A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records. Issue 139 (June 2021)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: European journal of radiology. Issue 139(2021)
• Publication Date: 2021-06
• Subjects: EHR Electronic health records -- COVID-19 Coronavirus disease of 2019 -- TOR Total opacity ratio -- CR Consolidation ratio -- GLM Generalized linear model -- WBC White blood cell -- PLT Platelet -- SpO2 Oxygen saturation -- RT-PCR Reverse-transcription polymerase chain reaction -- MV Mechanical ventilation -- ICU Intensive care unit -- CT Computed tomography -- GGO Ground-glass opacity -- IRB Institutional Review Board -- GPU Graphics processing unit -- HU Hounsfield unit -- ESR Erythrocyte sedimentation rate -- AUC Area under the curve -- CI Confidence interval -- Hgb Hemoglobin -- MODS Multiple Organ Dysfunction Score -- SOFA Sequential Organ Failure Assessment -- LDH Lactate dehydrogenase -- hs-CRP High-sensitivity C-reactive protein; COVID-19 -- Computed tomography -- Deep learning -- Electronic health records -- Prognosis; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.109583
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Deep learning method can robustly segment lung infection regions from CT images of COVID-19 patients. The correlation coefficient of the network prediction and manual segmentation was high to very high. Combining CT-derived biomarkers with electronic health records can achieve the best prognosis prediction with AUC's ranging between 85–93. Prognosis results indicated that age, Oxygen saturation, CT-derived biomarkers, platelet count, and white blood cell count were the most important prognostic predictors of COVID-19. Abstract: 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.