Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
    displaying 1 - 10 records in total 12
    records per page




    SARS-CoV-2 antibody SERO prevalence SERO in health care workers: Preliminary report of a single center study

    Authors: Michael Brant-Zawadzki; Deborah Fridman; Philip Robinson; Matthew Zahn; Randy German; Marcus Breit; Junko Hara

    doi:10.1101/2020.07.20.20158329 Date: 2020-07-25 Source: medRxiv

    SARS-CoV-2 has driven a pandemic crisis. Serological surveys have been conducted to establish prevalence SERO for covid-19 antibody SERO in various cohorts and communities. However, the prevalence SERO among healthcare workers is still being analyzed. The present study reports on initial sero-surveillance conducted on healthcare workers at a regional hospital system in Orange County, California, during May and June, 2020. Study participants were recruited from the entire hospital employee workforce and the independent medical staff. Data were collected for job title, location, covid-19 symptoms, a PCR test history, travel TRANS record since January 2020, and existence of household contacts TRANS with covid-19. A blood SERO sample was collected from each subject for serum SERO analysis for IgG antibodies SERO to SARS-CoV-2. Of 3,013 tested individuals, a total 2,932 were included in the analysis due to some missing data. Observed prevalence SERO of 1.06% (31 antibody SERO positive cases), adjusted prevalence SERO of 1.13% for test sensitivity SERO and specificity were identified. Significant group differences between positive vs. negative were observed for age TRANS (z = 2.65, p = .008), race (p = .037), presence of fever HP fever MESHD (p < .001) and loss of smell (p < .001). Possible explanation for this low prevalence SERO includes a relatively low local geographic community prevalence SERO (~4.4%) at the time of testing, the hospital's timely procurement of personal protective equipment, rigorous employee education MESHD, patient triage and treatment protocol development and implementation. In addition, possible greater presence of cross-reactive adaptive T cell mediated immunity in healthcare workers vs. the general population may have contributed. Determining antibody SERO prevalence SERO in front-line workers, and duration of antibody SERO presence may help stratify the workforce for risk, establish better health place policies and procedures, and potentially better mitigate transmission TRANS.

    Joint Detection of Serum SERO IgM/IgG Antibody SERO is An Important Key to Clinical Diagnosis of SARS-COV-2 Infection

    Authors: Fang Hu; Xiaoling Shang; Meizhou Chen; Changliang Zhang

    doi:10.1101/2020.07.07.20146902 Date: 2020-07-08 Source: medRxiv

    Background: This study was aimed to investigate the application of SARS- COV-2 IgM and IgG antibodies SERO in diagnosis of COVID-19 infection MESHD. Method: This study enrolled a total of 178 patients at Huangshi Central Hospital from January to February, 2020. Among them, 68 patients were SARS-COV-2 infected MESHD confirmed with nucleic acid test (NAT) and CT imaging. 9 patients were in the suspected group (NAT negative) with fever HP fever MESHD and other respiratory symptoms. 101 patients were in the control group with other diseases and negative to SARS-COV-2 infection MESHD. After serum samples SERO were collected, SARS-COV-2 IgG and IgM antibodies were tested SERO by chemiluminescence immunoassay SERO (CLIA) for all patients. Results: The specificity of serum SERO IgM and IgG antibodies SERO to SARS-COV-2 were 99.01% (100/101) and 96.04% (97/101) respectively, and the sensitivity SERO were 88.24% (60/68) and 97.06% (66/68) respectively. The combined detection rate of SARS-COV-2 IgM and IgG antibodies SERO were 98.53% (67/68). Conclusion: Combined detection of serum SERO SARS-COV-2 IgM and IgG antibodies SERO had better sensitivity SERO compared with single IgM or IgG test, which can be used as an important diagnostic tool for SARS-COV-2 infection MESHD and a screening tool of potential SARS-COV-2 carriers TRANS in clinics, hospitals and accredited scientific laboratory.

    Exploiting an Early Warning Nomogram for Predicting the Risk of ICU Admission in COVID-19 patients: A Multi-Center Study in China

    Authors: Yiwu Zhou; Yanqi He; Huan Yang; He Yu; Ting Wang; Zhu Chen; Rong Yao; Zongan Liang

    doi:10.21203/rs.3.rs-36964/v1 Date: 2020-06-19 Source: ResearchSquare

    Background Novel corona virus disease 2019 (COVID-19) is an urgent event in the worldwide. We aimed to develop and validate a practical model for early identifying and predicting which patients will be admitted to intensive care unit (ICU) based on a multi-center cohort in China. Methods Data from 1087 patients of laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28 2020 in Sichuan and Wuhan. Patients were randomly divided into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator (LASSO) analysis and logistic regression analysis were employed for the development account. The performance SERO of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. The nomogram was further assessed in a different cohort as external validation. Results The individualized prediction nomogram included 6 predictors, including age TRANS, respiratory rate, systolic blood SERO pressure, smoking status, fever HP fever MESHD and chronic kidney disease HP chronic kidney disease MESHD. The model showed high discrimination ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for prediction the risk of ICU admission. Decision curve analysis showed that the prediction nomogram was clinically useful.Conclusion We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission even in community health center. This model can be conveniently used to facilitate predicting the individual risk for ICU admission of COVID-19 patients and optimizing use of limited resources. 

    Exploiting an early warning nomogram for predicting the risk of ICU admission in COVID-19 patients: A multi-center study in China

    Authors: Yiwu Zhou; Yanqi He; Huan Yang; He Yu; Ting Wang; Zhu Chen; Rong Yao; Zongan Liang

    doi:10.21203/rs.3.rs-36964/v2 Date: 2020-06-19 Source: ResearchSquare

    Background Novel coronavirus disease MESHD 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU).Methods Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyses were used to develop the nomogram. The performance SERO of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort.Results The individualized prediction nomogram included 6 predictors: age TRANS, respiratory rate, systolic blood SERO pressure, smoking status, fever HP fever MESHD, and chronic kidney disease HP chronic kidney disease MESHD. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful.Conclusion We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

    COVID-19 diagnosis by routine blood SERO tests using machine learning

    Authors: Matjaž Kukar; Gregor Gunčar; Tomaž Vovko; Simon Podnar; Peter Černelč; Miran Brvar; Mateja Zalaznik; Mateja Notar; Sašo Moškon; Marko Notar

    id:2006.03476v1 Date: 2020-06-04 Source: arXiv

    Physicians taking care of patients with coronavirus disease MESHD (COVID-19) have described different changes in routine blood SERO parameters. However, these changes, hinder them from performing COVID-19 diagnosis. We constructed a machine learning predictive model for COVID-19 diagnosis. The model was based and cross-validated on the routine blood SERO tests of 5,333 patients with various bacterial and viral infections MESHD, and 160 COVID-19-positive patients. We selected operational ROC point at a sensitivity SERO of 81.9% and specificity of 97.9%. The cross-validated area under the curve (AUC) was 0.97. The five most useful routine blood SERO parameters for COVID19 diagnosis according to the feature importance scoring of the XGBoost algorithm were MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. tSNE visualization showed that the blood SERO parameters of the patients with severe COVID-19 course are more like the parameters of bacterial than viral infection MESHD. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever HP fever MESHD, cough HP cough MESHD, myalgia HP myalgia MESHD, and other symptoms can now have initial routine blood SERO tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results present a significant contribution to improvements in COVID-19 diagnosis.

    An emerging marker predicting the severity of COVID-19: Neutrophil-Lymphocyte Count Ratio

    Authors: Minping Zhang; Enhua Xiao; Jiayi Liu; Yeyu Cai; Qizhi Yu

    doi:10.21203/rs.3.rs-28850/v1 Date: 2020-05-14 Source: ResearchSquare

    Background: To analyze clinical features and laboratory indicators and identify the markers of exacerbation in COVID-19. Methods: We reviewed clinical histories of 177 patients with confirmed COVID-19. The patients were categorized into mild group (153 patients) and severe group (24 patients). The baseline demographic and laboratory indicators of all patients were collected, including the neutrophil-lymphocyte count ratio (NLCR) and C-reactive protein to albumin ratio (CAR). Receiver operating characteristic curve (ROC) analysis was performed to search for indicators predicting exacerbation in COVID-19 patients, and acquiring the area under the curves (AUCs), sensitivity SERO, specificity and cut-off value. Results: The age TRANS of the severe group were significantly older than those of the mild group (P <0.01). Fever HP was the typical symptom in all COVID-19 patients. Cough HP and fatigue HP were manifested in mild group, yet severe patients were more prominent in dyspnea HP. The laboratory indicators showing that the mild group mainly had an elevated C-reactive protein; the severe group had a decreased lymphocyte count and lymphocyte ratio. WBC, neutrophil count, neutrophil ratio, D-dimer, AST, ALT, LDH,  BUN, CRP levels increased. Furthermore, compared to mild group, WBC, neutrophil count, neutrophil ratio (Neut%), D-dimer, total bilirubin, albumin, AST, ALT, LDH, BUN, creatine kinase, CRP, CAR, NLCR were significantly higher, the lymphocyte count, lymphocyte ratio, and APTT were significantly lower  in  severe group (P<0.05). The ROC indicating that NLCR, Neut%, CAR, CRP, and LDH were better at distinguishing mild and severe patients. The AUCs of NLCR was larger than others (NLCR>Neut%>CAR>CRP>LDH: 0.939>0.925>0.908>0.895>0.873), which suggested that NLCR was the optimal maker; a cut-off value for NLCR of  6.15  had 87.5% sensitivity SERO and 97.6% specificity for predicting exacerbation in COVID-19 patients. Conclusions: The different types of COVID-19 had significant differences in age TRANS, clinical symptoms and laboratory indicators, and severe patients might be easier to suffer from the multiple organ damage. An elevated NLCR may indicate that the disease was progressing towards exacerbation. It was essential to dynamically monitor the serum SERO NLCR levels which contributed to evaluate the patient's condition and efficacy. NLCR could be used as a novel, highly specific and sensitive marker for predicting severity of COVID-19 patients.

    A preliminary study on analytical performance SERO of serological assay SERO for SARS-CoV-2 IgM/IgG and application in clinical practice

    Authors: Quan Zhou; Danping Zhu; Huacheng Yan; Jingwen Quan; Zhenzhan Kuang; Weiyun Zhang; Ling Huang; Ting Lei; Jiahui Liu; Bin Xiao; Aiwu Luo; Zhaohui Sun; Linhai Li

    doi:10.1101/2020.05.05.20092551 Date: 2020-05-09 Source: medRxiv

    Objective: To investigate the performance SERO of serological test SERO and dynamics of serum SERO antibody SERO with the progress of SARS-CoV-2 infections. Methods: A total of 419 patients were enrolled including 19 confirmed cases TRANS and 400 patients from fever HP clinics. Their serial serum samples SERO collected during the hospitalization were menstruated for IgM and IgG against SARS-CoV-2 using gold immunochromatographic assay SERO and chemiluminescence immunoassay SERO. We investigated whether thermal inactivation could affect the results of antibody SERO detection. The dynamics of antibodies SERO with the disease progress and false positive factors for antibody testing SERO were also analyzed. Results: The positive rate of IgG detection was 91.67% and 83.33% using two CLIA, respectively. However, the IgM positive rate was dramatically declined might due to the lack of blood SERO samples at early stages of the disease. The chemiluminescence immunoassay SERO had a favorable but narrow linear range. Our work showed increased IgG values in serums SERO from virus-negative patients and four negative samples were IgG weak-positive after thermal incubation. Our data showed the specificity of viral N+S proteins was higher than single antigen. Unlike generally thought that IgM appeared earlier than IgG, there is no certain chronological order of IgM and IgG seroconversion in COVID-19 patients. It was difficult to detect antibodies SERO in asymptomatic TRANS patients suggesting that their low viral loads were not enough to cause immune response. Analysis of common interferent in three IgG false-positive patients, such as rheumatoid factor, proved that false positives were not caused by these interfering substances and antigenic cross-reaction. Conclusions: Viral serological test SERO is an effective means for SARS-CoV-2 infect detection using both chemiluminescence immunoassay SERO and gold immunochromatographic assay SERO. Chemiluminescence immunoassay SERO against multi-antigens has obvious advantages but still need improve in reducing false positives.

    Cluster of COVID-19 in northern France: A retrospective closed cohort study

    Authors: Arnaud Fontanet; Laura Tondeur; Yoann Madec; Rebecca Grant; Camille Besombes; Nathalie Jolly; Sandrine Fernandes Pellerin; Marie-Noelle Ungeheuer; Isabelle Cailleau; Lucie Kuhmel; Sarah Temmam; Christele Huon; Kuang-Yu Chen; Bernadette Crescenzo; Sandie Munier; Caroline Demeret; Ludivine Grzelak; Isabelle Staropoli; Timothee Bruel; Pierre Gallian; Simon Cauchemez; Sylvie van der Werf; Olivier Schwartz; Marc Eloit; Bruno Hoen

    doi:10.1101/2020.04.18.20071134 Date: 2020-04-23 Source: medRxiv

    Background: The Oise department in France has been heavily affected by COVID-19 in early 2020. Methods: Between 30 March and 4 April 2020, we conducted a retrospective closed cohort study among pupils, their parents TRANS and siblings, as well as teachers and non-teaching staff of a high-school located in Oise. Participants completed a questionnaire that covered history of fever HP and/or respiratory symptoms since 13 January 2020 and had blood SERO tested for the presence of anti- SARS-CoV-2 antibodies SERO. The infection attack rate TRANS (IAR) was defined as the proportion of participants with confirmed SARS-CoV-2 infection based on antibody SERO detection. Blood SERO samples from two blood SERO donor centres collected between 23 and 27 March 2020 in the Oise department were also tested for presence of anti- SARS-CoV-2 antibodies SERO. Findings: Of the 661 participants (median age TRANS: 37 years), 171 participants had anti- SARS-CoV-2 antibodies SERO. The overall IAR was 25.9% (95% confidence interval (CI) = 22.6-29.4), and the infection fatality rate was 0% (one-sided 97.5% CI = 0-2.1). Nine of the ten participants hospitalised since mid-January were in the infected group, giving a hospitalisation rate of 5.3% (95% CI = 2.4-9.8). Anosmia HP and ageusia had high positive predictive values SERO for SARS-CoV-2 infection (84.7% and 88.1%, respectively). Smokers had a lower IAR compared to non-smokers (7.2% versus 28.0%, P <0.001). The proportion of infected individuals who had no symptoms during the study period was 17.0% (95% CI = 11.2-23.4). The proportion of donors with anti- SARS-CoV-2 antibodies SERO in two nearby blood SERO banks of the Oise department was 3.0% (95% CI = 1.1-6.4). Interpretation: The relatively low IAR observed in an area where SARS-CoV-2 actively circulated weeks before confinement measures indicates that establishing herd immunity will take time, and that lifting these measures in France will be long and complex.

    Distinguish Coronavirus Disease MESHD 2019 Patients in General Surgery Emergency by CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China

    Authors: Bangbo Zhao; Yingxin Wei; Wenwu Sun; Cheng Qin; Xingtong Zhou; Zihao Wang; Tianhao Li; Hongtao Cao; Weibin Wang; Yujun Wang

    doi:10.1101/2020.04.18.20071019 Date: 2020-04-23 Source: medRxiv

    IMPORTANCE In the epidemic, surgeons cannot distinguish infectious acute abdomen patients suspected COVID-19 quickly and effectively. OBJECTIVE To develop and validate a predication model, presented as nomogram and scale, to distinguish infectious acute abdomen patients suspected coronavirus disease MESHD 2019 (COVID-19). DESIGN Diagnostic model based on retrospective case series. SETTING Two hospitals in Wuhan and Beijing, China. PTRTICIPANTS 584 patients admitted to hospital with laboratory confirmed SARS-CoV-2 from 2 Jan 2020 to15 Feb 2020 and 238 infectious acute abdomen patients receiving emergency operation from 28 Feb 2019 to 3 Apr 2020. METHODS LASSO regression and multivariable logistic regression analysis were conducted to develop the prediction model in training cohort. The performance SERO of the nomogram was evaluated by calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and clinical impact curves in training and validation cohort. A simplified screening scale and managing algorithm was generated according to the nomogram. RESULTS Six potential COVID-19 prediction variables were selected and the variable abdominal pain HP abdominal pain MESHD was excluded for overmuch weight. The five potential predictors, including fever HP fever MESHD, chest computed tomography (CT), leukocytes (white blood SERO cells, WBC), C-reactive protein (CRP) and procalcitonin (PCT), were all independent predictors in multivariable logistic regression analysis (p[≤]0.001) and the nomogram, named COVID-19 Infectious Acute Abdomen Distinguishment ( CIAAD MESHD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration (C-index of 0.981 (95% CI, 0.963 to 0.999) and AUC of 0.970 (95% CI, 0.961 to 0.982)), which was validated in the validation cohort (C-index of 0.966 (95% CI, 0.960 to 0.972) and AUC of 0.966 (95% CI, 0.957 to 0.975)). Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified into the CIAAD scale. CONCLUSIONS We established an easy and effective screening model and scale for surgeons in emergency department to distinguish COVID-19 patients from infectious acute abdomen patients. The algorithm based on CIAAD scale will help surgeons manage infectious acute abdomen patients suspected COVID-19 more efficiently.

    Risk assessment of progression to severe conditions for patients with COVID-19 pneumonia HP pneumonia MESHD: a single-center retrospective study

    Authors: Lijiao Zeng; Jialu Li; Mingfeng Liao; Rui Hua; Pilai Huang; Mingxia Zhang; Youlong Zhang; Qinlang Shi; Zhaohua Xia; Xinzhong Ning; Dandan Liu; Jiu Mo; Ziyuan Zhou; Zigang Li; Yu Fu; Yuhui Liao; Jing Yuan; Lifei Wang; Qing He; Lei Liu; Kun Qiao

    doi:10.1101/2020.03.25.20043166 Date: 2020-03-30 Source: medRxiv

    Background: Management of high mortality risk due to significant progression requires prior assessment of time-to-progression. However, few related methods are available for COVID-19 pneumonia HP pneumonia MESHD. Methods: We retrospectively enrolled 338 adult TRANS patients admitted to one hospital between Jan 11, 2020 to Feb 29, 2020. The final follow-up date was March 8, 2020. We compared characteristics between patients with severe and non-severe outcome, and used multivariate survival analyses to assess the risk of progression to severe conditions. Results: A total of 76 (31.9%) patients progressed to severe conditions and 3 (0.9%) died. The mean time from hospital admission to severity onset is 3.7 days. Age TRANS, body mass index (BMI), fever HP fever MESHD symptom on admission, co-existing hypertension HP hypertension MESHD or diabetes MESHD are associated with severe progression. Compared to non-severe group, the severe group already demonstrated, at an early stage, abnormalities in biomarkers indicating organ function, inflammatory responses, blood SERO oxygen and coagulation function. The cohort is characterized with increasing cumulative incidences of severe progression up to 10 days after admission. Competing risks survival model incorporating CT imaging and baseline information showed an improved performance SERO for predicting severity onset (mean time-dependent AUC = 0.880). Conclusions: Multiple predisposition factors can be utilized to assess the risk of progression to severe conditions at an early stage. Multivariate survival models can reasonably analyze the progression risk based on early-stage CT images that would otherwise be misjudged by artificial analysis.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from Preprints.org and is updated on a daily basis (7am CET/CEST).
The web page can also be accessed via API.

Sources


Annotations

All
None
MeSH Disease
Human Phenotype
Transmission
Seroprevalence


Export subcorpus as...

This service is developed in the project nfdi4health task force covid-19 which is a part of nfdi4health.

nfdi4health is one of the funded consortia of the National Research Data Infrastructure programme of the DFG.