About: Eleven Routine Clinical Features Predict COVID-19 Severity

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Eleven Routine Clinical Features Predict COVID-19 Severity
Creator	Zhang, Chao Wang, Jing Li, Jun Zhou, Kai Li, Lu Chen, Hao Li, Minghui Li, Shen, Bo Liang, Junbo Chen, Haixiao Ge, Weigang Guo, Tiannan Sun, Yaoting Xu, Jiaqin Zhang, Fangfei Chen, Shiyong Peng, Minfei Wu, Xiaomai Zhu, Yi Chen, Baofu Dong, Xuejun Huang, Shigao Luxiao, Hong Lv, Dongqing Sun, Xindong Tu, Chunyu Yang, Hai Yue, Liang Zang, Zelin Zhang, Qiushi Zhao, Haihong Zhou, A Zhou, Zhehan Zhu, Hongguo
source	MedRxiv
abstract	Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort consisting of training, validation, and internal test sets, longitudinally recorded 124 routine clinical and laboratory parameters, and built a machine learning model to predict the disease progression based on measurements from the first 12 days since the disease onset when no patient became severe. A panel of 11 routine clinical factors, including oxygenation index, basophil counts, aspartate aminotransferase, gender, magnesium, gamma glutamyl transpeptidase, platelet counts, activated partial thromboplastin time, oxygen saturation, body temperature and days after symptom onset, constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. Our model captured predictive dynamics of LDH and CK while their levels were in the normal range. This study presents a practical model for timely severity prediction and surveillance for COVID-19, which is freely available at webserver https://guomics.shinyapps.io/covidAI/.
has issue date	2020-07-29(xsd:dateTime)
bibo:doi	10.1101/2020.07.28.20163022
has license	medrxiv
sha1sum (hex)	539fc78789331a7f40be5d9a33d75568917deb08
schema:url	https://doi.org/10.1101/2020.07.28.20163022
resource representing a document's title	Eleven Routine Clinical Features Predict COVID-19 Severity
resource representing a document's body	covid:539fc78789331a7f40be5d9a33d75568917deb08#body_text
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