Machine Learning Algorithm Identifies Deteriorating Patients in Hospital Who Need Intensive Care

Researchers have developed a machine learning algorithm that could significantly improve clinicians’ ability to identify hospitalized patients whose condition is deteriorating to the extent that they need intensive care.

The HAVEN system (Hospital-wide Alerting Via Electronic Noticeboard) developed by scientists at the University of Oxford (Oxford, UK) combines patients’ vital signs - such as blood pressure, heart rate and temperature - with their blood test results, comorbidities and frailty into a single risk score. The HAVEN score gives a more precise indication of which patients are deteriorating when compared with previously published scores.

Over the past 20 years, health care systems worldwide have implemented alerting systems to improve detection of patients at risk of deterioration. Most are based on abnormalities in patients’ vital signs, usually by combining them into an early warning score. Clinicians are alerted when the EWS rises above a given threshold.

“Late recognition of patient deterioration in hospital is associated with worse outcomes, including higher mortality. Despite the widespread introduction of early warning score systems, which are based on vital signs, deterioration still goes unrecognized,” said Prof Peter Watkinson, Associate Professor of Intensive Care Medicine at the University’s Nuffield Department of Clinical Neurosciences. “The HAVEN system we have developed and validated was able to detect nearly twice as many patients who suffered a cardiac arrest or needed intensive care up to 48 hours in advance, than the next best system.”

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