Machine learning could personalize oxygen levels for patients on ventilators

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Supplemental oxygen is among the most widely prescribed therapies in the world, with an estimated 13 to 20 million patients worldwide requiring oxygen delivery by mechanical ventilation each year.

Jun 4 2024University of Chicago Medical Center Mechanical ventilation -; a form of life support -; is a technology that moves breathable air into and out of the lungs, acting like a bellows. Ventilators have moved far beyond the "iron lung" machines some people might picture; now, apparatuses have progressed to sophisticated, compact digital machines that deliver oxygen through a small plastic tube that goes down the throat.

The standard of care is to maintain oxygen saturation between 88 and 100; within that range, doctors have had to choose an oxygen level for ventilation without having high-quality data to inform their decision-making. Whether we like it or not, making that decision for each patient exposes them to the potential benefits or harms of the chosen oxygen level.

As personalized medicine continues gaining traction, there is a growing interest in using machine learning to make predictions for individual patients. In the context of mechanical ventilation, these models could potentially use specific patient characteristics to predict an ideal oxygen level for each patient. These characteristics included age, sex, heart rate, body temperature and reason for being admitted to an Intensive Care Unit .

Those previous randomized trials didn't go to waste -; Buell and his collaborators used data from those studies to design and train their machine learning model. After the model was developed using trial data collected in the U.S., the collaborators applied it to data from patients across the world in Australia and New Zealand.

 

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