

Most cited references 54

DNA Sequencing Predicts 1st-Line Tuberculosis Drug Susceptibility Profiles

Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.

Development of a symptom specific health status measure for patients with chronic cough: Leicester Cough Questionnaire (LCQ).

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Cited by 9

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