Differences in acoustic features of cough by pneumonia severity in patients with COVID-19: a cross-sectional study

Abstract

BackgroundAcute respiratory syndrome due to coronavirus 2 (SARS-CoV-2) is characterised by heterogeneous levels of disease severity. It is not necessarily apparent whether a patient will develop a severe disease or not. This cross-sectional study explores whether acoustic properties of the cough sound of patients with coronavirus disease (COVID-19), the illness caused by SARS-CoV-2, correlate with their disease and pneumonia severity, with the aim of identifying patients with a severe disease.MethodsVoluntary cough sounds were recorded using a smartphone in 70 COVID-19 patients within the first 24 h of their hospital arrival, between April 2020 and May 2021. Based on gas exchange abnormalities, patients were classified as mild, moderate, or severe. Time- and frequency-based variables were obtained from each cough effort and analysed using a linear mixed-effects modelling approach.ResultsRecords from 62 patients (37% female) were eligible for inclusion in the analysis, with mild, moderate, and severe groups consisting of 31, 14 and 17 patients respectively. 5 of the parameters examined were found to be significantly different in the cough of patients at different disease levels of severity, with a further 2 parameters found to be affected differently by the disease severity in men and women.ConclusionsWe suggest that all these differences reflect the progressive pathophysiological alterations occurring in the respiratory system of COVID-19 patients, and potentially would provide an easy and cost-effective way to initially stratify patients, identifying those with more severe disease, and thereby most effectively allocate healthcare resources.

Cough Sounds in Screening and Diagnostics: A Scoping Review

“…[24][25][26] For instance, a recent study by Davidson et al sought to classify COVID-19-related pneumonia severity based on cough sounds. 27 Kuluozturk et al used a machine learning model to diagnose COVID-19 as well as heart failure and acute asthma based on cough sounds, 28 whereas Yellapu et al focused on the diagnosis of pulmonary tuberculosis. 29 Novel cough detection algorithms have also continued to be developed, 30,31 and Xu et al proposed a smartphone-based cough sound analysis as a potential home-based pulmonary function test.…”

“…Notably, diagnosis of COVID‐19 from cough sounds using machine learning has remained a frequent focus 24–26 . For instance, a recent study by Davidson et al sought to classify COVID‐19‐related pneumonia severity based on cough sounds 27 . Kuluozturk et al used a machine learning model to diagnose COVID‐19 as well as heart failure and acute asthma based on cough sounds, 28 whereas Yellapu et al focused on the diagnosis of pulmonary tuberculosis 29 .…”