  COVID-19 patient triaging with predictive outcome of the patients upon first
present to emergency department (ED) is crucial for improving patient
prognosis, as well as better hospital resources management and cross-infection
control. We trained a deep feature fusion model to predict patient outcomes,
where the model inputs were EHR data including demographic information,
co-morbidities, vital signs and laboratory measurements, plus patient's CXR
images. The model output was patient outcomes defined as the most insensitive
oxygen therapy required. For patients without CXR images, we employed Random
Forest method for the prediction. Predictive risk scores for COVID-19 severe
outcomes ("CO-RISK" score) were derived from model output and evaluated on the
testing dataset, as well as compared to human performance. The study's dataset
(the "MGB COVID Cohort") was constructed from all patients presenting to the
Mass General Brigham (MGB) healthcare system from March 1st to June 1st, 2020.
ED visits with incomplete or erroneous data were excluded. Patients with no
test order for COVID or confirmed negative test results were excluded. Patients
under the age of 15 were also excluded. Finally, electronic health record (EHR)
data from a total of 11060 COVID-19 confirmed or suspected patients were used
in this study. Chest X-ray (CXR) images were also collected from each patient
if available. Results show that CO-RISK score achieved area under the Curve
(AUC) of predicting MV/death (i.e. severe outcomes) in 24 hours of 0.95, and
0.92 in 72 hours on the testing dataset. The model shows superior performance
to the commonly used risk scores in ED (CURB-65 and MEWS). Comparing with
physician's decisions, CO-RISK score has demonstrated superior performance to
human in making ICU/floor decisions.
