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A smartphone-based application for cough counting in patients with acute asthma exacerbation

BACKGROUND: While tools exist for objective cough counting in clinical studies, there is no available tool for objective cough measurement in clinical practice. An artificial intelligence (AI)-based cough count system was recently developed that quantifies cough sounds collected through a smartphone...

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Autores principales: Shim, Ji-Su, Kim, Byung-Keun, Kim, Sae-Hoon, Kwon, Jae-Woo, Ahn, Kyung-Min, Kang, Sung-Yoon, Park, Han-Ki, Park, Heung-Woo, Yang, Min-Suk, Kim, Min-Hye, Lee, Sang Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407484/
https://www.ncbi.nlm.nih.gov/pubmed/37559656
http://dx.doi.org/10.21037/jtd-22-1492
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author Shim, Ji-Su
Kim, Byung-Keun
Kim, Sae-Hoon
Kwon, Jae-Woo
Ahn, Kyung-Min
Kang, Sung-Yoon
Park, Han-Ki
Park, Heung-Woo
Yang, Min-Suk
Kim, Min-Hye
Lee, Sang Min
author_facet Shim, Ji-Su
Kim, Byung-Keun
Kim, Sae-Hoon
Kwon, Jae-Woo
Ahn, Kyung-Min
Kang, Sung-Yoon
Park, Han-Ki
Park, Heung-Woo
Yang, Min-Suk
Kim, Min-Hye
Lee, Sang Min
author_sort Shim, Ji-Su
collection PubMed
description BACKGROUND: While tools exist for objective cough counting in clinical studies, there is no available tool for objective cough measurement in clinical practice. An artificial intelligence (AI)-based cough count system was recently developed that quantifies cough sounds collected through a smartphone application. In this prospective study, this AI-based cough algorithm was applied among real-world patients with an acute exacerbation of asthma. METHODS: Patients with an acute asthma exacerbation recorded their cough sounds for 7 days (2 consecutive hours during awake time and 5 consecutive hours during sleep) using Coughy(TM) smartphone application. During the study period, subjects received systemic corticosteroids and bronchodilator to control asthma. Coughs collected by application were counted by both the AI algorithm and two human experts. Subjects also provided self-measured peak expiratory flow rate (PEFR) and completed other outcome assessments [e.g., cough symptom visual analogue scale (CS-VAS), awake frequency, salbutamol use] to investigate the correlation between cough and other parameters. RESULTS: A total of 1,417.6 h of cough recordings were obtained from 24 asthmatics (median age =39 years). Cough counts by AI were strongly correlated with manual cough counts during sleep time (rho =0.908, P<0.001) and awake time (rho =0.847, P<0.001). Sleep time cough counts were moderately to strongly correlated with CS-VAS (rho =0.339, P<0.001), the frequency of waking up (rho =0.462, P<0.001), and salbutamol use at night (rho =0.243, P<0.001). Weak-to-moderate correlations were found between awake time cough counts and CS-VAS (rho =0.313, P<0.001), the degree of activity limitation (rho =0.169, P=0.005), and salbutamol use at awake time (rho =0.276, P<0.001). Neither awake time nor sleep time cough counts were significantly correlated with PEFR. CONCLUSIONS: The strong correlation between cough counts using the AI-based algorithm and human experts, and other indicators of patient health status provides evidence of the validity of this AI algorithm for use in asthma patients experiencing an acute exacerbation. Study findings suggest that Coughy(TM) could be a novel solution for objectively monitoring cough in a clinical setting.
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spelling pubmed-104074842023-08-09 A smartphone-based application for cough counting in patients with acute asthma exacerbation Shim, Ji-Su Kim, Byung-Keun Kim, Sae-Hoon Kwon, Jae-Woo Ahn, Kyung-Min Kang, Sung-Yoon Park, Han-Ki Park, Heung-Woo Yang, Min-Suk Kim, Min-Hye Lee, Sang Min J Thorac Dis Original Article on Novel Insights into Chronic Cough BACKGROUND: While tools exist for objective cough counting in clinical studies, there is no available tool for objective cough measurement in clinical practice. An artificial intelligence (AI)-based cough count system was recently developed that quantifies cough sounds collected through a smartphone application. In this prospective study, this AI-based cough algorithm was applied among real-world patients with an acute exacerbation of asthma. METHODS: Patients with an acute asthma exacerbation recorded their cough sounds for 7 days (2 consecutive hours during awake time and 5 consecutive hours during sleep) using Coughy(TM) smartphone application. During the study period, subjects received systemic corticosteroids and bronchodilator to control asthma. Coughs collected by application were counted by both the AI algorithm and two human experts. Subjects also provided self-measured peak expiratory flow rate (PEFR) and completed other outcome assessments [e.g., cough symptom visual analogue scale (CS-VAS), awake frequency, salbutamol use] to investigate the correlation between cough and other parameters. RESULTS: A total of 1,417.6 h of cough recordings were obtained from 24 asthmatics (median age =39 years). Cough counts by AI were strongly correlated with manual cough counts during sleep time (rho =0.908, P<0.001) and awake time (rho =0.847, P<0.001). Sleep time cough counts were moderately to strongly correlated with CS-VAS (rho =0.339, P<0.001), the frequency of waking up (rho =0.462, P<0.001), and salbutamol use at night (rho =0.243, P<0.001). Weak-to-moderate correlations were found between awake time cough counts and CS-VAS (rho =0.313, P<0.001), the degree of activity limitation (rho =0.169, P=0.005), and salbutamol use at awake time (rho =0.276, P<0.001). Neither awake time nor sleep time cough counts were significantly correlated with PEFR. CONCLUSIONS: The strong correlation between cough counts using the AI-based algorithm and human experts, and other indicators of patient health status provides evidence of the validity of this AI algorithm for use in asthma patients experiencing an acute exacerbation. Study findings suggest that Coughy(TM) could be a novel solution for objectively monitoring cough in a clinical setting. AME Publishing Company 2023-06-09 2023-07-31 /pmc/articles/PMC10407484/ /pubmed/37559656 http://dx.doi.org/10.21037/jtd-22-1492 Text en 2023 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article on Novel Insights into Chronic Cough
Shim, Ji-Su
Kim, Byung-Keun
Kim, Sae-Hoon
Kwon, Jae-Woo
Ahn, Kyung-Min
Kang, Sung-Yoon
Park, Han-Ki
Park, Heung-Woo
Yang, Min-Suk
Kim, Min-Hye
Lee, Sang Min
A smartphone-based application for cough counting in patients with acute asthma exacerbation
title A smartphone-based application for cough counting in patients with acute asthma exacerbation
title_full A smartphone-based application for cough counting in patients with acute asthma exacerbation
title_fullStr A smartphone-based application for cough counting in patients with acute asthma exacerbation
title_full_unstemmed A smartphone-based application for cough counting in patients with acute asthma exacerbation
title_short A smartphone-based application for cough counting in patients with acute asthma exacerbation
title_sort smartphone-based application for cough counting in patients with acute asthma exacerbation
topic Original Article on Novel Insights into Chronic Cough
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407484/
https://www.ncbi.nlm.nih.gov/pubmed/37559656
http://dx.doi.org/10.21037/jtd-22-1492
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