  While image analysis of chest computed tomography (CT) for COVID-19 diagnosis
has been intensively studied, little work has been performed for image-based
patient outcome prediction. Management of high-risk patients with early
intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a
majority of patients recover naturally. Therefore, an accurate prediction of
disease progression with baseline imaging at the time of the initial
presentation can help in patient management. In lieu of only size and volume
information of pulmonary abnormalities and features through deep learning based
image segmentation, here we combine radiomics of lung opacities and non-imaging
features from demographic data, vital signs, and laboratory findings to predict
need for intensive care unit (ICU) admission. To our knowledge, this is the
first study that uses holistic information of a patient including both imaging
and non-imaging data for outcome prediction. The proposed methods were
thoroughly evaluated on datasets separately collected from three hospitals, one
in the United States, one in Iran, and another in Italy, with a total 295
patients with reverse transcription polymerase chain reaction (RT-PCR) assay
positive COVID-19 pneumonia. Our experimental results demonstrate that adding
non-imaging features can significantly improve the performance of prediction to
achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable
to provide clinical decision support in managing COVID-19 patients. Our methods
may also be applied to other lung diseases including but not limited to
community acquired pneumonia. The source code of our work is available at
https://github.com/DIAL-RPI/COVID19-ICUPrediction.
