Johns Hopkins Medicine researchers have developed an advanced machine-learning system that can accurately predict how a patient’s bout with COVID-19 will go and relay its findings back to the clinician in an easily understandable form, according to a news release from the academic medical center.
The new prognostic tool, known as the Severe COVID-19 Adaptive Risk Predictor (SCARP), 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 asks for a minimal amount of input to give an accurate prediction, making it fast, simple to use and reliable for basing treatment and care decisions. The new tool is described in a paper posted online in the Annals of Internal Medicine.
“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,” said Matthew Robinson, MD, Assistant Professor of Medicine at the Johns Hopkins University School of Medicine and senior author of the paper.
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 make this dynamic analysis possible, RF-SLAM divides a patient’s hospital stay into six-hour windows. Data collected during those time spans are then evaluated by the algorithm’s “random forests” of approximately 1,000 “decision trees” that operate as an ensemble. This enables SCARP to give a more accurate prediction of an outcome than each individual decision tree could do on its own.
To demonstrate SCARP’s ability to predict severe COVID-19 cases or deaths from the disease, the researchers 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.
The patient information available included demographics, other medical conditions and behavioral risk factors, along more than 100 variables over time, such as vital signs, blood counts, metabolic profiles, respiratory rates and the amount of supplemental oxygen needed.
Among 3,163 patients admitted with moderate COVID-19 during this time, 228 (7%) became severely ill or died within 24 hours; an additional 355 (11%) became severely ill or died within the first week. Data also were collected on the numbers who developed severe COVID-19 or died on any day within the 14 days following admission.
Overall, SCARP’s one-day risk predictions for progression to severe COVID-19 or death were 89% accurate, while the seven-day risk predictions for both outcomes were 83% accurate.
