While most people infected with COVID-19 have milder symptoms and recover within a few weeks, the global pandemic produced by the SARS-CoV-2 virus continues to pose a substantial health risk.
Some of the individuals affected may develop more severe sickness and pneumonia, often resulting in a more unfavourable prognosis.
Although protocols have been developed to assess patients’ risk, diagnostic and prognostic tools primarily rely on expensive and less accessible imaging methods, such as radiography, ultrasound, or computed tomography (CT).
Therefore, there is a need to develop a simpler and more readily available prognostic tool that enables healthcare providers to identify patients who have developed or are at risk of developing severe disease. This would streamline patient triage and facilitate early intervention, even in home or primary care settings.
Now, a research team led by IBEC and Hospital del Mar, with collaboration from the Universitat Politècnica de Catalunya (UPC), CIBER-BBN and CIBERES, has carried out a study based on the analysis and interpretation of cough sounds in the initial phases of COVID-19. This method is presented as a potential predictive, simple, and accessible tool to assess the risk of suffering severe pneumonia.
The research involved smartphone recordings of voluntary coughing sounds from 70 patients with SARS-CoV-2 infection, all recorded within the first 24 hours after their admission to the hospital. IBEC conducted an acoustic analysis of these recordings, which revealed significant differences in cough sounds depending on the severity of the respiratory condition, as previously confirmed by imaging tests and the need for supplemental oxygen.
The results indicate that this analysis could be used to categorise COVID-19 patients as mild, moderate, or severe and to monitor patients with persistent COVID-19. The study was conducted using data collected between April 2020 and May 2021 at Hospital del Mar, and the findings have been published in the European Respiratory Journal Open Research.
(with inputs from ANI)
