Corpus overview


MeSH Disease

Human Phenotype


    displaying 1 - 10 records in total 173
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    Individualized Prediction of COVID-19 Adverse outcomes with MLHO

    Authors: Hossein Estiri; Zachary H. Strasser; Shawn N. Murphy

    id:2008.03869v1 Date: 2020-08-10 Source: arXiv

    The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of the COVID-19 adverse outcomes on individual patients could have led to better allocation of healthcare resources and more efficient targeted preventive measures. We developed MLHO (pronounced as melo) for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death MESHD from patients' past (before COVID-19 infection MESHD) medical records. MLHO is an end-to-end Machine Learning pipeline that implements iterative sequential representation mining and feature and model selection to predict health outcomes. MLHO's architecture enables a parallel and outcome-oriented calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested and leveraged to improve prediction of health outcomes. Using clinical data from a large cohort of over 14,000 patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' before-COVID health records. Overall, the best predictions were obtained from extreme and gradient boosting models. The median AUC ROC for mortality prediction was 0.91, while the prediction performance SERO ranged between 0.79 and 0.83 for ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence on predicting each outcome. As COVID-19 cases are re-surging in the U.S. and around the world, a Machine Learning pipeline like MLHO is crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases MESHD that may emerge in the near future.

    SARS-CoV-2 antigens expressed in plants detect antibody SERO responses in COVID-19 patients

    Authors: Mohau S Makatsa; Marius B Tincho; Jerome M Wendoh; Sherazaan D Ismail; Rofhiwa Nesamari; Francisco Pera; Scott de Beer; Anura David; Sarika Jugwanth; Maemu P Gededzha; Nakampe Mampeule; Ian Sanne; Wendy Stevens; Lesley Scott; Jonathan Blackburn; Elizabeth S Mayne; Roanne S Keeton; Wendy A Burgers

    doi:10.1101/2020.08.04.20167940 Date: 2020-08-04 Source: medRxiv

    Background: The SARS-CoV-2 pandemic has swept the world and poses a significant global threat to lives and livelihoods, with over 16 million confirmed cases TRANS and at least 650 000 deaths MESHD from COVID-19 in the first 7 months of the pandemic. Developing tools to measure seroprevalence SERO and understand protective immunity to SARS-CoV-2 is a priority. We aimed to develop a serological assay SERO using plant-derived recombinant viral proteins, which represent important tools in less-resourced settings. Methods: We established an indirect enzyme-linked immunosorbent assay SERO ( ELISA SERO) using the S1 and receptor-binding domain (RBD) portions of the spike protein from SARS-CoV-2, expressed in Nicotiana benthamiana. We measured antibody SERO responses in sera from South African patients (n=77) who had tested positive by PCR for SARS-CoV-2. Samples were taken a median of six weeks after the diagnosis, and the majority of participants had mild and moderate COVID-19 disease MESHD. In addition, we tested the reactivity of pre-pandemic plasma SERO (n=58) and compared the performance SERO of our in-house ELISA SERO with a commercial assay. We also determined whether our assay could detect SARS-CoV-2-specific IgG and IgA in saliva. Results: We demonstrate that SARS-CoV-2-specific immunoglobulins are readily detectable using recombinant plant-derived viral proteins, in patients who tested positive for SARS-CoV-2 by PCR. Reactivity to S1 and RBD was detected in 51 (66%) and 48 (62%) of participants, respectively. Notably, we detected 100% of samples identified as having S1-specific antibodies SERO by a validated, high sensitivity SERO commercial ELISA SERO, and OD values were strongly and significantly correlated between the two assays. For the pre-pandemic plasma SERO, 1/58 (1.7%) of samples were positive, indicating a high specificity for SARS-CoV-2 in our ELISA SERO. SARS-CoV-2-specific IgG correlated significantly with IgA and IgM responses. Endpoint titers of S1- and RBD-specific immunoglobulins ranged from 1:50 to 1:3200. S1-specific IgG and IgA were found in saliva samples from convalescent volunteers. Conclusions: We demonstrate that recombinant SARS-CoV-2 proteins produced in plants enable robust detection of SARS-CoV-2 humoral responses. This assay can be used for seroepidemiological studies and to measure the strength and durability of antibody SERO responses to SARS-CoV-2 in infected patients in our setting.

    Data-driven modeling of public risk perception and emotion on Twitter during the Covid-19 pandemic

    Authors: Blas Kolic; Joel Dyer

    id:2008.00854v1 Date: 2020-08-03 Source: arXiv

    Successful navigation of the Covid-19 pandemic is predicated on public cooperation with safety measures and appropriate perception of risk, in which emotion and attention play important roles. Signatures of public emotion and attention are present in social media data, thus natural language analysis of this text enables near-to-real-time monitoring of indicators of public risk perception. We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from approx. 20 million unique Covid-19-related tweets from 12 countries posted between 10th March and 14th June 2020. We find evidence of psychophysical numbing: Twitter users increasingly fixate on mortality, but in a decreasingly emotional and increasingly analytic tone. We find that the national attention on Covid-19 mortality is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths MESHD rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective. Our parameter estimates for these models are consistent with estimates from psychological experiments, and indicate that users in this dataset exhibit differential sensitivity SERO by country to the national Covid-19 death MESHD rates. Our work illustrates the potential utility of social media for monitoring public risk perception and guiding public communication during crisis scenarios.

    Implementation of Stacking Based ARIMA Model for Prediction of Covid-19 Cases in India

    Authors: Aman Swaraj; Arshpreet Kaur; Karan Verma; Ghanshyam Singh; Ashok Kumar; Leandro Melo de Sales

    doi:10.21203/ Date: 2020-08-01 Source: ResearchSquare

    Background: Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease TRANS disease MESHD. The novel coronavirus disease MESHD, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely.Methods: We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data.Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance SERO evaluation parameters.Result:The hybrid combination displayed significant reduction in RMSE(16.23%), MAE(37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths MESHD and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries.Conclusion: Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.

    Fighting COVID-19 spread among nursing home residents even in absence of molecular diagnosis: a retrospective cohort study.

    Authors: Alessio Strazzulla; Paul Tarteret; Maria Concetta Postorino; Marie Picque; Astrid de Pontfarcy; Nicolas Vignier; Catherine Chakvetadze; Coralie Noel; Cecile Drouin; Zine Eddine Benguerdi; Sylvain Diamantis

    doi:10.21203/ Date: 2020-07-30 Source: ResearchSquare

    Background Access to molecular diagnosis was limited out-of-hospital in France during the 2020 coronavirus disease MESHD 2019 (COVID-19) epidemic. This study describes the evolution of COVID-19 outbreak in a nursing home in absence of molecular diagnosis. Methods A monocentric prospective study was conducted in a French nursing home from March 17th, 2020 to June 11th, 2020. Because of lack of molecular tests for severe acute respiratory syndrome MESHD 2 (SARS-Cov2) infection MESHD, probable COVID-19 cases were early identified considering only respiratory and not-respiratory symptoms and therefore preventing measures and treatments were enforced. Once available, serology tests were performed at the end of the study.A chronologic description of new cases and deaths MESHD was made together with a description of COVID-19 symptoms. Data about personal characteristics and treatments were collected and the following comparisons were performed: i) probable COVID-19 cases vs asymptomatic TRANS residents; ii) SARS-Cov2 seropositive residents vs seronegative residents. Results Overall, 32/66 (48.5%) residents and 19/39 (48.7%) members of health-care personnel were classified as probable COVID-19 cases. A total of 34/61 (55.7%) tested residents resulted seropositive. Death MESHD occurred in 4/66 (6%) residents. Diagnosis according to symptoms had 65% of sensitivity SERO, 78% of specificity, 79% of positive predictive value SERO and 64% of negative predictive value SERO.In resident population, the following symptoms were registered: 15/32 (46.8%) lymphopenia MESHD lymphopenia HP, 15/32 (46.8%) fever MESHD fever HP, 8/32 (25%) fatigue MESHD fatigue HP, 8/32 (25%) cough MESHD cough HP, 6/32 (18.8%) diarrhoea, 4/32 (12.5%) severe respiratory distress HP requiring oxygen therapy, 4/32 (12.5%) fall HP, 3/32 (9.4%) conjunctivitis MESHD conjunctivitis HP, 2/32 (6.3%) abnormal pulmonary noise at chest examination and 2/32 (6,25%) abdominal pain MESHD abdominal pain HP. Probable COVID-19 cases were older (81.3 vs 74.9; p=0.007) and they had higher prevalence SERO of atrial fibrillation MESHD atrial fibrillation HP (8/32, 25% vs 2/34, 12%; p=0.030); insulin treatment (4/34, 12% vs 0, 0%; p=0.033) and positive SARS-Cov2 serology (22/32, 69% vs 12/34, 35%; p=0.001) than asymptomatic TRANS residents. Seropositive residents had lower prevalence SERO of diabetes (4/34, 12% vs 9/27, 33%; p=0.041) and angiotensin-converting-enzyme inhibitors’ intake (1/34, 1% vs 5/27, 19%; p=0.042). Conclusions During SARS-Cov2 epidemic, early detection of respiratory and not-respiratory symptoms allowed to enforce extraordinary measures. They achieved limiting contagion and deaths MESHD among nursing home residents, even in absence of molecular diagnosis.

    Model stability of COVID-19 mortality prediction with biomarkers

    Authors: Chenyan Huang; Xi Long; Zhuozhao Zhan; Edwin van den Heuvel

    doi:10.1101/2020.07.29.20161323 Date: 2020-07-30 Source: medRxiv

    Coronavirus disease MESHD 2019 (COVID-19) is an unprecedented and fast evolving pandemic, which has caused a large number of critically ill patients and deaths MESHD globally. It is an acute public health crisis leading to overloaded critical care capacity. Timely prediction of the clinical outcome ( death MESHD/survival) of hospital-admitted COVID-19 patients can provide early warnings to clinicians, allowing improved allocation of medical resources. In a recently published paper, an interpretable machine learning model was presented to predict the mortality of COVID-19 patients with blood SERO biomarkers, where the model was trained and tested on relatively small data sets. However, the model or performance SERO stability was not explored and assessed. By re-analyzing the data, we reveal that the reported mortality prediction performance SERO was likely over-optimistic and its uncertainty was underestimated or overlooked, with a large variability in predicting deaths MESHD.

    COVID-19: Time-Dependent Effective Reproduction Number TRANS and Sub-notification Effect Estimation Modeling

    Authors: Eduardo Atem De Carvalho; Rogerio Atem De Carvalho

    doi:10.1101/2020.07.28.20164087 Date: 2020-07-30 Source: medRxiv

    Background: Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection MESHD and death MESHD cycles, in order to be able to make better decisions. In particular, a series of reproduction number TRANS estimation models have been presented, with different practical results. Objective: This article aims to present an effective and efficient model for estimating the Reproduction Number TRANS and to discuss the impacts of sub-notification on these calculations. Methods: The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number TRANS, is derived from experimental data. The models are applied to real data and their performance SERO is presented. Results: Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance SERO of the methods here introduced. Conclusions: We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent Reproduction Numbers TRANS (Rt), as well as we demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

    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.

    Changes in Cause-of- Death MESHD Attribution During the Covid-19 Pandemic: Association with Hospital Quality Metrics and Implications for Future Research

    Authors: Kathleen A. Fairman; Kellie J. Goodlet; James D. Rucker

    doi:10.1101/2020.07.25.20162198 Date: 2020-07-28 Source: medRxiv

    Background: Severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) is often comorbid with conditions subject to quality metrics (QM) used for hospital performance SERO assessment and rate-setting. Although diagnostic coding change in response to financial incentives is well documented, no study has examined the association of QM with SARS-CoV-2 cause-of- death MESHD attribution (CODA). Calculations of excess all-cause deaths MESHD overlook the importance of accurate CODA and of distinguishing policy-related from virus-related mortality. Objective: Examine CODA, overall and for QM and non-QM diagnoses, in 3 pandemic periods: awareness (January 19-March 14), height (March 15-May 16), and late (May 17-June 20). Methods: Retrospective analysis of publicly available national weekly COD data, adjusted for population growth and reporting lags, October 2014-June 20, 2020. CODA in 5 pre-pandemic influenza seasons was compared with 2019-20. Suitability of the data to distinguish policy-related from virus-related effects was assessed. Results: Following federal guidance permitting SARS-CoV-2 CODA without laboratory testing, mortality from the QM diagnoses cancer and chronic lower respiratory disease MESHD declined steadily relative to prior-season means, reaching 4.4% less and 12.1% less, respectively, in late pandemic. Deaths MESHD for non-QM diagnoses increased, by 21.0% for Alzheimers disease MESHD Alzheimers disease HP and 29.0% for diabetes during pandemic height. Increases in competing CODs over historical experience, suggesting SARS-CoV-2 underreporting, more than offset declines during pandemic height. However, in the late-pandemic period, declines slightly numerically exceeded increases, suggesting SARS-CoV-2 overreporting. In pandemic-height and late-pandemic periods, respectively, only 83.5% and 69.7% of increases in all-cause deaths MESHD were explained by changes in the reported CODs, including SARS-CoV-2, preventing assessment of policy-related mortality or of factors contributing to increased all-cause deaths MESHD. Conclusions: Substitution of SARS-CoV-2 for competing CODs may have occurred, particularly for QM diagnoses and late in the pandemic. Continued monitoring of these trends, qualitative research on pandemic CODA, and the addition of place-of- death MESHD data and psychiatric CODs to the file would facilitate assessment of policy-related and virus-related effects on mortality.

    Trends of in-hospital and 30-day mortality after percutaneous coronary intervention in England before and after the COVID-19 era

    Authors: Mohamed O Mohamed; Tim Kinnaird; Nick Curzen; Peter Ludman; Jianhua Wu; Muhammad Rashid; Ahmad Shoaib; Mark de Belder; John Deanfield; Chris Gale; Mamas A Mamas

    doi:10.1101/2020.07.18.20155549 Date: 2020-07-27 Source: medRxiv

    Objectives: To examine short-term primary causes of death MESHD after percutaneous coronary intervention (PCI) in a national cohort before and during COVID-19. Background: Public reporting of PCI outcomes is a performance SERO metric and a requirement in many healthcare systems. There are inconsistent data on the causes of death MESHD after PCI, and what proportion of these are attributable to cardiac causes. Methods: All patients undergoing PCI in England between 1st January 2017 and 10th May 2020 were retrospectively analysed (n=273,141), according to their outcome from the date of PCI; no death MESHD and in-hospital, post-discharge, and 30-day death MESHD. Results: The overall rates of in-hospital and 30-day death MESHD were 1.9% and 2.8%, respectively. The rate of 30-day death MESHD declined between 2017 (2.9%) and February 2020 (2.5%), mainly due to lower in-hospital death MESHD (2.1% vs. 1.5%), before rising again from 1st March 2020 (3.2%) due to higher rates of post-discharge mortality. Only 59.6% of 30-day deaths MESHD were due to cardiac causes, the most common being acute coronary syndrome MESHD, cardiogenic shock MESHD cardiogenic shock HP and heart failure MESHD, and this persisted throughout the study period. 10.4% of 30-day deaths MESHD after 1st March 2020 were due to confirmed COVID-19. Conclusions: In this nationwide study, we show that 40% of 30-day deaths MESHD are due to non-cardiac causes. Non-cardiac deaths MESHD have increased even more from the start of the COVID-19 pandemic, with one in ten deaths MESHD from March 2020 being COVID-19 related. These findings raise a question of whether public reporting of PCI outcomes should be cause-specific.

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MeSH Disease
Human Phenotype

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