Machine learning used to predict outcome of Covid-19 patients

This technique, known as proning, is commonly used in this setting to improve oxygenation of the lungs, but is not suitable for all patients.

Researchers from Imperial College London gave the algorithm each patient’s data on a daily basis instead of only on admission so that it could more accurately track their condition.

They believe the system could be used to improve guidelines in clinical practice going forward and could be applied to potential future waves of the pandemic and other diseases treated in similar clinical settings.

First author of the study Dr Brijesh Patel said: “Most studies look at the health of a patient on admission to ICU and whether they were discharged or sadly died. In ICU there is a huge amount of information which we use at the bedside to manage patients on a day-by-day basis and our study focuses on how the patients’ state changed daily.

“This helped focus our attention on which specific parameters matter the most and how the importance of each parameter changes over time. This dynamic understanding is vitally important when trying to understand a new life-threatening disease and to know when and in whom each intervention works.”

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 treatments.

The 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 per cent of patients.

The researchers analysed data from 633 mechanically ventilated Covid-19 patients across 20 UK ICUs during the first wave of the outbreak which began last March. 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.

In November 2020, another group of researchers developed an AI that can detect signs of Covid-19 by looking at X-ray images of a patients’ lungs.