AI predicts COVID-19 patients’ daily trajectory in UK intensive care
Researchers used AI to identify which daily changing clinical parameters best predict intervention responses in critically ill COVID-19 patients, according to a news release from Imperial College London.
The investigators used machine learning to predict which patients might get worse and not respond positively to being turned onto their front in intensive care units (ICUs) — a technique known as proning that is commonly used in this setting to improve oxygenation of the lungs.
While the AI model was used on a retrospective cohort of patient data collected during the pandemic’s first wave, the study demonstrates the ability of AI methods to predict patient outcomes using routine clinical information used in the ICU.
The researchers say the approach, where each patient’s data were analyzed day-by-day instead of only on admission, could be used to improve guidelines in clinical practice going forward. It could be applied to potential future waves of the pandemic and other diseases treated in similar clinical settings.
This is the first study that examines daily COVID-19 patient data, using AI to understand the clinical response to the rapidly changing needs of patients in ICUs. Led by a team from Imperial College London and Royal Brompton and Harefield hospitals for the COVID-ICU Consortium, the research is published in Intensive Care Medicine.
The prone position is used in ICUs to help improve blood oxygenation in people with severe acute respiratory distress syndrome and has been used extensively during the pandemic. However, proning did not help all COVID-19 patients and, when used in patients who will not benefit, can delay the start of other sequential treatments like using extracorporeal membrane oxygenation (ECMO), a life-support machine that takes over for the heart and lungs to oxygenate blood and pump it round the body.
The new findings show that the AI model identified factors that determined which patients were likely to get worse and not respond to interventions like proning. The researchers found that during the first wave of the pandemic, patients with blood clots or inflammation in the lungs, lower oxygen levels, lower blood pressure, and lower lactate levels were less likely to benefit from being proned. Overall, proning improved oxygenation in only 44% of patients.
The researchers analyzed data from 633 mechanically ventilated COVID-19 patients across 20 UK ICUs during the first wave of the COVID-19 outbreak (March 1-August 31, 2020). They examined the importance of factors associated with disease progression, like blood clots and inflammation in the lungs, as well as treatments given and whether the patient ultimately died or was discharged.
They used this data, which was collected daily by legions of medical students, nurses, doctors, audit, research and data staff, to design and train the AI tool which then made predictions on factors that determine outcomes.
The researchers continue to collect patient data and are currently analyzing findings from the second wave of the pandemic. They note that in the first wave there were limited drug treatments available, so more COVID-19 patients may have been triaged directly to ICU for breathing support. However, in the second wave, proven treatments such as steroids and tociluzimab, pioneered by other Imperial researchers, were more widely available, so those who progressed to ICU had a different disease profile, as they were patients who were inherently resistant to these initial drug treatments.
