Cough Diagnosis: Present and Future
https://doi.org/10.21292/2075-1230-2021-99-11-56-64
Abstract
Chronic cough is a common symptom of numerous diseases occurring in about 10% of general population. The number of cough impulses over a period of time is an objective marker of cough severity. Cough frequency is now considered the primary endpoint in studies of the effectiveness of cough suppressants, as a factor contributing to the spread of tuberculosis, and as one of the indicators of patient stabilization during exacerbations of chronic obstructive pulmonary disease. The review discusses data from 60 literature sources on the principles of automatic cough impulses counting, methods used for objective cough assessment, and forecasts for future development in this field.
About the Authors
E. S. Ovsyannikov
Voronezh State Medical University named after N. N. Burdenko
Russian Federation
Russian Federation
Evgeny S. Ovsyannikov – Candidate of Medical Sciences, Associate Professor of Faculty Therapy Department.
10, Studencheskaya St., Voronezh, 394036.
S. N. Аvdeev
I. M. Sechenov First Moscow State Medical University (Sechenov University)
Russian Federation
Russian Federation
Sergey N. Avdeev - Doctor of Medical Sciences, Correspondent Member of RAS, Head of Pulmonology Department of General Medicine Faculty.
8, Bd. 2, Trubetskaya St., Moscow, 119991.
A. V. Budnevskiy
Voronezh State Medical University named after N. N. Burdenko
Russian Federation
Russian Federation
Andrey V. Budnevskiy - Doctor of Medical Sciences, Professor, Head of Faculty Therapy Department.
10, Studencheskaya St., Voronezh, 394036.
E. S. Drobyshevа
Voronezh State Medical University named after N. N. Burdenko
Russian Federation
Russian Federation
Elena S. Drobysheva – Candidate of Medical Sciences, Associate Professor of Faculty Therapy Department.
10, Studencheskaya St., Voronezh, 394036.
References
1. Budnevskiy А.V., Ovsyannikov E.S., Labzhaniya N.V. Chronic obstructive pulmonary disease concurrent with metabolic syndrome: pathophysiological and clinical features. Terapevticheskiy Arkhiv, 2017, 89(1), pp. 123-127. (In Russ.) doi: 10.17116/terarkh2017891123-127.
2. Delyagin V.M. Choice of therapy for coughing (spiral development).
3. Meditsinsky Soviet, 2019, no. 11, pp. 60-66. (In Russ.) doi: 10.21518/2079-701X-2019-11-60-66.
4. Zaytsev А.А. Cough: through the pages of international recommendations. Effektivnaya Farmakoterapiya, 2019, vol. 15, no. 27, pp. 38-49. (In Russ.) doi: 10.33978/2307-3586-2019-15-27-38-48.
5. Leschenko I.V., Tsarkova S.А., Zherebtsov А.D. Age-related aspects of differential diagnosis of acute cough in children and adults. Pulmonologiya, 2018, vol. 28, no. 4, pp. 461-468. (In Russ.) doi: 10.18093/0869-0189-2017-28-4-461-468.
6. Ovsyannikov E.S., Аvdeev S.N., Budnevskiy А.V., Shkatova Ya.S. The objective evaluation of cough in patients with chronic obstructive pulmonary disease and obesity. Sistemny Analiz I Upravlenie V Biomeditsinskikh Sistemakh, 2019, vol. 18, no. 3, pp. 18-25. (In Russ.) doi: 10.25987/VSTU.2019.18.3.002.
7. Orlova N.V. Chronic cough: differential diagnosis and treatment. Meditsinsky Soviet, 2020, vol. 17, pp. 124-131. (In Russ.) doi: 10.21518/2079-701X-2020-17-124-131.
8. Abdulqawi R., Dockry R., Holt K. et al. P2X3 receptor antagonist (AF-219) in refractory chronic cough: A randomised, double-blind, placebo-controlled phase 2 study. Lancet, 2015, vol. 385 (9974), pp. 1198-1205. doi: 10.1016/S0140-6736(14)61255-1.
9. Amoh J., Odame K. Deep neural networks for identifying cough sounds. IEEE Trans Biomed Circuits Syst., 2016, vol. 10, pp. 1003-1011. doi: 10.1109/TBCAS.2016.2598794.
10. Barry S.J., Dane A.D., Morice A.H. et al. The automatic recognition and counting of cough. Cough, 2006, vol. 2, no. 1, pp. 8, doi: 10.1186/1745-9974-2-8.
11. Barton A., Gaydecki P., Holt K. et al. Data reduction for cough studies using distribution of audio frequency content. Cough, 2012, vol. 8, no. 12, pp. 12. doi: 10.1186/1745-9974-8-12.
12. Birring S.S., Fleming T., Matos S. et al. The Leicester Cough Monitor: preliminary validation of an automated cough detection system in chronic cough. Eur. Respir. J., 2008, vol. 31, no. 5, pp. 1013-1038. doi: 10.1183/09031936.00057407.
13. Birring S.S., Parker D., Brightling C.E. et al. Induced sputum inflammatory mediator concentrations in chronic cough. Am. J. Respir. Crit. Care Med., 2004, vol. 169, no. 1, pp. 15-19. doi: 10.1164/rccm.200308-1092OC.
14. Birring S.S., Prudon B., Carr A. et al. Development of a symptom specific health status measure for patients with chronic cough: Leicester Cough Questionnaire (LCQ). Thorax, 2003, vol. 58, no. 4, pp. 339-343. doi: 10.1136/thorax.58.4.339.
15. Birring S.S., Mann V.M., Matos S. et al. From the authors (response to: The Leicester Cough Monitor: a semi-automated, semi-validated cough detection system?). Eur. Respir. J., 2008, vol. 32, pp. 530-531. doi: 10.1183/09031936.00060808.
16. Birring S.S., Wijsenbeek M.S., Agrawal S. et al. A novel formulation of inhaled sodium cromoglicate (PA101) in idiopathic pulmonary fibrosis and chronic cough: a randomised, double-blind, proof-of-concept, phase 2 trial. Lancet Respir. Med., 2017, vol. 5, no. 10, pp. 806-815. doi: 10.1016/S2213-260(17)30310-7.
17. Canning B.J., Chang A.B., Bolser D.C. et al. Anatomy and Neurophysiology of Cough. Chest, 2014, vol. 146, no. 6, pp. 1633-1648. 10.1378/chest.14-1481.
18. Cho P.S.P., Birring S.S., Fletcher H. et al. Methods of cough assessment. J. Allergy Clin. Immunol.: In Pract., 2019, vol. 7, no. 6, pp. 1715-1723. doi: 10.1016/j.jaip.2019.01.049.
19. Cho P.S.P., Fletcher H.V., Patel I.S. et al. Cough reflex sensitivity in exacerbations of chronic obstructive pulmonary disease. Eur. Respir. Society (ERS), 2018, vol. 52, PA4056. doi: 10.1183/13993003.congress-2018.PA405695).
20. Coyle M.A., Keenan D.B., Henderson L.S. et al. Evaluation of an ambulatory system for the quantification of cough frequency in patients with chronic obstructive pulmonary disease. Cough, 2005, vol. 4, no. 1, pp. 3. doi: 10.1186/1745-9974-1-3.
21. Crooks M.G., Hayman Y., Innes A. et al. Objective measurement of cough frequency during COPD exacerbation convalescence. Lung, 2016, vol. 194, pp. 117-120. doi: 10.1007/s00408-015-9782-y.
22. de Koning H.J., Van Der Aalst C.M., De Jong P.A. et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N. Engl. J. Med., 2020, vol. 382, pp. 503-513. doi: 10.1056/NEJMoa1911793.
23. Drugman T., Urbain J., Bauwens N. et al. Objective study of sensor relevance for automatic cough detection. IEEE J. Biomed. Health Inform., 2013, vol. 17, no. 3, pp. 699-707. doi: 10.1109/JBHI.2013.2239303.
24. Elghamoudi D.D., Sumner H., McGuiness K. et al. The feasibility and validity of objective cough monitoring in children using an adult cough detection system. Thorax, 2015, vol. 70, no. 3, pp. A198.1-A198. doi: 10.1136/thoraxjnl-2015-207770.377.
25. French C.T., Irwin R.S., Fletcher K.E. et al. Evaluation of a cough-specific quality-of-life questionnaire. Chest, 2002, vol. 121, no. 4, pp. 1123-1131. doi: 10.1378/chest.121.4.1123.
26. Hoehl S., Berger A., Kortenbusch M. et al. Evidence of SARS-CoV-2 infection in returning travelers from Wuhan, China. N. Engl. J. Med., 2020, vol. 382, pp. 1278-1280. doi:10.1056/NEJMc2001899.
27. Hoyos-Barcelo C., Monge-Alvarez J., Zeeshan Shakir M. et al. Efficient k-NN Implementation for real-time detection of cough events in smartphones. IEEE J. Biomed Health Inform., 2018, vol. 22, no. 5, pp. 1662-1671. doi: 10.1109/JBHI.2017.2768162.
28. Kulnik S.T., Williams N.M., Kalra L. et al. Cough frequency monitors: Can they discriminate patient from environmental coughs? J. Thorac. Dis., 2016, vol. 8, no. 11, pp. 3152-3159. doi: 10.21037/jtd.2016.11.02.
29. Kvapilova L., Boza V., Dubec P.J. et al. Continuous sound collection using smartphones and machine learning to measure cough. Digit Biomark., 2019, vol. 3, pp. 166-175. doi: 10.1159/000504666.
30. Larson S., Comina G., Gilman R.H. et al. Validation of an automated cough detection algorithm for tracking recovery of pulmonary tuberculosis patients. PLoS One, 2012, vol. 7, no. 10, pp. e46229. doi: 10.1371/journal.pone.0046229.
31. Lee K. K., Matos S. Ward K. et al. Sound: A non-invasive measure of cough intensity. BMJ Open Respir. Re., 2017, vol. 4, no. 1. e000178. doi: 10.1136/bmjresp-2017-000178.
32. Lee K.K., Birring S.S. Cough and sleep. Lung, 2010, vol. 188, no. 1, pp. S91-S94. doi: 10.1007/s00408-009-9176-0.
33. Lee K.K., Savani A., Matos S. et al. Four-hour cough frequency monitoring in chronic cough. Chest, 2012, vol. 142, no. 5, pp. 1237-1243. doi: 10.1378/chest.11-3309.
34. Liu J.-M., You M., Wang Z. et al. Cough event classification by pretrained deep neural network. BMC Med. Inform. Decis Mak., 2015, vol. 15, S2. doi: 10.1186/1472-6947-15-S4-S2.
35. Marsden P.A., Satia I., Ibrahim B. et al. Objective cough frequency, airway inflammation, and disease control in asthma. Chest, 2016, vol. 149, no. 6, pp. 1460-1466. doi: 10.1016/j.chest.2016.02.676.
36. McGuinness K., Holt K., Dockry R. et al. P159 Validation of the VitaloJAK™ 24 hour ambulatory cough monitor. Thorax, 2012, vol. 67, no. 2, A159. doi: 10.1136/thoraxjnl-2012-202678.220.
37. McGuinness K., Ward K., Reilly C.C. et al. Muscle activation and sound during voluntary single coughs and cough peals in healthy volunteers: Insights into cough intensity. Respir. Physiol. Neurobiol., 2018, vol. 257, pp. 42-50. doi: 10.1016/j.resp.2018.02.014.
38. Mohammadi H., Samadani A.A., Steele C. et al. Automatic discrimination between cough and non-cough accelerometry signal artefacts. Biomed Signal Process Control, 2019, vol. 52, pp. 394-402. doi: 10.1016/j.bspc.2018.10.013.
39. Pavesi L., Subburaj S., Porter-Shaw K. Application and validation of a computerized cough acquisition system for objective monitoring of acute cough: A meta-analysis. Chest, 2001, vol. 120, pp. 1121-1128. doi: 10.1378/chest.120.4.1121.
40. Perez M.V., Mahaffey K.W., Hedlin H. et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. New Engl. J. Med., 2019, vol. 381, pp. 1909-1917. doi: 10.1056/NEJMoa1901183.
41. Portnoy J.M., Waller M., De Lurgio S. et al. Telemedicine is as effective as in-person visits for patients with asthma. Ann. Allergy Asthma Immunol., 2016, vol. 117, pp. 241-245. doi: 10.1016/j.anai.2016.07.012.
42. Proaño A., Bravard M.A., López J.W. et al. Dynamics of cough frequency in adults undergoing treatment for pulmonary tuberculosis. Clin. Infect. Dis., 2017, vol. 64, no. 9, pp. 1174-1181. DOI:0.1093/cid/cix039.
43. Proaño A., Bravard M.A., Tracey B.H. et al. Protocol for studying cough frequency in people with pulmonary tuberculosis. BMJ Open, 2016, vol. 6, no. 4, pp. e010365. doi: 10.1136/bmjopen-2015-010365.
44. Rao A., Huynh E., Royston T.J. et al. Acoustic methods for pulmonary diagnosis. IEEE Rev Biomed Eng., 2019, vol. 12, pp. 221-239. doi: 10.1109/RBME.2018.2874353.
45. Ryan N.M., Birring S.S., Gibson P.G. Gabapentin for refractory chronic cough: A randomised, double-blind, placebo-controlled trial. Lancet, 2012, vol. 380, pp. 1583-1589. doi: 10.1016/S0140-6736(12)60776-4.
46. Sinha A., Lee K.K., Rafferty G.F. et al. Predictors of objective cough frequency in pulmonary sarcoidosis. Eur. Respir. J., 2016, vol. 47, pp. 1461-1471.(94). doi: 10.1183/13993003.01369-2015.
47. Smartphone users worldwide 2020. Statista [Internet]. Accessed January 26, 2021. https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/
48. Smith J., Woodcock A. New Developments in the objective assessment of cough. Lung, 2008, vol. 186 (S1), pp. 48-54. doi: 10.1007/s00408-007-9059-1.
49. Smith J.A., Kitt M.M., Morice A.H. et al. Gefapixant, a P2X3 receptor antagonist, for the treatment of refractory or unexplained chronic cough: a randomised, double-blind, controlled, parallel-group, phase 2b trial. Lancet Respir. Med., 2020, vol. 8, no. 8, pp. 775-785. doi: 10.1016/S2213-2600(19)30471-0.
50. Smith J.A., Owen E.C., Jones A.M. et al. Objective measurement of cough during pulmonary exacerbations in adults with cystic fibrosis. Thorax, 2006, vol. 61, no. 5, pp. 425-429. doi: 10.1136/thx.2005.050963.
51. Spinou A., Lee K.K., Sinha A. et al. The objective assessment of cough frequency in bronchiectasis. Lung, 2017, vol. 195, no. 5, pp. 575-585. doi: 10.1007/s00408-017-0038-x.
52. Turner R.D., Birring S.S., Darmalingam M. et al. Daily cough frequency in tuberculosis and association with household infection. Int. J. Tuberc. Lung Dis., 2018, vol. 22, no. 8, pp. 863-870. doi: 10.5588/ijtld.17.0652.
53. Turner R.D., Bothamley G.H. How to count coughs? Counting by ear, the effect of visual data and the evaluation of an automated cough monitor. Respir. Med., 2014, vol. 108, no. 12, pp. 1808-1815. DOI:10.1016/j.rmed.2014.10.003.
54. Turner R.D. Cough in pulmonary tuberculosis: Existing knowledge and general insights. Pulm. Pharmacol. Ther., 2019, vol. 55, pp. 89-94. doi: 10.1016/j.pupt.2019.01.008.
55. Vernon M., Kline Leidy N., Nacson A., Nelsen L. Measuring cough severity: development and pilot testing of a new seven-item cough severity patient-reported outcome measure. Ther. Adv. Respir. Dis., 2010, vol. 4, no. 4, pp. 199-208. doi: 10.1177/1753465810372526.
56. Vizel E., Yigla M., Goryachev Y. et al. Validation of an ambulatory cough detection and counting application using voluntary cough under different conditions. Cough, 2010, vol. 6, no. 1, pp. 3. doi: 10.1186/1745-9974-6-3.
57. Williams C.M., Abdulwhhab M., Birring S.S. et al. Exhaled Mycobacterium tuberculosis output and detection of subclinical disease by face-mask sampling: prospective observational studies. Lancet Infect. Dis., 2020, vol. 20, no. 5, pp. 607-617. doi: 0.1016/S1473-3099(19)30707-8.
58. Windmon A., Minakshi M., Bhart P. et al. TussisWatch: A smart-phone system to identify cough episodes as early symptoms of chronic obstructive pulmonary disease and congestive heart failure. IEEE J. Biomed. Health Inform., 2019, vol. 23, no. 4, pp. 1566-1573. doi: 10.1109/JBHI.2018.2872038.
59. Yousaf N., Monteiro W., Matos S. et al. Cough frequency in health and disease. Eur. Respir. J., 2013, vol. 41, no. 1, pp. 241-243. doi: 10.1183/09031936.00089312.
60. Yousaf N., Monteiro W., Parker D. et al. Long-term low-dose erythromycin in patients with unexplained chronic cough: A double-blind placebo controlled trial. Thorax, 2010, vol. 65, no. 12, pp. 1107-1110. doi: 10.1136/thx.2010.142711.
61. Yuen C.M., Amanullah F., Dharmadhikari A. et al. Turning off the tap: Stopping tuberculosis transmission through active case-finding and prompt effective treatment. Lancet, 2015, no. 386, pp. 2334-2343. doi: 10.1016/S0140-6736(15)00322-0.
Review
For citations:
Ovsyannikov E.S., Аvdeev S.N., Budnevskiy A.V., Drobyshevа E.S. Cough Diagnosis: Present and Future. Tuberculosis and Lung Diseases. 2021;99(11):56-64. (In Russ.) https://doi.org/10.21292/2075-1230-2021-99-11-56-64
Views:
832
Email this article 