The prognostic tool, known as the Severe COVID-19 Adaptive Risk Predictor (SCARP), offers findings in an easily understandable form and can help define the one-day and seven-day risk of a patient hospitalized with COVID-19 developing a more severe form of the disease or dying from it. "SCARP was designed to provide clinicians with a predictive tool that is interactive and adaptive, enabling real-time clinical variables to be entered at a patient's bedside," says senior author Matthew Robinson, assistant professor of medicine at the Johns Hopkins University School of Medicine. "By yielding a personalized clinical prediction of developing severe disease or death in the next day and week, and at any point in the first two weeks of hospitalization, SCARP will enable a medical team to make more informed decisions about how best to treat each patient with COVID-19." Unlike past clinical prediction methods that base a patient's risk score on their condition at the time they enter the hospital, RF-SLAM adapts to the latest available patient information and considers the changes in those measurements over time. To demonstrate SCARP's ability to predict severe COVID-19 cases or deaths from the disease, Robinson and his colleagues used a clinical registry with data about patients hospitalized with COVID-19 between March and December 2020, at five centers within the Johns Hopkins Health System.

An advanced machine-learning system can accurately predict if a patient's bout with COVID-19 will become severe or fatal and relay its findings to clinicians. Clinicians often learn how to recognize patterns in COVID-19 cases after they treat many patients with it. Machine-learning systems promise to enhance that ability, recognizing more complex patterns in large numbers of people with COVID-19 and using that insight to predict the course of an individual patient's case. However, physicians sworn to "do no harm" may be reluctant to base treatment and care strategies for their most seriously ill patients on difficult-to-use or hard-to-interpret machine-learning algorithms. The new system offers findings in an easily understandable form.