Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

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




    LOW BIRTH WEIGHT AS A RISK FACTOR FOR SEVERE COVID-19 IN ADULTS TRANS

    Authors: Fatima Crispi; Francesca Crovetto; Marta Larroya; Marta Camacho; Oriol Sibila; Joan Ramon Badia; Marta Lopez; Kilian Vellve; Ferran Garcia; Antoni Trilla; Rosa Faner; Isabel Blanco; Roger Borras; Alvar Agusti; Eduard Gratacos

    doi:10.1101/2020.09.14.20193920 Date: 2020-09-15 Source: medRxiv

    The identification of factors predisposing to severe COVID-19 in young adults TRANS remains partially characterized. Low birth weight (LBW) alters cardiovascular and lung development and predisposes to adult TRANS disease. We hypothesized that LBW is a risk factor for severe COVID-19 in non- elderly TRANS subjects. We analyzed a prospective cohort of 397 patients (18-70y) with laboratory-confirmed SARS-CoV-2 infection MESHD attended in a tertiary hospital, where 15% required admission to Intensive Care Unit (ICU). Perinatal and current potentially predictive variables were obtained from all patients and LBW was defined as birth weight [≤]2,500 g. Age TRANS (adjusted OR (aOR) 1.04 [1-1.07], P=0.012), male TRANS sex (aOR 3.39 [1.72-6.67], P<0.001), hypertension HP hypertension MESHD (aOR 3.37 [1.69-6.72], P=0.001), and LBW (aOR 3.61 [1.55-8.43], P=0.003) independently predicted admission to ICU. The area under the receiver-operating characteristics curve (AUC) of this model was 0.79 [95% CI, 0.74-0.85], with positive and negative predictive values SERO of 29.1% and 97.6% respectively. Results were reproduced in an independent cohort, from a web-based survey in 1,822 subjects who self-reported laboratory-positive SARS-CoV-2 infection MESHD, where 46 patients (2.5%) needed ICU admission (AUC 0.74 [95% CI 0.68-0.81]). LBW seems to be an independent risk factor for severe COVID-19 in non- elderly TRANS adults TRANS and might improve the performance SERO of risk stratification algorithms.

    Risk Factors Analysis of COVID-19 Patients with ARDS MESHD and Prediction Based on Machine Learning

    Authors: Wan Xu; Nan-Nan Sun; Hai-Nv Gao; Zhi-Yuan Chen; Ya Yang; Bin Ju; Ling-Ling Tang

    doi:10.21203/rs.3.rs-77820/v1 Date: 2020-09-15 Source: ResearchSquare

    COVID-19 is a newly emerging infectious disease MESHD, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress HP respiratory distress MESHD syndrome ( ARDS MESHD) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS MESHD patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the two groups were elaborately compared and both traditional machine learning algorithms MESHD and deep learning-based methods were used to build the prediction models. Results indicated the median age TRANS of ARDS MESHD patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male TRANS and patients with BMI>25 were more likely to develop ARDS MESHD. The clinical features of ARDS MESHD patients included cough HP (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection MESHD (30.3%), and comorbidities such as hypertension HP hypertension MESHD (48.7%). Abnormal biochemical indicators such as lymphocyte count, leukocyte counting, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, sensitivity SERO, and specificity in identifying the mild patients who were easy to develop ARDS MESHD, which undoubtedly helps to optimize the treatment strategy, reduce mortality, and relieve the medical pressure. 

    COVID-19 Inmate Risk Appraisal (CIRA): Development and validation of a screening tool to assess COVID-19 vulnerability in prisons

    Authors: Leonel C. Gonçalves; Stéphanie Baggio; Michael Weber; Laurent Gétaz; Hans Wolff; Jay P. Singh; Andreas Naegeli; Astrid Rossegger; Jérôme Endrass

    doi:10.21203/rs.3.rs-40225/v1 Date: 2020-07-06 Source: ResearchSquare

    Objectives. To develop and validate a screening tool designed to identify detained people at increased risk for COVID-19 mortality, the COVID-19 Inmate Risk Appraisal (CIRA). Design. Cross-sectional study with a representative sample (development) and a case-control sample (validation).Setting. The two largest Swiss prisons.Participants. (1) Development sample: all male TRANS persons detained in Pöschwies, Zurich (n=365); (2) Validation sample: case-control sample of male TRANS persons detained in Champ-Dollon, Geneva (n=192, matching 1:3 for participants at risk for severe course of COVID-19 and participants without risk factors).Main outcome measures. The CIRA combined seven risk factors identified by the World Health Organization and the Swiss Federal Office of Public Health as prognosis of severe COVID-19 to derive an absolute risk increase in mortality rate: Age TRANS ≥60, cardiovascular disease MESHD, diabetes MESHD, hypertension HP hypertension MESHD, chronic respiratory disease MESHD, immunodeficiency HP immunodeficiency MESHD, and cancer MESHD. Results. Based on the development sample, we proposed a three-level classification: average (<3.7), elevated (3.7-5.7), and high (>5.7) risk. In the validation sample, the CIRA identified all individuals considered vulnerable by national recommendations (having at least one risk factor). The category “elevated risk” maximized sensitivity SERO (1) and specificity (.97). The CIRA had even higher capacity in discriminating vulnerable individuals according to clinical evaluation (a four-level risk categorization based on a consensus of medical staff). The category “elevated risk” maximized sensitivity SERO and specificity (both 1). When considering the individuals classified as extremely high risk by medical staff, the category “high risk” had a high discriminatory capacity (sensitivity=.89, specificity=.97). Conclusions. The CIRA scores have a high discriminative ability and will be important in custodial settings to support decisions and prioritize actions using a standardized valid assessment method. However, as knowledge on risk factors for COVID-19 mortality is still limited, the CIRA should be considered preliminary. Underlying data will be updated regularly on the website www.prison-research.com, where the CIRA algorithm is freely available.

    Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong

    Authors: Jiandong Zhou; Gary Tse; Sharen Lee; Tong Liu; William KK Wu; zhidong cao; Dajun Zeng; Ian CK Wong; Qingpeng Zhang; Bernard MY Cheung

    doi:10.1101/2020.06.30.20143651 Date: 2020-07-02 Source: medRxiv

    Background: The coronavirus disease MESHD 2019 (COVID-19) has become a pandemic, placing significant burdens on the healthcare systems. In this study, we tested the hypothesis that a machine learning approach incorporating hidden nonlinear interactions can improve prediction for Intensive care unit (ICU) admission. Methods: Consecutive patients admitted to public hospitals between 1st January and 24th May 2020 in Hong Kong with COVID-19 diagnosed by RT-PCR were included. The primary endpoint was ICU admission. Results: This study included 1043 patients (median age TRANS 35 (IQR: 32-37; 54% male TRANS). Nineteen patients were admitted to ICU (median hospital length of stay (LOS): 30 days, median ICU LOS: 16 days). ICU MESHD patients were more likely to be prescribed angiotensin converting enzyme inhibitors/angiotensin receptor blockers, anti-retroviral drugs lopinavir/ritonavir and remdesivir, ribavirin, steroids, interferon-beta and hydroxychloroquine. Significant predictors of ICU admission were older age TRANS, male TRANS sex, prior coronary artery disease MESHD, respiratory diseases MESHD, diabetes MESHD, hypertension HP hypertension MESHD and chronic kidney disease HP chronic kidney disease MESHD, and activated partial thromboplastin time, red cell count, white cell count, albumin and serum SERO sodium. A tree-based machine learning model identified most informative characteristics and hidden interactions that can predict ICU admission. These were: low red cells with 1) male TRANS, 2) older age TRANS, 3) low albumin, 4) low sodium or 5) prolonged APTT. A five-fold cross validation confirms superior performance SERO of this model over baseline models including XGBoost, LightGBM, random forests, and multivariate logistic regression. Conclusions: A machine learning model including baseline risk factors and their hidden interactions can accurately predict ICU admission in COVID-19.

    Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study

    Authors: Chansik An; Hyunsun Lim; Dong-Wook Kim; Jung Hyun Chang; Yoon Jung Choi; Seong Woo Kim

    doi:10.21203/rs.3.rs-36458/v1 Date: 2020-06-19 Source: ResearchSquare

    The rapid spread of COVID-19 is likely to result in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7,772 (75.9%) recovered, and 2,237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age TRANS > 70, male TRANS sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus HP diabetes mellitus MESHD ( DM MESHD), chronic lung disease HP chronic lung disease MESHD, or asthma HP asthma MESHD were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities SERO (90.3% [95% confidence interval: 83.3, 97.3]and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities >90%. The most significant predictors for LASSO included old age TRANS and preexisting DM MESHD or cancer MESHD; for RF they were old age TRANS, infection MESHD route (at large clusters or from personal contact with an infected individual), and underlying hypertension HP hypertension MESHD. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources have to be wisely allocated without hesitation.

    Corona Virus Disease MESHD 2019 (COVID-19): Intensive Care Admission Prediction Model

    Authors: Mahomood Y. Hachim; Ibrahim Y. Hachim; Kashif Bin Naeem; Haifa Hannawi; Issa Al Salmi; Suad Hannawi

    doi:10.21203/rs.3.rs-35442/v1 Date: 2020-06-14 Source: ResearchSquare

    Background: Identifying clinical-features or a scoring-system to predict a benefit from hospital admission for patients with COVID-19 can be of great value for the decision-makers in the health-sector. We aim to identify differences in patients' demographic, clinical, laboratory and radiological findings of COVID-19 positive cases to develop and validate a diagnostic-model predicting who will develop severe-form and who will need critical-care in the future.Methodology: Patients were classified according to their clinical state into mild, moderate, severe, and critical. All their baseline clinical data, laboratory, and radiological results were used to construct a prediction-model that can predict if the COVID-19 patients will develop a severe condition that will necessitate their ICU-admission. An ensemble feature selection tool was used to identify the relative importance of each variable. The performance SERO of the selected features compared to all features using logistic regression and area under curve test.Results: Patients with ICU admission showed a distinct clinical, demographic as well as laboratory features when compared to pattients that did not need ICU admission. This includes elder age group TRANS, male TRANS gender TRANS and presence of comorbidities like diabetis and history of hypertension HP hypertension MESHD.Out of the different demographic, clinical and laboratory charasteristics of these patients, Age TRANS at diagnosis, Lymphocyte count, C-reactive protein (CRP), lactate dehydrogenase (LDH), Albumin, Urea, and Procalcitonin levels were found to be able to predict which patients may need ICU admission. Conclusion: Higher CRP, LDH, Age TRANS at diagnosis, Urea, Procalcitonin, and lower Albumin, Lymphocyte count are significant determinant in ICU admission for COVID-19 patients. 

    Early prediction for severe COVID-19 with  hypertension MESHD and intervention

    Authors: Denggao Peng; Yanzhang Gao; Zhenyu Zhou; Huan Wang; Anjue Tang

    doi:10.21203/rs.3.rs-31888/v1 Date: 2020-05-28 Source: ResearchSquare

    Background To identify the early predictors of severe coronavirus disease MESHD 2019 (COVID-19) with hypertension HP hypertension MESHD,explore antihypertensive drugs with potential therapeutic effects, and provide a basis for clinical prediction and treatment decisions.Method: A retrospective study was performed on all included cases.Results A total of 68 COVID-19 patients with hypertension HP hypertension MESHD were included,27 (39.7%) was severe and 41 (60.3%) was non-severe. Between the non-severe group (n = 41) and the severe group (n = 27),number of elevated B-type natriuretic peptide (BNP) and abnormal renal function MESHD,and albumin,lactate dehydrogenase,ultrasensitive troponin I,PH Value,arterial carbon dioxide partial pressure,sodium,osmotic pressure (OP), blood SERO sugar (BS) and oxygenation index (OI) are significantly different.While age TRANS, male TRANS gender TRANS,comorbidities with diabetes MESHD or atherosclerotic cardiovascular disease MESHD,smoking history,number of abnormal liver function MESHD,heart rate,respiratory rate, blood SERO pressure,white blood SERO cell count,hematocrit,potassium and lactic acid are statistically insignificant.Four independent predictors of BNP (P = .026),OP (P = .004),BS (P = .017) and OI (P = .001) are obtained through multivariate binary logistic regression model.The area under curve (AUC) of receiver operating characteristic (ROC) of model is 0.904 ([95%CI] [0.832–0.976];P = .000),with excellent performance SERO.Compared with blank control group (n = 27) and other antihypertensive drugs group (n = 20),OP ([287.3 ± 5.7] vs [283.5 ± 6.1];P = .045) ([287.3 ± 5.7] vs [281.9 ± 5.4];P = .007) in renin-angiotensin-aldosterone system (RAS) inhibitors group (n = 21) have increased significantly.Compared with controlled blood SERO pressure group (n = 30),OP ([285.7 ± 6.2] vs [282.2 ± 5.2];P = .012) of uncontrolled group (n = 38) increased significantly.Conclusion Decreased OP MESHD and OI, increased BNP and BS are early predictors for severe COVID-19 patients with hypertension HP hypertension MESHD.For poorly controlled blood SERO pressure,targeting RAS MESHD and OP,early use of RAS inhibitors or combination with loop diuretics may be an effective treatment.

    Identification of risk factors for the severity of coronavirus disease MESHD 2019: a retrospective study of 163 hospitalized patients

    Authors: Ye Tu; Ping Yang; Jingjing Wang; Xuebi Tian; Kai Wang; Chaolong Wang; Ailin Luo; Feng Gao

    doi:10.21203/rs.3.rs-29825/v1 Date: 2020-05-19 Source: ResearchSquare

    Background: To compare clinical features between moderate and severe cases with COVID-19, and screen factors associated with disease severity.Methods: Demographic and clinical data were compared between moderate and severe cases. Logistic regression was performed for prognostic factors.Results: 163 patients (median age TRANS 65.0 (56.8-71.0) years, 78 (47.9%) females TRANS) were enrolled, including 87 (53.4%) severe and 76 (46.6%) moderate cases. 79 (90.8%) severe and 59 (77.6%) moderate cases had comorbidities, with hypertension HP hypertension MESHD and diabetes MESHD commonly presented. The most common symptoms were fever HP fever MESHD. Severe cases had higher lactate dehydrogenase (LDH), inflammatory cytokines and lymphopenia HP lymphopenia MESHD, eosinopenia on admission, and lower eosinophil and higher neutrophil counts from admission to day 13 and 19. Multivariable regression showed that neutrophilia HP, eosinopenia, high LDH and D-dimer were associated with severe COVID-19. In receiver operating characteristic curve analysis, LDH, eosinophil and neutrophil + eosinophil + LDH + D-dimer combination, with area under curve of 0.86, 0.76 and 0.93, predicted severe illness with high sensitivity SERO (82.8%, 83.3%, 88.0%) and specificity (68.4%, 84.2%, 81.3%).Conclusions: Eosinopenia, higher LDH and neutrophil + eosinophil + LDH + D-dimer combination on admission were powerful indicators of severe COVID-19. Dynamic changes of neutrophils and eosinophils may be used to evaluate disease progression.

    Development and External Validation of a Prognostic Tool for COVID-19 Critical Disease MESHD

    Authors: Daniel S Chow; Justin Glabis-Bloom; Jennifer Soun; Brent Weinberg; Theresa Berens-Loveless; Xiaohui Xie; Simukayi Mutasa; Edwin Monuki; Jung In Park; Daniela Bota; Jie Wu; Leslie Thompson; Bernadette Boden-Albala; Saahir Khan; Alpesh Amin; Peter Chang

    doi:10.1101/2020.05.06.20093435 Date: 2020-05-11 Source: medRxiv

    Background: The rapid spread of coronavirus disease MESHD 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care. Methods: This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease MESHD was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance SERO was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia. Results: Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age TRANS, 48 years [range, 21-88 years]; 64% [36/55] male TRANS). The most common comorbidities included obesity HP obesity MESHD (37%, 31/87), hypertension HP hypertension MESHD (37%, 32/87), and diabetes MESHD (24%, 24/87). Critical disease MESHD was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease MESHD: number of comorbidities, body mass index, respiratory rate, white blood SERO cell count, % lymphocytes, serum SERO creatinine, lactate dehydrogenase, high sensitivity SERO troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age TRANS, 60 years [range, 27-88 years]; 55% [22/40] male TRANS), critical disease MESHD was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87-1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease MESHD. Conclusions and Relevance: We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.

    SARS-COV-2 comorbidity network and outcome in hospitalized patients in Crema, Italy

    Authors: Gianpaolo Benelli; Elisabetta Buscarini; Ciro Canetta; Giuseppe La Piana; Guido Merli; Alessandro Scartabellati; Giovanni Vigano; Roberto Sfogliarini; Giovanni Melilli; Roberto Assandri; Daniele Cazzato; Davide Sebastiano Rossi; Susanna Usai; Guido Caldarelli; Tommaso Gili; Irene Tramacere; Germano Pellegata; Giuseppe Lauria

    doi:10.1101/2020.04.14.20053090 Date: 2020-04-20 Source: medRxiv

    No systematic data on hospitalized SARS-COV-2 patients from Western countries are available. We report onset, course, correlations with comorbidities, and diagnostic accuracy of nasopharyngeal swab in 539 individuals suspected to carry SARS-COV-2 admitted to the hospital of Crema, Italy. All individuals underwent clinical and laboratory exams, SARS-COV-2 reverse transcriptase-polymerase chain reaction on nasopharyngeal swab, and chest X-ray and/or computed tomography (CT). Data on onset, course, comorbidities, number of drugs including angiotensin converting enzyme (ACE) inhibitors and angiotensin-II-receptor antagonists (sartans), follow-up swab, pharmacological treatments, non-invasive respiratory support, ICU admission, and deaths MESHD were recorded. Among 411 SARS-COV-2 patients (66.6% males TRANS) median age TRANS was 70.5 years (range 1-99). Chest CT was performed in 317 (77.2%) and showed interstitial pneumonia MESHD pneumonia HP in 304 (96%). Fatality rate was 17.5% (74% males TRANS), with 6.6% in 60-69 years old, 21.1% in 70-79 years old, 38.8% in 80-89 years old, and 83.3% above 90 years. No death occurred below 60 years. Non-invasive respiratory support rate was 27.2% and ICU admission 6.8%. Older age TRANS, cough HP and dyspnea HP dyspnea MESHD at onset, hypertension HP hypertension MESHD, cardiovascular diseases MESHD, diabetes MESHD, renal insufficiency HP renal insufficiency MESHD, >7 drugs intake and positive X-ray, low lymphocyte count, high C-reactive protein, aspartate aminotransferase and lactate dehydrogenase values, and low PO2 partial pressure with high lactate at arterial blood SERO gas analysis at admission were significantly associated with death MESHD. Use of ACE inhibitors or sartans was not associated with outcomes. Comorbidity network analysis revealed homogenous distribution of deceased and 60-80 aged TRANS SARS-COV-2 patients across diseases. Among 128 swab negative patients at admission (63.3% males TRANS) median age TRANS was 67.7 years (range 1-98). Chest CT was performed in 87 (68%) and showed interstitial pneumonia MESHD pneumonia HP in 76 (87.3%). Follow-up swab turned positive in 13 of 32 patients. Using chest CT at admission as gold standard on the entire study population of 539 patients, nasopharyngeal swab had 80% sensitivity SERO. SARS-CoV-2 caused high mortality among patients older than 60 years and correlated with pre-existing multiorgan impairment. ACE inhibitors and sartans did not influence patients' outcome.

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.