In a recent study from Michigan Medicine, researchers developed an algorithm to predict which COVID-19 patients are likely to quickly deteriorate while hospitalized.
The study is published in the British Journal of Anaesthesia. One author is Nicholas J. Douville, M.D., Ph.D.
In the study, the team explored the potential of predictive machine learning.
They looked at a set of patients with COVID-19 hospitalized during the first pandemic surge from March to May 2020 and modeled their clinical course.
The team generated an algorithm with inputs such as a patient’s age, whether they had underlying medical conditions and what medications they were on when entering the hospital, as well as variables that changed while hospitalized, including vital signs like blood pressure, heart rate and oxygenation ratio, among others.
Of the 398 patients in their study, 93 required a ventilator or died within two weeks.
The model was able to predict mechanical ventilation most accurately based upon key vital signs, including oxygen saturation ratio, respiratory rate, heart rate, blood pressure and blood glucose level.
The team assessed the data points of interest at 4, 8, 24, and 48-hour increments, in an attempt to identify the optimal amount of time necessary to predict—and intervene—before a patient deteriorates.
They found the closer they were to the event, the higher their ability to predict, which they expected.
But they were still able to predict the outcomes with good discrimination at 48 hours, giving providers time to make alterations to the patient’s care or to mobilize resources.
In the short term, the study brings to light patient characteristics that clinicians caring for patients with COVID-19 should keep in the back of their minds.
In the long term, the researchers hope the algorithm can be integrated into existing clinical decision support tools already used in the ICU.
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