| Literature DB >> 33132302 |
Ke Ma1, Yan Xia2, Boqi Hu3, Yingli Zhang2, Xiaoming Xu2, Nan Zhang2, Hong Xu2.
Abstract
This study aimed to develop and validate a bedside risk analysis system for predicting the clinical severity and prognosis of patients with coronavirus disease 2019 (COVID-19). In total, 444 COVID-19 patients were included and randomly assigned in a 2:1 ratio to 2 groups: derivation group and validation group. The new scoring system comprised of the following 8 variables: history of malignant diseases, history of diabetes mellitus, dyspnea, respiratory rate >24 breaths/min, C-reactive protein level >14 mg/L, white blood cell count >8×109/L, platelets count <180 × 1012/L, and lymphocyte count <1 × 109/L. The sensitivity analysis revealed that this new scoring system was more efficient than the sequential organ failure assessment scoring system on the first day of admission. The receiver characteristic curve analysis revealed that the new risk scoring predicted the severe cases of COVID-19 infection with an area under the curve of 0.831 (95% confidence interval [CI]: 0.783-0.879) and 0.798 (95% CI: 0.727-0.869) in the derivation and validation groups, respectively. This proposed risk score system is a fairly reliable and robust tool for evaluating the severity and prognosis of patients with COVID-19. This may help in the early identification of severe COVID-19 patients with poor prognosis, requiring more intense interventions.Entities:
Keywords: COVID-19; SARS-CoV-2; risk prediction; severity
Mesh:
Year: 2020 PMID: 33132302 DOI: 10.7883/yoken.JJID.2020.718
Source DB: PubMed Journal: Jpn J Infect Dis ISSN: 1344-6304 Impact factor: 1.362