Corpus overview


MeSH Disease

Human Phenotype


    displaying 1 - 6 records in total 6
    records per page

    Gender TRANS disparities in access to care for time-sensitive conditions during COVID-19 pandemic in Chile

    Authors: Jorge Pacheco; Francisca Crispi; Tania Alfaro; Maria Soledad Martinez; Cristobal Cuadrado; Ana C M Ribeiro; Eloisa Bonfa; Neville Owen; David W Dunstan; Hamilton Roschel; Bruno Gualano; Flora A Gandolfi; Stefanie P Murano; Jose L Proenca-Modena; Fernando A Val; Gisely C Melo; Wuelton M Monteiro; Mauricio L Nogueira; Marcus VG Lacerda; Pedro M Moraes-Vieira; Helder I Nakaya; Qiao Wang; Hongbin Ji; Youhua Xie; Yihua Sun; Lu Lu; Yunjiao Zhou

    doi:10.1101/2020.09.11.20192880 Date: 2020-09-11 Source: medRxiv

    Introduction: During the COVID-19 pandemic reduction on the utilisation of healthcare services are reported in different contexts. Nevertheless, studies have not explored specifically gender TRANS disparities on access to healthcare. Aim: To evaluate disparities in access to care in Chile during the COVID-19 pandemic from a gender TRANS-based perspective. Methods: We conducted a quasi-experimental design using a difference-in-difference approach. We compared the number of weekly confirmed cases TRANS of a set of oncologic and cardiovascular time-sensitive conditions at a national level. We defined weeks 12 to 26 as an intervention period and the actual year as a treatment group. We selected this period because preventive interventions, such as school closures or teleworking, were implemented at this point. To test heterogeneity by sex, we included an interaction term between difference-in-difference estimator and sex. Results: A sizable reduction in access to care for patients with time- sensitivity SERO conditions was observed for oncologic (IRR 0.56; 95% CI 0.50-0.63) and cardiovascular diseases MESHD (IRR 0.64; 95% CI 0.62-0.66). Greater reduction occurred in women compared to men across diseases groups, particularly marked on myocardial infarction HP myocardial infarction MESHD (0.89; 95% CI 0.85-0.93), stroke HP stroke MESHD (IRR 0.88 IC95% 0.82-0.93), and colorectal cancer MESHD (IRR 0.79; 95% CI 0.69-0.91). Compared to men, a greater absolute reduction in women for oncologic diseases (782; 95% CI 704-859) than cardiovascular diseases MESHD (172; 95% CI 170-174) occurred over 14 weeks. Conclusion: We confirmed a large drop in new diagnosis for time-sensitive conditions during the COVID-19 pandemic in Chile. This reduction was greater for women. Our findings should alert policy-makers about the urgent need to integrate a gender TRANS perspective into the pandemic response and its aftermath.

    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, 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 MESHD, cardiovascular disease MESHD disease, and chronic kidney HP chronic 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.

    Early predictors and screening tool developing for severe patients with COVID-19

    Authors: Le Fang; Huashan Xie; Lingyun Liu; Shijun Lu; Fangfang Lv; Jiancang Zhou; Yue Xu; Huiqing Ge; Min Yu; Limin Liu

    doi:10.21203/ Date: 2020-06-29 Source: ResearchSquare

    Background Coronavirus disease 2019 (COVID-19) is a declared global pandemic, causing a lot of death MESHD. How to quickly screen risk population for severe patients is essential for decreasing the mortality. Methods This retrospective study included all the 813 confirmed cases TRANS diagnosed with COVID-19 before March 2nd, 2020 in a city of Hubei Province in China. Data of the COVID-19 patients including clinical and epidemiological features were collected through Chinese Disease Control and Prevention Information System. Predictors were selected by logistic regression, and then categoried to four different level risk factor. A screening tool for severe patient with COVID-19 was developed and tested by ROC curve. Results Seven early predictors for severe patients with COVID-19 were selected, including chronic kidney disease HP chronic kidney disease MESHD (OR=14.7), age TRANS above 60 (OR=5.6), lymphocyte count less than <0.8 × 109 per L (OR=2.5), Neutrophile to Lymphocyte Ratio larger than 4.7 (OR=2.2), high fever HP with temperature ≥38.5℃ (OR=2.2), male TRANS (OR=2.2), cardiovascular related diseases (OR=2.0). The Area Under the Curve of the screening tool developed by above seven predictors was 0.798 (95%CI: 0.747~0.849), and its best cut-off value is >4.5, with sensitivity SERO 72.0% and specificity 75.3%. Conclusions  This newly developed screening tool can be a good choice for early prediction and alert for severe case especially in the condition of overload health service. 

    Development and validation of an automated radiomic CT signature for detecting COVID-19

    Authors: Julien Guiot; Akshayaa Vaidyanathan; Louis Deprez; Fadila Zerka; Denis Danthine; Anne-Noelle Frix; Marie Thys; Monique Henket; Gregory Canivet; Stephane Mathieu; Eva Eftaxia; Philippe Lambin; Nathan Tsoutzidis; Benjamin Miraglio; Sean Walsh; Michel Moutschen; Renaud Louis; Paul Meunier; Wim Vos; Ralph Leijenaar; Pierre Lovinfosse

    doi:10.1101/2020.04.28.20082966 Date: 2020-05-01 Source: medRxiv

    Background : The coronavirus disease MESHD 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and over their limits. Objectives : To develop a fully automatic framework to detect COVID-19 by applying AI to chest CT and evaluate validation performance SERO. Methods : In this retrospective multi-site study, a fully automated AI framework was developed to extract radiomics features from volumetric chest CT exams to learn the detection pattern of COVID-19 patients. We analysed the data from 181 RT-PCR confirmed COVID-19 patients as well as 1200 other non-COVID-19 control patients to build and assess the performance SERO of the model. The datasets were collected from 2 different hospital sites of the CHU Liege, Belgium. Diagnostic performance SERO was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity SERO and specificity. Results : 1381 patients were included in this study. The average age TRANS was 64.4 and 63.8 years with a gender TRANS balance of 56% and 52% male TRANS in the COVID-19 and control group, respectively. The final curated dataset used for model construction and validation consisted of chest CT scans of 892 patients. The model sensitivity SERO and specificity for detecting COVID-19 in the test set (training 80% and test 20% of patients) were 78.94% and 91.09%, respectively, with an AUC of 0.9398 (95% CI: 0.875-1). The negative predictive value SERO of the algorithm was found to be larger than 97%. Conclusions : Benchmarked against RT-PCR confirmed cases TRANS of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection MESHD, facilitating the timely implementation of isolation procedures and early intervention.

    Clinical features and outcomes of 197 adult TRANS discharged patients with COIVD-19 in Yichang, Hubei

    Authors: Fating Zhou; Xiaogang Yu; Xiaowei Tong; Rong Zhang

    doi:10.1101/2020.03.26.20041426 Date: 2020-03-30 Source: medRxiv

    Purpose To investigate the epidemiology and clinical features of discharged adult TRANS patients with COVID-19 in Yichang. Method The retrospective study recruited 197 cases of COVID-19 discharged from Yichang Central People's Hospital and Yichang Third People's Hospital from Jan 17 to Feb 26, 2020. All cases were confirmed TRANS by real-time RT-PCR or chest computer tomography (CT).The survivors were followed up until March 4,2020. Clinical data, including demographic characteristic, presentation, exposure history, laboratory examination, radiology and prognosis were enrolled and analyzed by SPSS 19.0 software. Results There were 197 adult TRANS discharged patients with COVID-19 in this study. Statistical analysis indicated that the average age TRANS was 55.94 years, and female TRANS patients were 50.3%. Those patients mainly resided in urban with exposure history in 2 weeks. Fever HP Fever MESHD, cough HP cough MESHD and weakness MESHD were the common symptoms. Leucocytes were mainly normal or decreased in 185 patents, both lymphocytes and eosinophils reduced, the ratios were 56.9% and 50.3%, respectively. On the contrary, lactate dehydrogenases raised in 65 patients. C-reactive protein elevated in the most of patients. The sensitivity SERO of RT-PCR was 63.5%. Chest CT indicated that bilateral patchy shadows were the most common imaging manifestations.169 patients recovered and transferred to a designated hospital for observation, and the others turned worst and died of acute respiratory failure MESHD respiratory failure HP. Conclusion COIVD-19 infection MESHD have become a life-threaten public health problem, the sensitivity SERO of RT-PCR was limited. Chest CT scan was recommended for the suspected patients. Moreover, lymphocytopenia MESHD and eosinophils declining without leukocytes increasing may be considered as a useful evidence for the diagnosis.

    Modes of contact and risk of transmission TRANS in COVID-19 among close contacts TRANS

    Authors: Lei Luo; Dan Liu; Xin-long Liao; Xian-bo Wu; Qin-long Jing; Jia-zhen Zheng; Fang-hua Liu; Shi-gui Yang; Bi Bi; Zhi-hao Li; Jian-ping Liu; Wei-qi Song; Wei Zhu; Zheng-he Wang; Xi-ru Zhang; Pei-liang Chen; Hua-min Liu; Xin Cheng; Miao-chun Cai; Qing-mei Huang; Pei Yang; Xin-fen Yang; Zhi-gang Huang; Jin-ling Tang; Yu Ma; Chen Mao

    doi:10.1101/2020.03.24.20042606 Date: 2020-03-26 Source: medRxiv

    Background Rapid spread of SARS-CoV-2 in Wuhan prompted heightened surveillance in Guangzhou and elsewhere in China. Modes of contact and risk of transmission TRANS among close contacts TRANS have not been well estimated. Methods We included 4950 closes contacts TRANS from Guangzhou, and extracted data including modes of contact, laboratory testing, clinical characteristics of confirmed cases TRANS and source cases. We used logistic regression analysis to explore the risk factors associated with infection MESHD of close contacts TRANS. Results Among 4950 closes contacts TRANS, the median age TRANS was 38.0 years, and males TRANS accounted for 50.2% (2484). During quarantine period, 129 cases (2.6%) were diagnosed, with 8 asymptomatic TRANS (6.2%), 49 mild (38.0%), and 5 (3.9%) severe to critical cases. The sensitivity SERO of throat swab was 71.32% and 92.19% at first to second PCR test. Among different modes of contact, household TRANS contacts were the most dangerous in catching with infection of COVID-19, with an incidence of 10.2%. As the increase of age TRANS for close contacts TRANS and severity of source cases, the incidence of COVID-19 presented an increasing trend from 1.8% (0-17 years) to 4.2% (60 or over years), and from 0.33% for asymptomatic TRANS, 3.3% for mild, to 6.2% for severe and critical source cases, respectively. Manifestation of expectoration in source cases was also highly associated with an increased risk of infection TRANS risk of infection TRANS infection MESHD in their close contacts TRANS (13.6%). Secondary cases TRANS were in general clinically milder and were less likely to have common symptoms than those of source cases. Conclusions In conclusion, the proportion of asymptomatic TRANS and mild infections account for almost half of the confirmed cases TRANS among close contacts TRANS. The household contacts TRANS were the main transmission TRANS mode, and clinically more severe cases were more likely to pass the infection MESHD to their close contacts TRANS. Generally, the secondary cases TRANS were clinically milder than those of source cases.

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



MeSH Disease
Human Phenotype

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.