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Providers can use scans to score a patient’s lung health, potentially identifying which patients will fare worse from the virus, requiring more extensive treatment.
Lung ultrasound scans could play a vital role in helping providers identify and predict which patients, who are hospitalized for the virus, will worsen and require more treatment. This development could be critical nationwide as cases of COVID-19 are surging.
It is already known that lung ultrasounds can help identify patients who have COVID-19 pneumonia, but according to researchers from Policlinico San Matteo in Pavia, the scans can also help physicians rapidly assess and determine the probability of whether a patient will continue to deteriorate.
It all comes down to a lung score assigned from a 3-point scale.
The team, led by Umberto Sabatini, M.D., an internal medicine resident, presented their method, which uses ultrasound images to score a patient’s lung health, during the 179th Meeting of the Acoustical Society of America. By assigning a simple score to these scans, they said, providers can make much more informed decisions about patients and the care they will need.
To develop the tool, the team performed lung ultrasounds – both upon arrival and before discharge – on 52 patients who were admitted to San Matteo Hospital between March 15, 2020, and April 29, 2020. They examined 14 points in the lungs for abnormalities, such as pleural irregularities, and gave each spot a score on a 3-point scale to assign a level of severity.
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Overall, the entire exam lasted only 15 minutes-to-20 minutes as a scan of each spot took roughly 10 seconds.
For each exam, the team added up all the points, and based on those calculations, they discovered that patients who had higher lung ultrasound scores ended up having worse COVID-19 outcomes. These individuals were more likely to require supplemental oxygen, to be admitted to the intensive care unit, or to die. Specifically, patients who had at least three spots with a score of “3” or those with high overall scores were more than six times more likely to have a more negative outcome.
Using these results, Sabatini’s team suggested a nomogram that could help clinicians more accurately predict the likelihood that a patient will continue to decline and need more extensive care.