A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records

• DOI: 10.1016/j.ejrad.2021.109583
• Publication Date: 2021-02-06T11:43:28Z
• Authors: Kuang Gong; Dufan Wu; Chiara Daniela Arru; Fatemeh Homayounieh; Nir Neumark; Jiahui Guan; Varun Buch; Kyungsang Kim; Bernardo Canedo B...; Hui Ren; Won Young Tak; Soo Young Park; Yu Rim Lee; Min Kyu Kang; Jung Gil Park; Alessandro Carriero; Luca Saba; Mahsa Masjedi; Hamidreza Talari; Rosa Babaei; Hadi Karimi Mobin; Shadi Ebrahimian; Ning Guo; Subba R. Digumarthy; Ittai Dayan; Mannudeep K. Kalra; Quanzheng Li
• Keywords: Prognosi; Electronic health record; 610; 03 medical and health sciences; Deep Learning; 0302 clinical medicine; Electronic health records; Electronic Health Records; Humans; Tomography; Computed tomography; Lung; SARS-CoV-2; COVID-19; 600; Deep learning; General Medicine; Prognosis; X-Ray Computed; 3. Good health; Radiology Nuclear Medicine and imaging; Tomography, X-Ray Computed; Human; Research Article

As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction.We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction.For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort.The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.