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In European journal of radiology ; h5-index 47.0

PURPOSE : 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.

METHOD : 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.

RESULTS : 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.

CONCLUSION : 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.

Gong Kuang, Wu Dufan, Arru Chiara Daniela, Homayounieh Fatemeh, Neumark Nir, Guan Jiahui, Buch Varun, Kim Kyungsang, Bizzo Bernardo Canedo, Ren Hui, Tak Won Young, Park Soo Young, Lee Yu Rim, Kang Min Kyu, Park Jung Gil, Carriero Alessandro, Saba Luca, Masjedi Mahsa, Talari Hamidreza, Babaei Rosa, Mobin Hadi Karimi, Ebrahimian Shadi, Guo Ning, Digumarthy Subba R, Dayan Ittai, Kalra Mannudeep K, Li Quanzheng


COVID-19, Computed tomography, Deep learning, Electronic health records, Prognosis