Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
    displaying 1 - 10 records in total 85
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    Performance SERO of Abbott Architect, Ortho Vitros, and Euroimmun Assays in Detecting Prior SARS-CoV-2 Infection MESHD

    Authors: Shiwani Mahajan; Carrie A Redlich; Adam V Wisnewski; Louis E Fazen; Lokinendi V Rao; Karthik Kuppusamy; Albert I Ko; Harlan M Krumholz

    doi:10.1101/2020.07.29.20164343 Date: 2020-07-30 Source: medRxiv

    Background: Several serological assays SERO have been developed to detect anti-SARS-CoV-2 IgG antibodies SERO, but evidence about their comparative performance SERO is limited. We sought to assess the sensitivity SERO of four anti-SARS-CoV-2 IgG enzyme-linked immunosorbent assays SERO ( ELISA SERO) in individuals with evidence of prior SARS-CoV-2 infection MESHD. Methods: We obtained sera from 36 individuals with PCR-confirmed SARS-CoV-2 infection MESHD between March and May 2020. We evaluated samples collected at around 21 days ({+/-}14 days) after their initial PCR test using 3 commercially available ELISA assays SERO, two anti-spike (Ortho-Clinical Diagnostics Vitros, and Euroimmun) and one anti-nucleocapsid (Abbott Architect), and a Yale-developed anti-spike ELISA SERO test. We determined the sensitivity SERO of the tests and compared their results. The Euroimmun and Yale ELISA SERO had an equivocal and indeterminate category, which were considered as both negative and positive. Results: Among the 36 individuals with SARS-CoV-2 infection MESHD, mean age TRANS was 43 ({+/-}13) years and 19 (53%) were female TRANS. The sensitivities SERO of the tests were not significantly different (Abbott Architect, Ortho Vitros, Euroimmmun, and Yale assays: 86% (95% confidence interval [CI], 71-95), 94% (95% CI, 81-99), 86% (95% CI, 71-95), and 94% (95% CI, 81-99), respectively; p-value=0.464). The sensitivities SERO of the Euroimmun and Yale ELISA SERO tests increased when the equivocal/indeterminate results were considered positive (97% [95% CI, 85-100] and 100% [95% CI, 90-100], respectively), but were not significantly different from other tests (p=0.082). The cross-correlation coefficient ranged from 0.85-0.98 between three anti-spike protein assays (Ortho Vitros, Euroimmun, Yale) and was 0.58-0.71 between the three anti-spike protein assays and the anti-nucleocapsid assay (Abbott). Conclusion: The sensitivities SERO of four anti-SARS-CoV-2 protein assays did not significantly differ, although the sample size was small. Sensitivity SERO also depended on the interpretation of equivocal and indeterminate results. The strongest correlations were present for the three anti-spike proteins assays. These findings suggest that individual test characteristics and the correlation between different tests should be considered when comparing or aggregating data across different populations studies for serologic surveillance of past SARS-CoV-2 infection MESHD.

    Diagnostic Accuracy of Computed Tomography for Identifying Hospitalization in Patients with Suspected COVID-19

    Authors: Sergey P. Morozov; Roman V. Reshetnikov; Victor A. Gombolevskiy; Natalia V. Ledikhova; Ivan A. Blokhin; Vladislav G. Kljashtorny; Olesya A. Mokienko; Anton V. Vladzymyrskyy

    id:2007.15476v2 Date: 2020-07-29 Source: arXiv

    The controversy of computed tomography (CT) use in COVID-19 screening is associated with ambiguous characteristics of chest CT as a diagnostic test. The reported values of CT sensitivity SERO and specificity calculated using RT-PCR as a reference standard vary widely. The objective of this study was to reevaluate the diagnostic and prognostic value of CT using an alternative approach. This study included 973 symptomatic COVID-19 patients aged TRANS 42 $\pm$ 17 years, 56% females TRANS. We reviewed the disease MESHD dynamics between the initial and follow-up CT studies using a "CT0-4" grading system. Sensitivity SERO and specificity were calculated as conditional probabilities that a patient's condition would improve or deteriorate relative to the initial CT study results. For the calculation of negative (NPV) and positive (PPV) predictive values, we estimated the COVID-19 prevalence SERO in Moscow. We used several ARIMA and EST models with different parameters to fit the data on total cases of COVID-19 from March 6, 2020, to July 20, 2020, and forecast the incidence. The "CT0-4" grading scale demonstrated low sensitivity SERO (28%) but high specificity (95%). The best statistical model for describing the pandemic in Moscow was ETS with multiplicative trend, error, and season type. According to our calculations, with the predicted prevalence SERO of 2.1%, the values of NPV and PPV would be 98% and 10%, correspondingly. We associate the low sensitivity SERO and PPV values with the small sample size of the patients with severe symptoms and non-optimal methodological setup for measuring these specific characteristics. The "CT0-4" grading scale was highly specific and predictive for identifying admissions to hospitals of COVID-19 patients. Despite the ambiguous accuracy, chest CT proved to be an effective practical tool for patient management during the pandemic, provided that the necessary infrastructure and human resources are available.

    Seroprevalence SERO of anti-SARS-CoV-2 IgG antibodies SERO in Kenyan blood SERO donors

    Authors: Sophie Uyoga; Ifedayo M.O. Adetifa; Henry K. Karanja; James Nyagwange; James Tuju; Perpetual Wanjiku; Rashid Aman; Mercy Mwangangi; Patrick Amoth; Kadondi Kasera; Wangari Ng'ang'a; Charles Rombo; Christine K. Yegon; Khamisi Kithi; Elizabeth Odhiambo; Thomas Rotich; Irene Orgut; Sammy Kihara; Mark Otiende; Christian Bottomley; Zonia N. Mupe; Eunice W. Kagucia; Katherine Gallagher; Anthony Etyang; Shirine Voller; John Gitonga; Daisy Mugo; Charles N. Agoti; Edward Otieno; Leonard Ndwiga; Teresa Lambe; Daniel Wright; Edwine Barasa; Benjamin Tsofa; Philip Bejon; Lynette I. Ochola-Oyier; Ambrose Agweyu; J. Anthony G. Scott; George M Warimwe

    doi:10.1101/2020.07.27.20162693 Date: 2020-07-29 Source: medRxiv

    Background There are no data on SARS-CoV-2 seroprevalence SERO in Africa though the COVID-19 epidemic curve and reported mortality differ from patterns seen elsewhere. We estimated the anti- SARS-CoV-2 antibody SERO prevalence SERO among blood SERO donors in Kenya. Methods We measured anti-SARS-CoV-2 spike IgG prevalence SERO by ELISA SERO on residual blood SERO donor samples obtained between April 30 and June 16, 2020. Assay sensitivity SERO and specificity were 83% (95% CI 59, 96%) and 99.0% (95% CI 98.1, 99.5%), respectively. National seroprevalence SERO was estimated using Bayesian multilevel regression and post-stratification to account for non-random sampling with respect to age TRANS, sex and region, adjusted for assay performance SERO. Results Complete data were available for 3098 of 3174 donors, aged TRANS 15-64 years. By comparison with the Kenyan population, the sample over-represented males TRANS (82% versus 49%), adults TRANS aged TRANS 25-34 years (40% versus 27%) and residents of coastal Counties (49% versus 9%). Crude overall seroprevalence SERO was 5.6% (174/3098). Population-weighted, test-adjusted national seroprevalence SERO was 5.2% (95% CI 3.7, 7.1%). Seroprevalence SERO was highest in the 3 largest urban Counties; Mombasa (9.3% [95% CI 6.4, 13.2%)], Nairobi (8.5% [95% CI 4.9, 13.5%]) and Kisumu (6.5% [95% CI 3.3, 11.2%]). Conclusions We estimate that 1 in 20 adults TRANS in Kenya had SARS-CoV-2 antibodies SERO during the study period. By the median date of our survey, only 2093 COVID-19 cases and 71 deaths MESHD had been reported through the national screening system. This contrasts, by several orders of magnitude, with the numbers of cases and deaths MESHD reported in parts of Europe and America when seroprevalence SERO was similar.

    Eleven Routine Clinical Features Predict COVID-19 Severity

    Authors: Kai Zhou; Yaoting Sun; Lu Li; Zelin Zang; Jing Wang; Jun Li; Junbo Liang; Fangfei Zhang; Qiushi Zhang; Weigang Ge; Hao Chen; Xindong Sun; Liang Yue; Xiaomai Wu; Bo Shen; Jiaqin Xu; Hongguo Zhu; Shiyong Chen; Hai Yang; Shigao Huang; Minfei Peng; Dongqing Lv; Chao Zhang; Haihong Zhao; Luxiao Hong; Zhehan Zhou; Haixiao Chen; Xuejun Dong; Chunyu Tu; Minghui Li; Yi Zhu; Baofu Chen; Stan Z. Li; Tiannan Guo

    doi:10.1101/2020.07.28.20163022 Date: 2020-07-29 Source: medRxiv

    Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort consisting of training, validation, and internal test sets, longitudinally recorded 124 routine clinical and laboratory parameters, and built a machine learning model to predict the disease progression MESHD based on measurements from the first 12 days since the disease MESHD onset when no patient became severe. A panel of 11 routine clinical factors, including oxygenation index, basophil counts, aspartate aminotransferase, gender TRANS, magnesium, gamma glutamyl transpeptidase, platelet counts, activated partial thromboplastin time, oxygen saturation, body temperature and days after symptom onset TRANS, constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity SERO, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. Our model captured predictive dynamics of LDH and CK while their levels were in the normal range. This study presents a practical model for timely severity prediction and surveillance for COVID-19, which is freely available at webserver https://guomics.shinyapps.io/covidAI/.

    Predicting Critical State after COVID-19 Diagnosis Using Real-World Data from 20152 Confirmed US Cases TRANS

    Authors: Mike Domenik Rinderknecht; Yannick Klopfenstein

    doi:10.1101/2020.07.24.20155192 Date: 2020-07-27 Source: medRxiv

    The global COVID-19 pandemic caused by the virus SARS-CoV-2 has led to over 10 million confirmed cases TRANS, half a million deaths MESHD, and is challenging healthcare systems worldwide. With limited medical resources, early identification of patients with a high risk of progression to severe disease MESHD or a critical state is crucial. We present a prognostic model predicting critical state within 28 days following COVID-19 diagnosis trained on data from US electronic health records (EHR) within IBM Explorys, including demographics, comorbidities, symptoms, laboratory test results, insurance types, and hospitalization. Our entire cohort included 20152 COVID-19 cases, of which 3160 patients went into critical state or died. Random, stratified train-test splits were repeated 100 times to obtain a distribution of performance SERO. The median and interquartile range of the areas under the receiver operating characteristic curve (ROC AUC) and the precision recall SERO curve (PR AUC) were 0.863 [0.857, 0.866] and 0.539 [0.526, 0.550], respectively. Optimizing the decision threshold lead to a sensitivity SERO of 0.796 [0.775, 0.821] and a specificity of 0.784 [0.769, 0.805]. Good model calibration was achieved, showing only minor tendency to over-forecast probabilities above 0.6. The validity of the model was demonstrated by the interpretability analysis confirming existing evidence on major risk factors (e.g., higher age TRANS and weight, male TRANS gender TRANS, diabetes, cardiovascular disease MESHD disease, and chronic kidney HP kidney disease MESHD). The analysis also revealed higher risk for African Americans and "self-pay patients". To the best of our knowledge, this is the largest dataset based on EHR used to create a prognosis model for COVID-19. In contrast to large-scale statistics computing odds ratios for individual risk factors, the present model combining a rich set of covariates can provide accurate personalized predictions enabling early treatment to prevent patients from progressing to a severe or critical state.

    Initial Experience in Predicting the Risk of Hospitalization of 496 Outpatients with COVID-19 Using a Telemedicine Risk Assessment Tool

    Authors: James B O'Keefe; Elizabeth J Tong; Thomas H Taylor Jr.; Ghazala D Datoo O'Keefe; David C Tong

    doi:10.1101/2020.07.21.20159384 Date: 2020-07-24 Source: medRxiv

    Objective: To determine whether a risk prediction tool developed and implemented in March 2020 accurately predicts subsequent hospitalizations. Design: Retrospective cohort study, enrollment from March 24 to May 26, 2020 with follow-up calls until hospitalization or clinical improvement (final calls until June 19, 2020) Setting: Single center telemedicine program managing outpatients from a large medical system in Atlanta, Georgia Participants: 496 patients with laboratory-confirmed COVID-19 in isolation at home. Exclusion criteria included: (1) hospitalization prior to telemedicine program enrollment, (2) immediate discharge with no follow-up calls due to resolution. Exposure: Acute COVID-19 illness Main Outcome and Measures: Hospitalization was the outcome. Days to hospitalization was the metric. Survival analysis using Cox regression was used to determine factors associated with hospitalization. Results: The risk-assessment rubric assigned 496 outpatients to risk tiers as follows: Tier 1, 237 (47.8%); Tier 2, 185 (37.3%); Tier 3, 74 (14.9%). Subsequent hospitalizations numbered 3 (1%), 15 (7%), and 17 (23%) and for Tiers 1-3, respectively. From a Cox regression model with age TRANS [≥] 60, gender TRANS, and self-reported obesity MESHD obesity HP as covariates, the adjusted hazard ratios using Tier 1 as reference were: Tier 2 HR=3.74 (95% CI, 1.06-13.27; P=0.041); Tier 3 HR=10.87 (95% CI, 3.09-38.27; P<0.001). Tier was the strongest predictor of time to hospitalization. Conclusions and Relevance: A telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified both low-risk and high-risk patients with better performance SERO than individual risk factors alone. This approach may be appropriate for optimum allocation of resources.

    Ocular findings and retinal involvement in COVID-19 pneumonia MESHD pneumonia HP patients: A cross-sectional study in an Italian referral centre

    Authors: Maria Pia Pirraglia; Giancarlo Ceccarelli; Alberto Cerini; Giacomo Visioli; Gabriella d'Ettorre; Claudio Maria Mastroianni; Francesco Pugliese; Alessandro Lambiase; Magda Gharbiya

    doi:10.21203/rs.3.rs-48240/v1 Date: 2020-07-23 Source: ResearchSquare

    Background: changes in immune and coagulation systems and possible viral spread through blood SERO-brain barrier have been described in SARS-CoV-2 infection MESHD. In this study, we evaluate the possible retinal involvement and ocular findings in severe COVID-19 pneumonia MESHD pneumonia HP patients.  Methods: a cross sectional study was conducted on 46 patients affected by severe COVID-19 who were hospitalized in one Intensive Care Unit (ICU) and in two Infectious Diseases MESHD wards, including a bedside eye screening, corneal sensitivity SERO assessment and retinography. Results: a total of 43 SARS-CoV-2 positive pneumonia MESHD pneumonia HP patients affected with COVID-19 pneumonia MESHD pneumonia HP were included, 25 males TRANS and 18 females TRANS, with a median age TRANS of 70 [IQR 59-78]. Except for one patient with unilateral posterior chorioretinitis MESHD chorioretinitis HP of opportunistic origin, of whom aqueous tap was negative for SARS-CoV-2, no further retinal manifestation related to COVID-19 infection MESHD was found in our cohort. We found 3 patients (7%) with bilateral conjunctivitis MESHD conjunctivitis HP in whom PCR analysis on conjunctival swab provided negative results for SARS-CoV-2. No alterations of corneal sensitivity SERO were found.Conclusion: we demonstrated the absence of retinal involvement in SARS-CoV-2 pneumonia MESHD pneumonia HP patients. Ophthalmologic evaluation in COVID-19, particularly in patients hospitalized in an ICU setting, may be useful to reveal systemic co- infections by opportunistic MESHD infections by opportunistic HP pathogens. 

    Clinical Characteristics and Outcomes for 7,995 Patients with SARS-CoV-2 Infection MESHD

    Authors: Jacob McPadden; Frederick Warner; H. Patrick Young; Nathan C. Hurley; Rebecca A. Pulk; Avinainder Singh; Thomas JS Durant; Guannan Gong; Nihar Desai; Adrian Haimovich; Richard Andrew Taylor; Murat Gunel; Charles S. Dela Cruz; Shelli F Farhadian; Jonathan Siner; Merceditas Villanueva; Keith Churchwell; Allen Hsiao; Charles J. Torre Jr.; Eric J. Velazquez; Roy S. Herbst; Akiko Iwasaki; Albert I. Ko; Bobak J. Mortazavi; Harlan M. Krumholz; Wade L. Schulz

    doi:10.1101/2020.07.19.20157305 Date: 2020-07-21 Source: medRxiv

    Objective: Severe acute respiratory syndrome MESHD virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease MESHD severity in patients with SARS-CoV-2. Design: This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository. Setting: Yale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas. Populations: The study included adult TRANS patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020. Main outcome and performance SERO measures: Primary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection MESHD as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support. Results: Of the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection MESHD (median age TRANS 52.3 years) and 2154 (26.9%) of these had an associated admission (median age TRANS 66.2 years). Of admitted patients, 1633 (75.8%) had a discharge disposition at the end of the study period. Of these, 192 (11.8%) required invasive mechanical ventilation and 227 (13.5%) expired. Increased age TRANS and male TRANS sex were positively associated with admission and in-hospital mortality (median age TRANS 81.9 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race (OR 1.43, 95%CI 1.14-1.78) and Hispanic ethnicity (OR 1.81, 95%CI 1.50-2.18) were identified as risk factors for admission, but, among discharged patients, age TRANS-adjusted in-hospital mortality was not significantly different among racial and ethnic groups. Conclusions: This observational study identified, among people testing positive for SARS-CoV-2 infection MESHD, older age TRANS and male TRANS sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection MESHD. While minority racial and ethnic groups had increased burden of disease MESHD and risk of admission, age TRANS-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines.

    The Predictive Value of D-Dimer and DPR for in-Hospital Mortality in Patients with COVID-19

    Authors: Ping-ji Deng; Ming-shui Xie; Zun-qiong Ke; Bin Qiao; Bin-wu Ying; Xue-yan Xi; Leyong Yuan

    doi:10.21203/rs.3.rs-46839/v1 Date: 2020-07-21 Source: ResearchSquare

    Background: The aim of this study was to explore the predictive value of D-dimer and D-dimer to platelet ratio (DPR) on admission in COVID-19 patients. Methods: This is a retrospective cohort study of confirmed COVID-19 patients that were admitted to the Renmin Hospital of Wuhan University from January 30, 2020 to February 15, 2020. The baseline clinical and laboratory parameters were collected.Results: 264 COVID-19 patients were enrolled in this study, of whom 52 died during hospitalization and 212 were discharged from the hospital. Receiver operating characteristic (ROC) curve analysis demonstrated that the area under curve (AUC) of D-dimer was 0.818 with a sensitivity SERO of 75.0% and a specificity of 78.3%, and the AUC of DPR was 0.847 with a sensitivity SERO of 84.6% and a specificity of 75.5% for the prediction of in-hospital mortality on admission. Patients with higher D-dimer or DPR levels were associated with higher mortality risk, compared to those who lower levels. The multivariable Cox regression indicated that in-hospital mortality was associated with age TRANS (hazard ratio (HR) 1.034, 95% confidence interval (CI) 1.011-1.058, P=0.003), gender TRANS (HR 0.553, 95% CI 0.313-0.976; P=0.041), coronary heart disease MESHD (HR 2.315, 95% CI 1.276-4.200; P=0.006), elevated D-dimer (HR 6.111, 95% CI 3.095-12.068; P<0.001), and elevated DPR (HR 7.158, 95% CI 3.633-14.101; P<0.001). Conclusions: DPR levels seem to be slightly better at predicting mortality than D-dimer, and elevated D-dimer and DPR can both be considered independent risk factors of the death MESHD in COVID-19 patients.

    Machine learning based prognostic model for predicting infection MESHD susceptibility of COVID-19 using health care data 

    Authors: R Srivatsan; Prithviraj N Indi; Swapnil Agrahari; Siddharth Menon; Dr. S. Denis Ashok

    doi:10.21203/rs.3.rs-46681/v1 Date: 2020-07-21 Source: ResearchSquare

    From public health perspectives of COVID-19 pandemic, accurate estimates of infection MESHD severity of individuals are extremely valuable for the informed decision making and targeted response to an emerging pandemic.  This paper presents machine learning based prognostic model for providing early warning to the individuals for COVID-19 infection MESHD using the health care data set. In the present work, a prognostic model using Random Forest classifier and support vector regression is developed for predicting the susceptibility of COVID-19 infection MESHD and it is applied on an open health care data set containing 27 field values. The typical fields of the health care data set include basic personal details such as age TRANS, gender TRANS, number of children TRANS in the household, marital status along with medical data like Coma MESHD Coma HP score, Pulmonary score, Blood SERO Glucose level, HDL cholesterol etc. An effective preprocessing method is carried out for handling the numerical, categorical values (non-numerical), missing data in the health care data set. Principal component analysis is applied for dimensionality reduction of the health care data set. From the classification results, it is noted that the random forest classifier provides a higher accuracy as compared to Support vector regression for the given health data set. Proposed machine learning approach can help the individuals to take additional precautions for protecting against COVID-19 infection MESHD. Based on the results of the proposed method, clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread. Methods In the present work, Random Forest classifier and support vector regression techniques are applied to a medical health care dataset containing 27 variables for predicting the susceptibility score of an individual towards COVID-19 infection MESHD and the accuracy of prediction is compared. An effective preprocessing is carried for handling the missing data in the health care data set. Principal Component Analysis is carried out on the data set for dimensionality reduction of the feature vectors. Results From the classification results, it is noted that the Random Forest classifier provides an accuracy of 90%, sensitivity SERO of 94% and specificity of 81% for the given medical data set.Conclusion Proposed machine learning approach can help the individuals to take additional precautions for protecting people from the COVID-19 infection MESHD, clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread. 

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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