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dc.contributor.authorYuan, Yeen_US
dc.contributor.authorSun, Chuanen_US
dc.contributor.authorTang, Xiuchuanen_US
dc.contributor.authorCheng, Chengen_US
dc.contributor.authorMombaerts, Laurenten_US
dc.contributor.authorWang, Maolinen_US
dc.contributor.authorHu, Taoen_US
dc.contributor.authorSun, Chenyuen_US
dc.contributor.authorGuo, Yuqien_US
dc.contributor.authorLi, Xiutingen_US
dc.contributor.authorXu, Huien_US
dc.contributor.authorRen, Tongxinen_US
dc.contributor.authorXiao, Yangen_US
dc.contributor.authorXiao, Yaruen_US
dc.contributor.authorZhu, Honglingen_US
dc.contributor.authorWu, Honghanen_US
dc.contributor.authorLi, Kezhien_US
dc.contributor.authorChen, Chumingen_US
dc.contributor.authorLiu, Yingxiaen_US
dc.contributor.authorLiang, Zhichaoen_US
dc.contributor.authorCao, Zhiguoen_US
dc.contributor.authorZhang, Hai-Taoen_US
dc.contributor.authorCh Paschaldis, Ioannisen_US
dc.contributor.authorLiu, Quanyingen_US
dc.contributor.authorGoncalves, Jorgeen_US
dc.contributor.authorZhong, Qiangen_US
dc.contributor.authorYan, Lien_US
dc.date2020-11-28
dc.date.accessioned2021-09-01T14:10:34Z
dc.date.available2021-09-01T14:10:34Z
dc.date.issued2020-11-28
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/33282444
dc.identifier.citationYe Yuan, Chuan Sun, Xiuchuan Tang, Cheng Cheng, Laurent Mombaerts, Maolin Wang, Tao Hu, Chenyu Sun, Yuqi Guo, Xiuting Li, Hui Xu, Tongxin Ren, Yang Xiao, Yaru Xiao, Hongling Zhu, Honghan Wu, Kezhi Li, Chuming Chen, Yingxia Liu, Zhichao Liang, Zhiguo Cao, Hai-Tao Zhang, Ioannis Ch Paschaldis, Quanying Liu, Jorge Goncalves, Qiang Zhong, Li Yan. 2020. "Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China." Engineering (Beijing), https://doi.org/10.1016/j.eng.2020.10.013
dc.identifier.issn2095-8099
dc.identifier.urihttps://hdl.handle.net/2144/42962
dc.description.abstractCoronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.en_US
dc.languageeng
dc.language.isoen_US
dc.relation.ispartofEngineering (Beijing)
dc.rights© 2020 THE AUTHORS. This pre-proof version of the article is distributed under a Creative Commons NonCommercial NoDerivatives license.en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCOVID-19en_US
dc.subjectMortality risk predictionen_US
dc.subjectRisk scoreen_US
dc.titleDevelopment and validation of a prognostic risk score system for COVID-19 inpatients: a multi-center retrospective study in Chinaen_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1016/j.eng.2020.10.013
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Electrical & Computer Engineeringen_US
pubs.publication-statusPublisheden_US
dc.date.online2020-11-28
dc.identifier.mycv576768


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© 2020 THE AUTHORS. This pre-proof version of the article is distributed under a Creative Commons NonCommercial NoDerivatives license.
Except where otherwise noted, this item's license is described as © 2020 THE AUTHORS. This pre-proof version of the article is distributed under a Creative Commons NonCommercial NoDerivatives license.