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BMC Infect Dis . Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai

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  • BMC Infect Dis . Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai

    BMC Infect Dis


    . 2023 Sep 16;23(1):606.
    doi: 10.1186/s12879-023-08582-5. Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai

    Chun Yin # 1 2 , Bo Hu # 3 , Kunyan Li # 1 , Xian Liu # 4 , Shuili Wang 2 , Rulin He 2 , Haibing Ding 2 , Mingpeng Jin 5 , Cheng Chen 6



    AffiliationsAbstract

    Background: Omicron variant of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has rapidly become a global threat to public health. Numerous asymptomatic and mild cases had been admitted in shelter hospitals to quickly win the fight against Omicron pandemic in Shanghai. However, little is known about influencing factors for deterioration and length of stay (LOS) in hospitals among these non-severe cases.
    Methods: This study included 12,555 non-severe cases with COVID-19 in largest shelter hospital of Shanghai, aiming to explore prognostic factors and build effective models for prediction of LOS.
    Results: Data showed that 75.0% of participants were initially asymptomatic. In addition, 94.6% were discharged within 10 days, only 0.3% with deterioration in hospitals. The multivariate analysis indicated that less comorbidities (OR = 1.792, P = 0.012) and booster vaccination (OR = 0.255, P = 0.015) was associated with the decreased risk of deterioration. Moreover, age (HR = 0.991, P < 0.001), number of symptoms (HR = 0.969, P = 0.005), time from diagnosis to admission (HR = 1.013, P = 0.001) and Cycle threshold (CT) values of N gene (HR = 1.081, P < 0.001) were significant factors associated with LOS. Based on these factors, a concise nomogram model for predicting patients discharged within 3 days or more than 10 days was built in the development cohort. In validation cohort, 0.75 and 0.73 of Areas under the curve (AUC) in nomograms, similar with AUC in models of simple machine learning, showed good performance in estimating LOS.
    Conclusion: Collectively, this study not only provides important evidence to deeply understand clinical characteristics and risk factors of short-term prognosis in Shanghai Omicron outbreaks, but also offers a concise and effective nomogram model to predict LOS. Our findings will play critical roles in screening high-risk groups, providing advice on duration of quarantine and helping decision-makers with better preparation in outbreak of COVID-19.

    Keywords: COVID-19; Deterioration; Nomogram; Omicron; Prognosis.

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