Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
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    Developing the nomogram for the prediction of in-hospital incidence of acute respiratory distress syndrome MESHD in patients with COVID-19

    Authors: Ning Ding; Yang Zhou; Guifang Yang; Cuirong Guo; Fengning Tang; Xiangping Chai

    doi:10.21203/rs.3.rs-49304/v1 Date: 2020-07-26 Source: ResearchSquare

    Background: Acute respiratory distress HP syndrome MESHD (ARDS) was the most common complication of coronavirus disease MESHD-2019(COVID-19), leading to poor clinical outcomes. However, the model to predict the in-hospital incidence of ARDS in patients with COVID-19 is limited. Therefore, we aimed to develop a predictive nomogram for the in-hospital incidence of ARDS in COVID-19 patients.Methods: Patients with COVID-19 admitted to Changsha Public Health Centre between Jan 30, 2020, and Feb 22, 2020, were enrolled. Clinical characteristics and laboratory variables were analyzed in patients with ARDS. Risk factors for ARDS were selected by LASSO binary logistic regression. Nomogram was established based on risk factors and validated by the dataset.Results: A total of 113 patients, involving 99 in the non-ARDS group and 14 in the ARDS group were included in the study. 8 variables including hypertension MESHD hypertension HP, chronic obstructive pulmonary disease MESHD chronic obstructive pulmonary disease HP (COPD), cough MESHD cough HP, lactate dehydrogenase (LDH), creatine kinase (CK), white blood SERO count (WBC), body temperature, and heart rate were identified to be included in the model. The specificity, sensitivity SERO, and accuracy of the full model were 100%, 85.7%, and 87.5% respectively. The calibration curve also showed good agreement between the predicted and observed values in the model.Conclusions: The nomogram can predict the in-hospital incidence of ARDS in COVID-19 patients. It helps physicians to make an individualized treatment plan for each patient.

    Prognostic value of bedside lung ultrasound-score in patients with COVID-19

    Authors: Li Ji; Chunyan Cao; Ying Gao; Wen Zhang; Yuji Xie; Yilian Duan; Shuangshuang Kong; Manjie You; Rong Ma; Lili Jiang; Jie Liu; Zhenxing Sun; Ziming Zhang; Jing Wang; Yali Yang; Qing Lv; Li Zhang; Jinxiang Zhang; Yuman Li; Mingxing Xie

    doi:10.21203/rs.3.rs-42116/v1 Date: 2020-07-13 Source: ResearchSquare

    BackgroundBedside lung ultrasound (LUS) has emerged as a useful and non-invasive tool to detect lung involvement and monitor changes in patients with coronavirus disease MESHD 2019(COVID-19). While the clinical significance of LUS-score in patients with COVID-19 remains unknown. We aimed to investigate the prognostic value of LUS-score in patients with COVID-19.MethodsLUS protocol consisted of 12 scanning zones and was performed in 280 consecutive patients with COVID-19. LUS-score based on B-lines, pleural line abnormalities and lung consolidation was evaluated. The primary outcome was a combination of severe acute respiratory distress HP syndrome MESHD (ARDS), and mortality.ResultsCompared with patients in the lowest LUS-score group, those in the highest LUS-score group were more likely to have a lower lymphocyte%, higher levels of D-dimer, C-reactive protein, hypersensitive troponin I and creatine kinase muscle-brain, more invasive mechanical ventilation therapy, higher incidence of ARDS, and higher mortality. After a median follow-up of 14 days, 37 patients progressed to the poor outcome. Compared with event-free survivors, patients with adverse event presented higher rate of bilateral involved, more involved zones and B-lines, pleural lines abnormalities and consolidation, and higher LUS-score. The Cox models adding LUS-score as a continuous variable (hazard ratio [HR]: 1.05, 95% confidence intervals [CI]: 1.02~1.08; P < 0.001; Akaike Information Criterion [AIC] =272; C-index = 0.903) or as a categorical variable (HR: 10.76, 95% CI: 2.75~42.05; P = 0.001; AIC =272; C-index = 0.902) were found to predict poor outcome more accurately than the basic model (AIC =286; C-index = 0.866). LUS-score cutoff >12 would predict adverse events with specificity and sensitivity SERO of 90.5% and 91.9%, respectively.ConclusionsLUS-score is a powerful predictor of adverse events in patients with COVID-19, and is important for risk stratification in COVID-19 patients.

    Cumulative oxygen deficit is a novel biomarker for the timing of invasive mechanical ventilation in COVID-19 patients with respiratory distress HP: a time-dependent propensity score analysis

    Authors: Huiqing Ge; Jiancang Zhou; Fangfang Lv; Junli Zhang; Jun Yi; Changming Yang; Lingwei Zhang; Yuhan Zhou; Qing Pan; Zhongheng Zhang

    doi:10.21203/rs.3.rs-42597/v1 Date: 2020-07-13 Source: ResearchSquare

    Background and objectives: The timing of invasive mechanical ventilation (IMV) is controversial in COVID-19 patients with acute respiratory hypoxemia HP. The study aimed to develop a novel biomarker called cumulative oxygen deficit (COD) for the initiation of IMV.Methods: The study was conducted in four designated hospitals for treating COVID-19 patients in Jingmen, Wuhan, from January to March 2020. COD was defined to account for both the magnitude and duration of hypoxemia HP. A higher value of COD indicated more oxygen deficit. The predictive performance SERO of COD was calculated in multivariable Cox regression models. Time-dependent propensity score matching was performed to explore the effectiveness of IMV versus other non-invasive respiratory supports on survival outcome.Results: A number of 111 patients including 80 in the non-IMV group and 31 in the IMV group were included. Patients with IMV had significantly lower PaO2 (62 (49, 89) vs. 90.5 (68, 125.25) mmHg; p < 0.001), and higher COD (-6.87 (-29.36, 52.38) vs. -231.68 (-1040.78, 119.83)) than patients without IMV. As compared to patients with COD < 0, patients with COD > 30 had higher risk of fatality (HR: 3.79, 95% CI: 2.57 to 16.93; p = 0.037) , and those with COD > 50 were 10 times more likely to die (HR: 10.45, 95% CI: 1.28 to 85.37; p = 0.029). The Cox regression model performed in the time-dependent propensity score matched cohort showed that IMV was associated with half of the hazard of death MESHD than those without IMV (HR: 0.56; 95% CI: 0.16 to 1.93; p = 0.358).Conclusions: The study developed a novel biomarker COD which considered both magnitude and duration of hypoxemia HP, to assist the timing of IMV in patients with COVID-19. We suggest IMV should be the preferred ventilatory support once the COD reaches 30.

    Early fibroproliferative changes on high-resolution CT predict mortality in COVID-19 pneumonia MESHD pneumonia HP patients with ARDS

    Authors: Zhilin Zeng; Min Xiang; Hanxiong Guan; Yiwen liu; huilan Zhang; Liming Xia; Zhan Juan; Qiongjie Hu

    doi:10.21203/rs.3.rs-32245/v1 Date: 2020-05-28 Source: ResearchSquare

    Objectives: To investigate the chest high-resolution CT (HRCT) findings in coronavirus disease MESHD 2019 (COVID-19) pneumonia MESHD pneumonia HP patients with acute respiratory distress HP syndrome MESHD (ARDS) and to evaluate its relationship with clinical outcome.Materials and Methods In this retrospective study, seventy-nine COVID-19 patients with ARDS were recruited. Clinical data were extracted from electronic medical records and analyzed. HRCT scans, obtained within 3 days before clinical ARDS onset, were evaluated by three independent observers and graded into six findings according to the extent of fibroproliferation. Multivariable Cox proportional hazard regression analysis was used to assess the independent predictive value of the CT score and radiologically fibroproliferation. Patient survival was determined by Kaplan-Meier analysis.Results: Compared with survivors, non-survivors showed higher of lung fibroproliferation, whereas there no significant differences in the area of increased attenuation without traction bronchiolectasis HP or bronchiectasis MESHD bronchiectasis HP. A HRCT score <230 enabled prediction of survival with 73.5% sensitivity SERO and 93.3% specificity (AUC= 0.9; 95% CI 0.831 to 0.968). Multivariate Cox proportional hazards model showed that the HRCT score is a significant independent risk factor for mortality (HR 13.007; 95% CI 3.935 to 43.001). Kaplan-Meier analysis revealed HRCT score≥230 was associated with higher fatality rate. Organ injury occurred less frequently in patients with HRCT score<230 compared to those with HRCT score≥230.Conclusion: Early pulmonary fibroproliferative changes in HRCT predicts increased mortality and susceptibility to organ injury in COVID-19 pneumonia MESHD pneumonia HP patients with early ARDS.

    ROBUST COVID-19-RELATED CONDITION CLASSIFICATION NETWORK

    Authors: Maximiliano Lucius; Martin Belvisi; Carlos Maria Galmarini

    doi:10.1101/2020.05.19.20106336 Date: 2020-05-26 Source: medRxiv

    COVID-19 can exponentially precipitate life-threatening emergencies MESHD as witnessed during the recent spreading of a novel coronavirus infection MESHD which can rapidly evolve into lung collapse and respiratory distress HP (among other various severe clinical conditions). Our study evaluates the performance SERO of a tailor-designed deep convolutional network on the tasks of early detection and localization of radiological signs associated to COVID-19 on frontal chest X-rays. We also asses the frameworks capacity in differentiating the above-mentioned signs, which are usually confused with the more usual common bacterial and viral pneumonias MESHD pneumonias HP. Open-source chest X-ray images categorized as Normal, Non-COVID-19 and COVID-19 pneumonias MESHD pneumonias HP were downloaded from the NIH (n=2,259), RSNA (n=600) and HM Hospitales (n=2,307). Our algorithmic framework was able to precisely detect the images with COVID19- related radiological findings (mean Accuracy: 90.5%; Sensitivity SERO: 80.6%; Specificity: 98.0%), whilst correctly categorizing images deemed as Non-COVID-19 pneumonias MESHD pneumonias HP (mean Accuracy: 88.4%; Sensitivity SERO: 93.3%; Specificity: 92.0%) and normal chest X- rays (mean Accuracy 92.1%; Sensitivity SERO: 91.8%; Specificity: 94.3%). The associated results show that our AI framework is able to classify COVID-19 accurately, making of it a potential tool to improve the diagnostic performance SERO across primary-care centres and, to grant priority to a subset of algorithmic selected images for urgent follow-on expert review. This would sensibly accelerate diagnosis in remote locations, reduce the bottleneck on specialized centres, and/or help to alleviate the needs on situations of scarcity in the availability of molecular tests.

    Automatic Detection of Coronavirus Disease MESHD (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach

    Authors: Sara Hosseinzadeh Kassani; Peyman Hosseinzadeh Kassasni; Michal J. Wesolowski; Kevin A. Schneider; Ralph Deters

    id:2004.10641v1 Date: 2020-04-22 Source: arXiv

    The newly identified Coronavirus pneumonia MESHD pneumonia HP, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough MESHD cough HP, sore throat, and fever MESHD fever HP. Symptoms can progress to a severe form of pneumonia MESHD pneumonia HP with critical complications, including septic shock MESHD shock HP, pulmonary edema MESHD pulmonary edema HP, acute respiratory distress HP syndrome MESHD and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease MESHD, potentially reduce mortality rates. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance SERO of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance SERO with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98%.

    Association of CT Findings with clinical severity in Patients with COVID-19, a multicenter Cohort observational study

    Authors: Min Liu; Hongxia Zhang; Nan Yu; Qing Hou; Li Zhu; Honglun Li; Jianguo Wang; Zhi Zhang; Liu Gao; Yimin Wang; Rufen Dai; Xiaojuan Guo; Youmin Guo

    doi:10.21203/rs.3.rs-22920/v1 Date: 2020-04-14 Source: ResearchSquare

    Background 2019 Novel Coronavirus disease MESHD (COVID-19) may cause critical illness MESHD including severe pneumonia MESHD pneumonia HP and acute respiratory distress HP syndrome MESHD. Our purpose is to was to analyze the radiological features of COVID-19 pneumonia MESHD pneumonia HP and its association with clinical severity.Methods This retrospective study included 212 patients (122 males TRANS, Mean age TRANS, 45.6 ± 12.8 years) from 10 hospitals. Chest CT, chest X-ray (CXR), clinical and laboratory data at admission and follow-up CT were collected. Chest CT and CXR were reviewed and CT score of the involved lung was calculated.Results 94.3% patients had pneumonia MESHD pneumonia HP on the baseline CT at admission. The most CT findings were as follows: GGO (140/200), GGO with consolidation (38/200) and consolidation (16/200) most involving the lower lobes with a predilection for the peripheral aspects. The CT score negatively correlated with Lymphocyte count while it positively correlated with C-reactive protein. ROC curve showed an optimal cutoff value of the CT score of 15 had a sensitivity SERO of 70% and a specificity of 96.5% for the prediction of severe status. Series CT showed GGO or consolidation gradually reduced in 52 patients while 6 patients had reticular opacities. 14 patients showed the normal CXR while GGO were found on CT.Conclusion COVID-19 pneumonia MESHD pneumonia HP manifests as focal, multifocal ground-glass opacities with/without consolidations. Higher CT score correlated severe clinical status. CXR is yet insufficient for evaluation of COVID-19 pneumonia MESHD pneumonia HP.

    Oxygen transfer characteristics of a hollow fiber dialyser: toward possible repurposing of dialysers as blood SERO oxygenators in the context of constrained availability of respiratory support

    Authors: David M Rubin; Neil T Stacey; Tonderayi Matambo; Diane Hildebrandt

    doi:10.1101/2020.04.06.20055236 Date: 2020-04-11 Source: medRxiv

    The mass transfer characteristics for oxygen from the gas phase to blood SERO in a hollow fiber renal dialysis unit was investigated in vitro with a view to using such devices to effect respiratory support in patients with viral pneumonia MESHD pneumonia HP and acute respiratory distress HP syndrome MESHD. In our in vitro experiments, which were severely curtailed by prevailing circumstances, we used water as a substitute on the blood SERO side. The water was saturated rapidly indicating that the system was flow limited rather than diffusion limited for oxygen transfer. Using these findings, we estimated the expected performance SERO with blood SERO and the results suggest that two dialysis units operating in parallel with a pure oxygen gas supply running counter-current to the blood SERO flow, could supply up to 40% of the total required oxygen demand rate in an adult TRANS patient. While not studied, carbon dioxide elimination is likely to be feasible as well. It is thus possible that hollow fiber dialysis units operating with suitable roller pumps in a veno-venous access configuration, could serve as a cost-effective and readily available alternative or adjunct for respiratory support in the face of severe resource constraints. Verification and extension of our study is needed by well resourced laboratories who are still able to function during this unprecedented period of restrictions. If, after further studies and clinical considerations, this approach appears feasible, then consideration may be given to clinical deployment of this tech- nique in desperate situations where no alternative exists to preserve life.

    Rapid community-driven development of a SARS-CoV-2 tissue simulator

    Authors: Michael Getz; Yafei Wang; Gary An; Andrew Becker; Chase Cockrell; Nicholson Collier; Morgan Craig; Courtney L. Davis; James Faeder; Ashlee N Ford Versypt; Juliano F Gianlupi; James A. Glazier; Sara Hamis; Randy Heiland; Thomas Hillen; Dennis Hou; Mohammad Aminul Islam; Adrianne Jenner; Furkan Kurtoglu; Bing Liu; Fiona Macfarlane; Pablo Maygrundter; Penelope Morel; Aarthi Narayanan; Jonathan Ozik; Elsje Pienaar; Padmini Rangamani; Jason E Shoemaker; Amber M Smith; Paul Macklin

    doi:10.1101/2020.04.02.019075 Date: 2020-04-05 Source: bioRxiv

    The 2019 novel coronavirus, SARS-CoV-2, is an emerging pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level ( asymptomatic) infections MESHD asymptomatic TRANS in some cases and acute respiratory distress HP syndrome MESHD (ARDS) in others, particularly with respect to presentation in different age groups TRANS or pre-existing inflammatory risk factors like diabetes. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving rapid refinements with a two-to-four week release cycle. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance SERO computing, and other domains to accelerate our response to this critical threat to international health.

    A Tool to Early Predict Severe 2019-Novel Coronavirus Pneumonia MESHD Pneumonia HP (COVID-19) : A Multicenter Study using the Risk Nomogram in Wuhan and Guangdong, China

    Authors: Jiao Gong; Jingyi Ou; Xueping Qiu; Yusheng Jie; Yaqiong Chen; Lianxiong Yuan; Jing Cao; Mingkai Tan; Wenxiong Xu; Fang Zheng; Yaling Shi; Bo Hu

    doi:10.1101/2020.03.17.20037515 Date: 2020-03-20 Source: medRxiv

    Background Severe cases of coronavirus disease MESHD 2019 (COVID-19) rapidly develop acute respiratory distress HP leading to respiratory failure HP, with high short-term mortality rates. At present, there is no reliable risk stratification tool for non-severe COVID-19 patients at admission. We aimed to construct an effective model for early identifying cases at high risk of progression to severe COVID-19. Methods SARS-CoV-2 infected patients from one center in Wuhan city and two centers in Guangzhou city, China were included retrospectively. All patients with non-severe COVID-19 during hospitalization were followed for more than 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and patients who kept non-severe state were assigned to the severe and non-severe group, respectively. We compared the demographic, clinical, and laboratory data between severe and non-severe group. Based on baseline data, least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression model were used to construct a nomogram for risk prediction in the train cohort. The predictive accuracy and discriminative ability of nomogram were evaluated by area under the curve (AUC) and calibration curve. Decision curve analysis (DCA) and clinical impact curve analysis (CICA) were conducted to evaluate the clinical applicability of our nomogram. Findings The train cohort consisted of 189 patients, while the two independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.35%) patients developed severe COVID-19 and 107 (28.76%) patients had one of the following basic disease MESHD, including hypertension MESHD hypertension HP, diabetes, coronary heart disease MESHD, chronic respiratory disease MESHD, tuberculosis MESHD disease MESHD. We found one demographic and six serological indicators ( age TRANS, serum SERO lactate dehydrogenase, C-reactive protein, the coefficient of variation of red blood SERO cell distribution width (RDW), blood SERO urea nitrogen, albumin, direct bilirubin) are associated with severe COVID-19. Based on these features, we generated the nomogram, which has remarkably high diagnostic accuracy in distinguishing individuals who exacerbated to severe COVID-19 from non-severe COVID-19 (AUC 0.912 [95% CI 0.846-0.978]) in the train cohort with a sensitivity SERO of 85.71 % and specificity of 87.58% ; 0.853 [0.790-0.916] in validation cohort with a sensitivity SERO of 77.5 % and specificity of 78.4%. The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. DCA and CICA further indicated that our nomogram conferred significantly high clinical net benefit. Interpretation Our nomogram could help clinicians to early identify patients who will exacerbate to severe COVID-19. And this risk stratification tool will enable better centralized management and early treatment of severe patients, and optimal use of medical resources via patient prioritization and thus significantly reduce mortality rates. The RDW plays an important role in predicting severe COVID-19, implying that the role of RBC in severe disease MESHD is underestimated.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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