Selected article for: "disease outbreak and select model"

Author: Liao, Yunfei; Feng, Yong; Wang, Bo; Wang, Hanyu; Huang, Jinsha; Wu, Yaxin; Wu, Ziling; Chen, Xiao; Yang, Chao; Fu, Xinqiao; Sun, Hui
Title: Clinical characteristics and prognostic factors of COVID-19 patients progression to severe: a retrospective, observational study
  • Cord-id: zq7lraqc
  • Document date: 2020_10_14
  • ID: zq7lraqc
    Snippet: The outbreak of coronavirus disease 2019 (COVID-19) has become a world-wide emergency. The severity of COVID-19 is highly correlated with its mortality rate. We aimed to disclose the clinical characteristics and prognostic factors of COVID-19 patients who developed severe COVID-19. The study enrolled cases (no=1848) with mild or moderate type of COVID-19 in Fangcang shelter hospital of Jianghan. A total of 56 patients progressed from mild or moderate to severe. We used least absolute shrinkage a
    Document: The outbreak of coronavirus disease 2019 (COVID-19) has become a world-wide emergency. The severity of COVID-19 is highly correlated with its mortality rate. We aimed to disclose the clinical characteristics and prognostic factors of COVID-19 patients who developed severe COVID-19. The study enrolled cases (no=1848) with mild or moderate type of COVID-19 in Fangcang shelter hospital of Jianghan. A total of 56 patients progressed from mild or moderate to severe. We used least absolute shrinkage and selection operator regression model to select prognostic factors for this model. The case-severity rate was 3.6% in the shelter hospital. They were all symptomatic at admission. Fever, cough, and fatigue were the most common symptoms. Hypertension, diabetes and coronary heart diseases were common co-morbidities. Predictors contained in the prediction nomogram included fever, distribution of peak temperature (>38°C), myalgia or arthralgia and distribution of C-reactive protein (≥10 mg per L). The distribution of peak temperature (>38°C) on set, myalgia or arthralgia and C-reactive protein (≥10 mg per L) were the prognostic factors to identify the progression of COVID-19 patients with mild or moderate type. Early attention to these risk factors will help alleviate the progress of the COVID-19.

    Search related documents:
    Co phrase search for related documents
    • absolute lasso selection shrinkage operator and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • absolute lasso selection shrinkage operator and logistic regression analysis: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    • absolute lasso selection shrinkage operator and logistic regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
    • acid test result and logistic regression: 1, 2, 3, 4, 5
    • acid test result and logistic regression analysis: 1, 2, 3
    • acid test result and logistic regression model: 1
    • acid testing and logistic regression: 1, 2, 3, 4, 5, 6, 7
    • acid testing and logistic regression analysis: 1, 2, 3, 4
    • admission aging and logistic regression: 1
    • admission common symptom and logistic regression: 1, 2
    • admission common symptom and logistic regression analysis: 1, 2
    • admission scan and logistic regression: 1, 2, 3, 4, 5, 6
    • admission scan and logistic regression analysis: 1, 2, 3, 4
    • admission scan and logistic regression model: 1
    • liver kidney heart and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9
    • liver kidney heart and logistic regression analysis: 1, 2
    • liver kidney heart and logistic regression model: 1
    • liver kidney heart disease and logistic regression: 1, 2, 3, 4
    • liver kidney heart disease and logistic regression model: 1