Corpus overview


MeSH Disease

Human Phenotype


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    Neutrophil-to-Lymphocyte Ratio on Admission Predicts In-hospital Mortality in Patients with COVID-19

    Authors: Jin Hu; Jun Zhou; Fang Dong; Jie Tan; Shuntao Wang; Zhi Li; Ximeng Zhang; Huiqiong Zhang; Jie Ming; Tao Huang

    doi:10.21203/ Date: 2020-07-26 Source: ResearchSquare

    Background: A novel coronavirus caused an outbreak of acute infectious pneumonia HP pneumonia MESHD are spreading over the globe. However, studies predicting prognosis are limited. We predicted outcomes of patients with coronavirus disease MESHD 2019 (COVID-19) using the neutrophil-to-lymphocyte ratio (NLR) on admission.Methods: We retrospectively analyzed the characteristics of COVID-19 patients diagnosed from February 6 to March 1. The outcomes, including the occurrence of in-hospital mortality, acute kidney injury MESHD acute kidney injury HP (AKI), and endotracheal intubation (ETI), were recorded. The relationships of neutrophils, lymphocytes, C-reactive protein, lactate dehydrogenase, and NLR with outcomes were assessed using multivariate regression model. P-values for trends across quartiles of NLR was examined.Results: A total of 182 patients were included. 37 (20.3%) patients died during the hospitalization, 41 (22.5%) developed AKI, and 36 (19.8%) received ETI. The NLR had a superior predictive performance SERO than others. Using an NLR cutoff of 11.4, the area under the curves (AUC) were 0.766 for in-hospital mortality, 0.755 for AKI, and 0.733 for ETI. In multivariate analysis, NLR >11.4 was further identified as an independent prognostic factor. Following stratification with quartiles of NLR, a positive trend between the increasing quartiles of NLR and the three outcomes were observed (p-values for trends across quartiles were 0.043, <0.001, and 0.041, respectively). The multivariate adjusted odds ratio (OR) in the highest quartile vs. the lowest quartile were 5.738 for mortality, 25.307 for AKI, and 5.136 for ETI.Conclusions: Increasing NLR obtained on admission is a powerful predictor for inpatient mortality, AKI, and ETI in COVID-19 patients.

    Estimate the incubation period TRANS of coronavirus 2019 (COVID-19)

    Authors: Henry Han

    doi:10.1101/2020.02.24.20027474 Date: 2020-02-29 Source: medRxiv

    Motivation: Wuhan pneumonia MESHD pneumonia is an acute infectious HP is an acute infectious disease MESHD caused by the 2019 novel coronavirus (COVID-19). It is being treated as a Class A infectious disease MESHD though it was classified as Class B according to the Infectious Disease MESHD Prevention Act of China. Accurate estimation of the incubation period TRANS of the coronavirus is essential to the prevention and control. However, it remains unclear about its exact incubation period TRANS though it is believed that symptoms of COVID-19 can appear in as few as 2 days or as long as 14 or even more after exposure. The accurate incubation period TRANS calculation requires original chain-of- infection MESHD data that may not be fully available in the Wuhan regions. In this study, we aim to accurately calculate the incubation period TRANS of COVID-19 by taking advantage of the chain-of- infection MESHD data, which is well-documented and epidemiologically informative, outside the Wuhan regions. Methods: We acquired and collected officially reported COVID-19 data from 10 regions in China except for Hubei province. To achieve the accurate calculation of the incubation period TRANS, we only involved the officially confirmed cases TRANS with a clear history of exposure and time of onset. We excluded those without relevant epidemiological descriptions, working or living in Wuhan for a long time, or hard to determine the possible exposure time. We proposed a Monte Caro simulation approach to estimate the incubation of COVID-19 as well as employed nonparametric ways. We also employed manifold learning and related statistical analysis to decipher the incubation relationships between different age TRANS/ gender TRANS groups. Result: The incubation period TRANS of COVID-19 did not follow general incubation distributions such as lognormal, Weibull, and Gamma distributions. We estimated that the mean and median of its incubation were 5.84 and 5.0 days via bootstrap and proposed Monte Carlo simulations. We found that the incubation periods TRANS of the groups with age TRANS>=40 years and age TRANS<40 years demonstrated a statistically significant difference. The former group had a longer incubation period TRANS and a larger variance than the latter. It further suggested that different quarantine time should be applied to the groups for their different incubation periods TRANS. Our machine learning analysis also showed that the two groups were linearly separable. incubation of COVID-19 along with previous statistical analysis. Our results further indicated that the incubation difference between males TRANS and females TRANS did not demonstrate a statistical significance.

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MeSH Disease
Human Phenotype

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