While most persons infected with COVID-19 experience minor symptoms and recover in a matter of weeks, the global pandemic caused by the SARS-CoV-2 virus remains a significant health danger.
Some of those infected may develop more severe illness and pneumonia, resulting in a more bleak outlook.
Although techniques for assessing patients' risk have been created, diagnostic and prognostic tools rely mostly on expensive and less accessible imaging technologies such as radiography, ultrasonography, or computed tomography (CT).
As a result, there is a need for the development of a simpler and more easily accessible prognostic tool that allows healthcare providers to identify individuals who have developed or are at risk of getting severe disease. This would simplify patient triage and allow for earlier intervention, even at home or in primary care settings.
In the early stages of COVID-19, a research team directed by IBEC and Hospital del Mar, with support from the Universitat Politècnica de Catalunya (UPC), CIBER-BBN, and CIBERES, conducted a study based on the analysis and interpretation of cough sounds.
This method is given as a potentially predictive, straightforward, and user-friendly tool for assessing the risk of severe pneumonia.
The study included smartphone recordings of voluntarily coughing sounds from 70 patients with SARS-CoV-2 infection, all taken within the first 24 hours of their hospitalisation.
IBEC performed an acoustic analysis of these recordings, which revealed significant changes in cough sounds depending on the severity of the respiratory ailment, which had previously been confirmed by imaging tests and the requirement for supplemental oxygen.
According to the findings, this approach might be utilised to classify COVID-19 patients as mild, moderate, or severe, as well as to monitor patients with chronic COVID-19. The study used data obtained at Hospital del Mar between April 2020 and May 2021, and the results were published in the European Respiratory Journal Open Research.
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