AI Analytics Predict Daily Trajectory of COVID-19 Patients in Intensive Care Units

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, the study found that 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.

This was 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. The researchers analyzed data from 633 mechanically ventilated COVID-19 patients across 20 UK ICUs during the first wave of the COVID-19 outbreak (1 March – 31 August 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 new findings showed 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.

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 by ICU medics. 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.

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 tocilizumab 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.

“Advanced analytics to enable tracking of disease allows patient care to be streamlined so that the window of opportunity for any intervention is not missed,” said first author and clinical science lead Dr. Brijesh Patel, from Imperial’s Department of Surgery and Cancer and senior intensivist at Royal Brompton Hospital. “The data from this national evaluation enabled us not only to examine how our management decisions affected disease course but importantly where we could improve.”

“Our machine learning tool could help track patient progress in real time and help inform ICU guidelines by filling the gaps of patient care - reflecting back to clinicians to identify best practice quickly and benefit from sharing,” said senior author and data science lead Professor Aldo Faisal, Director of Imperial’s UKRI Centre for Doctoral Training in AI for Healthcare at the Departments of Computing and Bioengineering. “More than one year on, we’re still learning how the course of COVID-19 affects the body, and how this can change day-by-day. Data science and the daily data feeds from ICUs across the country help us learn much faster how best to treat individual patients based on their daily symptoms and needs.”


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Imperial College London