Selected article for: "clinical deterioration and warning score"

Author: Yabing Guo; Yingxia Liu; Jiatao Lu; Rong Fan; Fuchun Zhang; Xueru Yin; Zhihong Liu; Qinglang Zeng; Jing Yuan; Shufang Hu; Qiongya Wang; Baolin Liao; Mingxing Huang; Sichun Yin; Xilin Zhang; Rui Xin; Zhanzhou Lin; Changzheng Hu; Boliang Zhao; Ridong He; Minfeng Liang; Zheng Zhang; Li Liu; Jian Sun; Lu Tang; Lisi Deng; Jinyu Xia; Xiaoping Tang; Lei Liu; Jinlin Hou
Title: Development and validation of an early warning score (EWAS) for predicting clinical deterioration in patients with coronavirus disease 2019
  • Document date: 2020_4_21
  • ID: e2wevpi6_29
    Snippet: While most people with COVID-19 develop only mild or uncomplicated illness, approximately 14%, even among the young patients, develop severe disease. 12 Thus, it is critical to better understand the clinical features of COVID-19 and the risk of disease progression. To our knowledge, we describe here the first validated score for predicting clinical deterioration of COVID-19 which was developed with the dataset of COVID-19 patients from Guangdong .....
    Document: While most people with COVID-19 develop only mild or uncomplicated illness, approximately 14%, even among the young patients, develop severe disease. 12 Thus, it is critical to better understand the clinical features of COVID-19 and the risk of disease progression. To our knowledge, we describe here the first validated score for predicting clinical deterioration of COVID-19 which was developed with the dataset of COVID-19 patients from Guangdong Province and validated with the dataset of COVID-19 patients from Hubei Province. Our findings show that the early-warning score can be useful in the assessment of the 14-day risk of clinical deterioration in patients with mild to moderate COVID-19 at admission. A major advantage of this score is that it is practical and easy to use in routine clinical practice, based on patient's age, history of chronic disease, NLR, CRP, and D-dimer. It can therefore be widely applicable in many health care settings globally. Although there were several differences in the characteristics of the patients in the training and validation datasets, this strengthens the reliability of our score, which was shown to offer similar predictability in different patient populations.

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