https://www.socictopen.socict.org/files/original/fabd2f2ca9160a449b2a0c52f327c0a7.pdf e998f50c25880875ce95e22dec9c5d4d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Novel Scoring System for Prediction of Disease Severity in COVID-19 Creator An entity primarily responsible for making the resource Yan Zhao, Chi Zhang, Qi Wang, Xuemei Li, Ling Qin, Bin Xu, Yonghong Zhang, Ying-Mei Feng, Kang Li, Ronghua Jin, Jian-Ping Sun, Yanchao Dai, Tao Dong, Lian-Chun Liang, Zhongjie Hu, Haiping Xiang Description An account of the resource Background: A novel enveloped RNA beta coronavirus, Corona Virus Disease 2019 (COVID-19) caused severe and even fetal pneumonia in China and other countries from December 2019. Early detection of severe patients with COVID-19 is of great significance to shorten the disease course and reduce mortality.Methods: We assembled a retrospective cohort of 80 patients (including 56 mild and 24 severe) with COVID-19 infection treated at Beijing You'an Hospital. We used univariable and multivariable logistic regression analyses to select the risk factors of severe and even fetal pneumonia and build scoring system for prediction, which was validated later on in a group of 22 COVID-19 patients.Results: Age, white blood cell count, neutrophil, glomerular filtration rate, and myoglobin were selected by multivariate analysis as candidates of scoring system for prediction of disease severity in COVID-19. The scoring system was applied to calculate the predictive value and found that the percentage of ICU admission (20%, 6/30) and ventilation (16.7%, 5/30) in patients with high risk was much higher than those (2%, 1/50; 2%, 1/50) in patients with low risk (p = 0.009; p = 0.026). The AUC of scoring system was 0.906, sensitivity of prediction is 70.8%, and the specificity is 89.3%. According to scoring system, the probability of patients in high risk group developing severe disease was 20.24 times than that in low risk group.Conclusions: The possibility of severity in COVID-19 infection predicted by scoring system could help patients to receiving different therapy strategies at a very early stage.Topic: COVID-19, severe and fetal pneumonia, logistic regression, scoring system, prediction. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Logistic regression, retrospective cohort, COVID-19, severity pneumonia, prediction scoring system Identifier An unambiguous reference to the resource within a given context DOI: 10.3389/fcimb.2020.00318 Source A related resource from which the described resource is derived Frontiers in Cellular and Infection Microbiology Publisher An entity responsible for making the resource available Frontiers Media S.A. Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Microbiology