Corpus overview


MeSH Disease

HGNC Genes

SARS-CoV-2 proteins

There are no SARS-CoV-2 protein terms in the subcorpus


SARS-CoV-2 Proteins
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    Developing machine learning models for predicting intensive care unit resource use during the COVID-19 pandemic MESHD

    Authors: Stephan Slot Lorenzen; Mads Nielsen; Espen Jimenez-Solem; Tonny Studsgaard Petersen; Anders Perner; Hans-Christian Thorsen-Meyer; Christian Igel; Martin Sillesen

    doi:10.1101/2021.03.19.21253947 Date: 2021-03-22 Source: medRxiv

    ABSTRACT Importance: The COVID-19 pandemic MESHD has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. Objective: We investigate whether Machine Learning (ML) can be used for predictions of intensive care requirements 5 and 10 days into the future. Design: Retrospective design where health Records from 34,012 SARS-CoV-2 positive patients was extracted. Random Forest ( RF MESHD) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 5, 10). Setting: Two Danish regions, encompassing approx. 2.5 million citizens. Participants: All patients from the bi-regional area with a registered positive SARS-CoV-2 test from March 2020 to January 2021. Main outcomes: Prediction of future 5- and 10-day requirements of ICU admission and ventilator use. Mortality was also predicted. Results. Models predicted 5-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) of 0.986 and 5-day risk of use of ventilation with an ROC-AUC of 0.995. The corresponding 5-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) of 0.930 and use of ventilation with an R2 of 0.934. Performance was comparable but slightly reduced for 10-day forecasting models. Conclusions. Random Forest-based modelling can be used for accurate 5- and 10-day forecasting predictions of ICU resource requirements.

    Country differences in transmissibility, age distribution and case-fatality of SARS-CoV-2: a global ecological analysis

    Authors: Caroline Favas; Prudence Jarrett; Ruwan Ratnayake; Oliver J Watson; Francesco Checchi

    doi:10.1101/2021.02.17.21251839 Date: 2021-02-19 Source: medRxiv

    IntroductionSARS-CoV-2 has spread rapidly across the world yet the first pandemic waves in many low-income countries appeared milder than initially forecasted through mathematical models. Hypotheses for this observed difference include under-ascertainment of cases and deaths MESHD, country population age structure, and immune modulation secondary to exposure to endemic parasitic infections MESHD. We conducted a country-level ecological study to describe patterns in key SARS-CoV-2 outcomes by country and region and to explore possible associations of the potential explanatory factors with these outcomes. MethodsWe collected publicly available data at country level and compared them using standardisation techniques. We then explored the association between exposures and outcomes using alternative approaches: random forest (RF) regression and linear (LM) regression. We adjusted for potential confounders and plausible effect modifications. ResultsAltogether, data on the mean time-varying reproduction number (mean Rt) were available for 153 countries, but standardised averages for the age of cases and deaths MESHD and for the case-fatality ratio (CFR) could only be computed for 61, 39 and 31 countries respectively. While mean Rt was highest in the WHO Europe and Americas regions, median age of death MESHD was lower in the Africa region even after standardisation, with broadly similar CFR. Population age was strongly associated with mean Rt and the age-standardised median age of observed cases and deaths MESHD in both RF MESHD and LM models. The models highlighted other plausible roles of population density, testing intensity and co-morbidity prevalence, but yielded uncertain results as regards exposure to common parasitic infections MESHD. ConclusionsThe average age of a population seems to be an important country-level factor explaining both transmissibility and the median age of observed cases and deaths MESHD, even after age-standardisation. Potential associations between endemic infections and COVID-19 MESHD are worthy of further exploration but seem unlikely, from this analysis, to be key drivers of the variation in observed COVID-19 MESHD epidemic trends. Our study was limited by the availability of outcome data and its causally uncertain ecological design, with the observed distribution of age amongst reported cases and deaths suggesting key differences in surveillance and testing strategy and capacity by country and the representativeness of case reporting of infection. Research at subnational and individual level is needed to explore hypotheses further.

    Using Machine Learning to Predict Mortality for COVID-19 MESHD Patients on Day Zero in the ICU

    Authors: Elham Jamshidi; Amirhossein Asgary; Nader Tavakoli; Alireza Zali; Hadi Esmaily; Seyed Hamid Jamaldini; Amir Daaee; Amirhesam Babajani; Mohammad Ali Sendani Kashi; Masoud Jamshidi; Sahand Rahi; Nahal Mansouri

    doi:10.1101/2021.02.04.21251131 Date: 2021-02-08 Source: medRxiv

    RationaleGiven the expanding number of COVID-19 MESHD cases and the potential for upcoming waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies. ObjectivesEarly prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission. MethodsWe studied retrospectively 263 COVID-19 MESHD ICU patients. To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Logistic regression and random forest (RF) algorithms were utilized to build classification models. The impact of each marker on the RF MESHD model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP). ResultsAmong 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume, white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin along with a history of neurological, cardiovascular, and respiratory disorders MESHD. Our RF MESHD model can predict patients outcomes with a sensitivity of 70% and a specificity of 75%. ConclusionsThe most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW along with gender and age. Complete blood count parameters were also crucial for some patients. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 MESHD ICU decision-making.

    Establishment of CORONET; COVID-19 MESHD Risk in Oncology Evaluation Tool to identify cancer patients at low versus high risk of severe complications of COVID-19 MESHD infection upon presentation to hospital

    Authors: Rebecca J Lee Dr; Cong Zhou Dr; Oskar Wysocki Dr; Rohan Shotton Dr; Ann Tivey Dr; Louise Lever Dr; Joshua Woodcock Dr; Angelos Angelakas Dr; Theingi Aung Dr; Kathryn Banfill Dr; Mark Baxter Dr; Talvinder Bhogal Dr; Hayley Boyce Dr; Ellen Copson Dr; Elena Dickens Dr; Leonie Eastlake Dr; Hannah Frost Ms; Fabio Gomes Dr; Donna Graham Dr; Christina Hague Dr; Michelle Harrison Dr; Laura Horsley Dr; Prerana Huddar Dr; Zoe Hudson Dr; Sam Khan Dr; Umair T Khan Dr; Alec Maynard Dr; Hayley McKenzie Dr; Tim Robinson Dr; Michael Rowe Dr; Anne Thomas Prof; Lance Turtle; Roseleen Sheehan Dr; Alexander Stockdale; Jamie Weaver Dr; Sophie Williams Dr; Caroline Wilson Dr; Richard Hoskins Dr; Julie Stevenson Dr; Paul Fitzpartick Dr; Carlo Palmieri Prof; Donal Landers Dr; Tim Cooksley Dr; Caroline Dive Prof; Andre Freitas Dr; Anne C Armstrong Dr

    doi:10.1101/2020.11.30.20239095 Date: 2020-12-03 Source: medRxiv

    Background: Cancer MESHD patients are at increased risk of severe COVID-19 MESHD. As COVID-19 MESHD presentation and outcomes are heterogeneous in cancer MESHD patients, decision-making tools for hospital admission, severity prediction and increased monitoring for early intervention are critical. Objective: To identify features of COVID-19 MESHD in cancer MESHD patients predicting severe disease and build a decision-support online tool; COVID-19 MESHD Risk in Oncology Evaluation Tool (CORONET) Method: Data was obtained for consecutive patients with active cancer MESHD with laboratory confirmed COVID-19 MESHD presenting in 12 hospitals throughout the United Kingdom (UK). Univariable logistic regression was performed on pre-specified features to assess their association with admission ([≥]24 hours inpatient), oxygen requirement and death MESHD. Multivariable logistic regression and random forest models (RFM) were compared with patients randomly split into training and validation sets. Cost function determined cut-offs were defined for admission/death using RFM. Performance was assessed by sensitivity, specificity and Brier scores (BS). The CORONET model was then assessed in the entire cohort to build the online CORONET tool. Results: Training and validation sets comprised 234 and 66 patients respectively with median age 69 (range 19-93), 54% males, 46% females, 71% vs 29% had solid and haematological cancers MESHD. The RFM MESHD, selected for further development, demonstrated superior performance over logistic regression with AUROC predicting admission (0.85 vs. 0.78) and death MESHD (0.76 vs. 0.72). C-reactive protein HGNC was the most important feature predicting COVID-19 MESHD severity. CORONET cut-offs for admission and mortality of 1.05 and 1.8 were established. In the training set, admission prediction sensitivity and specificity were 94.5% and 44.3% with BS 0.118; mortality sensitivity and specificity were 78.5% and 57.2% with BS 0.364. In the validation set, admission sensitivity and specificity were 90.7% and 42.9% with BS 0.148; mortality sensitivity and specificity were 92.3% and 45.8% with BS 0.442. In the entire cohort, the CORONET decision support tool recommended admission of 99% of patients requiring oxygen and of 99% of patients who died. Conclusions: CORONET, a decision support tool validated in hospitals throughout the UK showed promise in aiding decisions regarding admission and predicting COVID-19 MESHD severity in patients with cancer MESHD presenting to hospital. Future work will validate and refine in further datasets.

    An Interpretable Machine Learning Framework for Accurate Severe vs Non-severe COVID-19 MESHD Clinical Type Classification

    Authors: Yuanfang Chen; Liu Ouyang; Sheng Bao; Qian Li; Lei Han; Hengdong Zhang; Baoli Zhu; Ming Xu; Jie Liu; Yaorong Ge; Shi Chen

    doi:10.1101/2020.05.18.20105841 Date: 2020-05-22 Source: medRxiv

    Effectively and efficiently diagnosing COVID-19 MESHD patients with accurate clinical type is essential to achieve optimal outcomes of the patients as well as reducing the risk of overloading the healthcare system. Currently, severe and non-severe COVID-19 MESHD types are differentiated by only a few clinical features, which do not comprehensively characterize complicated pathological, physiological, and immunological responses to SARS-CoV-2 invasion in different types. In this study, we recruited 214 confirmed COVID-19 MESHD patients in non-severe and 148 in severe type, from Wuhan, China. The patients' comorbidity and symptoms (26 features), and blood biochemistry (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest ( RF MESHD) models using features in each modality were developed and validated to classify COVID-19 MESHD clinical types. Using comorbidity/symptom and biochemistry as input independently, RF models achieved >90% and >95% predictive accuracy, respectively. Input features' importance based on Gini impurity were further evaluated and top five features from each modality were identified (age, hypertension MESHD, cardiovascular disease MESHD, gender, diabetes MESHD; D-Dimer, hsTNI, neutrophil, IL-6, and LDH HGNC). Combining top 10 multimodal features, RF MESHD model achieved >99% predictive accuracy. These findings shed light on how the human body reacts to SARS-CoV-2 invasion as a unity and provide insights on effectively evaluating COVID-19 MESHD patient's severity and developing treatment plans accordingly. We suggest that symptoms and comorbidities can be used as an initial screening tool for triaging, while biochemistry and features combined are applied when accuracy is the priority.

    Severity Assessment of Coronavirus Disease 2019 MESHD ( COVID-19 MESHD) Using Quantitative Features from Chest CT Images

    Authors: Zhenyu Tang; Wei Zhao; Xingzhi Xie; Zheng Zhong; Feng Shi; Jun Liu; Dinggang Shen

    id:2003.11988v1 Date: 2020-03-26 Source: arXiv

    Background: Chest computed tomography (CT) is recognized as an important tool for COVID-19 MESHD severity assessment. As the number of affected patients increase rapidly, manual severity assessment becomes a labor-intensive task, and may lead to delayed treatment. Purpose: Using machine learning method to realize automatic severity assessment (non-severe or severe) of COVID-19 MESHD based on chest CT images, and to explore the severity-related features from the resulting assessment model. Materials and Method: Chest CT images of 176 patients (age 45.3$\pm$16.5 years, 96 male and 80 female) with confirmed COVID-19 MESHD are used, from which 63 quantitative features, e.g., the infection volume/ratio of the whole lung and the volume of ground-glass opacity (GGO) regions, are calculated. A random forest (RF) model is trained to assess the severity (non-severe or severe) based on quantitative features. Importance of each quantitative feature, which reflects the correlation to the severity of COVID-19 MESHD, is calculated from the RF MESHD model. Results: Using three-fold cross validation, the RF MESHD model shows promising results, i.e., 0.933 of true positive rate, 0.745 of true negative rate, 0.875 of accuracy, and 0.91 of area under receiver operating characteristic curve (AUC). The resulting importance of quantitative features shows that the volume and its ratio (with respect to the whole lung volume) of ground glass opacity (GGO) regions are highly related to the severity of COVID-19 MESHD, and the quantitative features calculated from the right lung are more related to the severity assessment than those of the left lung. Conclusion: The RF based model can achieve automatic severity assessment (non-severe or severe) of COVID-19 MESHD infection, and the performance is promising. Several quantitative features, which have the potential to reflect the severity of COVID-19 MESHD, were revealed.

    Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection MESHD: A multicenter study

    Authors: Xiaolong Qi; Zicheng Jiang; QIAN YU; Chuxiao Shao; Hongguang Zhang; Hongmei Yue; Baoyi Ma; Yuancheng Wang; Chuan Liu; Xiangpan Meng; Shan Huang; Jitao Wang; Dan Xu; Junqiang Lei; Guanghang Xie; Huihong Huang; Jie Yang; Jiansong Ji; Hongqiu Pan; Shengqiang Zou; Shenghong Ju

    doi:10.1101/2020.02.29.20029603 Date: 2020-03-03 Source: medRxiv

    Abstract Objectives To develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with pneumonia MESHD associated with SARS-CoV-2 infection MESHD. Design Cross-sectional Setting Multicenter Participants A total of 52 patients with laboratory-confirmed SARS-CoV-2 infection MESHD and their initial CT images were enrolled from 5 designated hospitals in Ankang, Lishui, Zhenjiang, Lanzhou, and Linxia between January 23, 2020 and February 8, 2020. As of February 20, patients remained in hospital or with non-findings in CT were excluded. Therefore, 31 patients with 72 lesion segments were included in the final analysis. Intervention CT radiomics models based on logistic regression (LR) and random forest (RF) were developed on features extracted from pneumonia lesions MESHD in training and inter-validation datasets. The predictive performance was further evaluated in test dataset on lung lobe- and patients-level. Main outcomes Short-term hospital stay ([≤]10 days) and long-term hospital stay (>10 days). Results The CT radiomics models based on 6 second-order features were effective in discriminating short- and long-term hospital stay in patients with pneumonia MESHD associated with SARS-CoV-2 infection MESHD, with areas under the curves of 0.97 (95%CI 0.83-1.0) and 0.92 (95%CI 0.67-1.0) by LR and RF, respectively, in the test dataset. The LR model showed a sensitivity and specificity of 1.0 and 0.89, and the RF MESHD model showed similar performance with sensitivity and specificity of 0.75 and 1.0 in test dataset. Conclusions The machine learning-based CT radiomics models showed feasibility and accuracy for predicting hospital stay in patients with pneumonia MESHD associated with SARS-CoV-2 infection MESHD.

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MeSH Disease
HGNC Genes
SARS-CoV-2 Proteins

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