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    Establishment of a clinical nomogram model to predict the progress to severe COVID-19 MESHD 

    Authors: Changli Tu; Guojie Wang; Cuiyan Tan; Meizhu Chen; Hu Peng; Ying Wang; Yingjian Liang; Yiying Huang; Zhenguo Wang; Jian Wu; Kongqiu Wang; Qinhuan Huang; Jin Huang; Xiaobin Zheng; Qiuyue Chen; Yayuan Geng; Na Guo; Xiaorong Zhou; Xinran Liu; Jing Liu; Hong Shan

    doi:10.21203/rs.3.rs-17574/v1 Date: 2020-03-14 Source: ResearchSquare

    Background: Severe acute r espiratory syndrome coronavirus 2 (SARS-CoV-2) infection MESHDis the leading cause of a public health emergency in the world, accompanying with high mortality in severe c orona virus disease MESHD2019( COVID-19 MESHD ), thereby early detection and stopping the progress to severe COVID-19 MESHD is important. Our aim is to establish a clinical nomogram model to calculate and predict the progress to severe COVID-19 MESHD timely and efficiently.Methods: In this study, 65 patients with COVID-19 MESHD had been included retrospectively in the Fifth Affiliated Hospital of Sun Yat-sen University from January 17, to February 11, 2020. Patients were randomly assigned to train dataset (n=51 with 15 progressing to severe COVID-19 MESHD) and test dataset (n=14 with 4 progressing to severe COVID-19 MESHD). Lasso algorithm was applied to filter the most classification relevant clinical factors. Based on selected factors, logistic regression model was fit to predict the severe from mild/common. Meanwhile in nomogram sensitivity SERO, specificity, AUC (Area under Curve), and calibration curve were depicted and calculated by R language, to evaluate the prediction performance SERO to severe COVID-19 MESHD.Results:High ratio of sever COVID-19 MESHD patients (26.5%) had been found in our retrospective study, and 84% of these cases progress to severe or critical after 5 days from their first clinical examination. In these 65 patients with COVID-19 MESHD, 77 clinical characteristics in first examination were collected and analyzed, and 37 ones had been found different between non-severe and severe COVID-19 MESHD. But when all these factors were analyzed in establishment of prediction model, six factors are crucial for predicting progress of severe COVID-19 MESHD via Lasso algorithm. Based on these six factors, including increased fibrinogen, hyponatremia HP yponatremia, MESHD decreased PaO2,multiple lung lobes involved, down-regulated CD3(+)T-lymphocyte and fever HP ever, MESHD a logistic regression model was fit to discriminate severe and common COVID-19 MESHD patients. The sensitivity SERO, specificity and AUC were 0.93, 0.86, 0.96 in the train dataset and 0.9, 1.0, 1.0 in test dataset respectively. Nomogram-predicted probability was more consistent with actual probability by R language.Conclusions:In summary, an efficient and reliable clinical nomogram model had been established, which indicate increased fibrinogen, hyponatremia HP yponatremia, MESHD decreased PaO2, multiple lung lobes involved, down-regulated CD3(+)T-lymphocyte and fever HP ever MESHDat the first clinical examination, could predict progress of patients to severe COVID-19 MESHD.

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MeSH Disease
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