Tags: China | Coronavirus | covid | coronavirus | mortality | china

Chinese Researchers: Models Predict COVID-19 Mortality Rate With 90 Percent Accuracy

test tubes with blood are analyzed for covid-19
A medical Laboratory scientist tests samples for coronavirus. (John Moore/Getty Images)

By    |   Tuesday, 19 May 2020 05:59 PM

Researchers in Wuhan, China, have created a model that predicts with 90 percent certainty whether a patient is likely to die from COVID-19.

The study, published Thursday in the journal Nature Machine Intelligence, looked at blood samples from 485 infected individuals in Wuhan, where the virus allegedly originated. Researchers developed a mathematical modeling approach based on machine learning algorithms to identify three biological markers – lactic dehydrogenase, lymphocyte and high-sensitivity C-reactive protein levels – to predict the mortality of individual patients more than 10 days in advance with more than 90 percent accuracy.

“Relatively high levels of LDH alone seem to play a crucial role in distinguishing the vast majority of cases that require immediate medical attention, researchers wrote in the abstract.

"The three key features, LDH, lymphocytes and hs-CRP, can be easily collected in any hospital," the researchers wrote. "In crowded hospitals, and with shortages of medical resources, this simple model can help to quickly prioritize patients, especially during a pandemic when limited healthcare resources have to be allocated."

Of the 375 cases included in the analysis, 201 recovered from COVID-19 and were discharged from the hospital, while the remaining 174 patients died.

More than 82,690 people have been infected with COVID-19 in China, and nearly 5,000 have died.

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Researchers in Wuhan, China, have created a model that predicts with 90 percent certainty whether a patient is likely to die from COVID-19.The study, published Thursday in the journal Nature Machine Intelligence, looked at blood samples from 485 infected individuals in...
covid, coronavirus, mortality, china
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2020-59-19
Tuesday, 19 May 2020 05:59 PM
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