Corpus overview


MeSH Disease

Human Phenotype


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    A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia MESHD pneumonia HP in China

    Authors: Dawei Yang; Tao Xu; Xun Wang; Deng Chen; Ziqiang Zhang; Lichuan Zhang; Jie Liu; Kui Xiao; Li Bai; Yong Zhang; Lin Zhao; Lin Tong; Chaomin Wu; Yaoli Wang; Chunling Dong; Maosong Ye; Yu Xu; Zhenju Song; Hong Chen; Jing Li; Jiwei Wang; Fei Tan; Hai Yu; Jian Zhou; Jinming Yu; Chunhua Du; Hongqing Zhao; Yu Shang; Linian Huang; Jianping Zhao; Yang Jin; Charles A. Powell; Yuanlin Song; Chunxue Bai

    doi:10.1101/2020.08.07.20163402 Date: 2020-08-11 Source: medRxiv

    Background The outbreak of coronavirus disease MESHD 2019 (COVID-19) has become a global pandemic acute infectious disease MESHD, especially with the features of possible asymptomatic TRANS carriers TRANS and high contagiousness. It causes acute respiratory distress HP syndrome MESHD and results in a high mortality rate if pneumonia MESHD pneumonia HP is involved. Currently, it is difficult to quickly identify asymptomatic TRANS cases or COVID-19 patients with pneumonia MESHD pneumonia HP due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans, which facilitates the spread of the disease TRANS disease MESHD at the community level, and contributes to the overwhelming of medical resources in intensive care units. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist global frontline doctors to efficiently and precisely diagnose asymptomatic TRANS COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan Unversity (approval number B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. These patients had SARS-CoV-2 RT-PCR test results and chest CT scans, both of which were used as the gold standard for the diagnosis of COVID-19 and COVID-19 pneumonia MESHD pneumonia HP. In particular, the dataset included 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, and 122 asymptomatic TRANS cases who had positive RT-PCR test results, amongst whom 31 cases were diagnosed. We also integrated the function of a survey in nCapp to collect user feedback from frontline doctors. Findings We applied the statistical method of a multi-factor regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are fast and accessible: 'Residing or visiting history in epidemic regions', 'Exposure history to COVID-19 patient', 'Dry cough MESHD cough HP', ' Fatigue MESHD Fatigue HP', 'Breathlessness', 'No body temperature decrease after antibiotic treatment', 'Fingertip blood SERO oxygen saturation<=93%', ' Lymphopenia MESHD Lymphopenia HP', and 'C-reactive protein (CRP) increased'. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). To ensure the sensitivity SERO of the model, we used a cutoff value of 0.09. The sensitivity SERO and specificity of the model were 98.0% (95% CI: 96.9%, 99.1%) and 17.3% (95% CI: 15.0%, 19.6%), respectively, in the training dataset, and 96.5% (95% CI: 95.1%, 98.0%) and 18.8% (95% CI: 16.4%, 21.2%), respectively, in the validation dataset. In the subset of the 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, the model predicted 132 cases, accounting for 96.4% (95% CI: 91.7%, 98.8%) of the cases. In the subset of the 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, the model predicted 59 cases, accounting for 95.2% (95% CI: 86.5%, 99.0%) of the cases. Considering the specificity of the model, we used a cutoff value of 0.32. The sensitivity SERO and specificity of the model were 83.5% (95% CI: 80.5%, 86.4%) and 83.2% (95% CI: 80.9%, 85.5%), respectively, in the training dataset, and 79.6% (95% CI: 76.4%, 82.8%) and 81.3% (95% CI: 78.9%, 83.7%), respectively, in the validation dataset, which is very close to the published AI model. The results of the online survey 'Questionnaire Star' showed that 90.9% of nCapp users in WeChat mini programs were 'satisfied' or 'very satisfied' with the tool. The WeChat mini program received a significantly higher satisfaction rate than other platforms, especially for 'availability and sharing convenience of the App' and 'fast speed of log-in and data entry'. Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic TRANS patients and avoid misdiagnoses of cases with false-negative RT-PCR results. These patients require timely isolation or close medical supervision. By applying the model, medical resources can be allocated more reasonably, and missed diagnoses can be reduced. In addition, further education and interaction among medical professionals can improve the diagnostic efficiency for COVID-19, thus avoiding the transmission TRANS of the disease from asymptomatic MESHD asymptomatic TRANS patients at the community level.

    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.

    Association of olfactory dysfunction with hospitalization for COVID-19: a multicenter study in Kurdistan

    Authors: Hosna Zobairy; Erfan Shamsoddin; Mohammad Aziz Rasouli; Nasrollah Veisi Khodlan; Ghobad Moradi; Bushra Zareie; Sara Teymori; Jalal Asadi; Ahmad Sofi-Mahmudi; Ahmad R. Sedaghat

    doi:10.1101/2020.07.26.20158550 Date: 2020-07-28 Source: medRxiv

    Objective: To evaluate the association of olfactory dysfunction (OD) with hospitalization for COVID-19. Study Design: Multi-center cohort study. Setting: Emergency MESHD departments of thirteen COVID-19-designed hospitals in Kurdistan province, Iran. Subjects and Methods: Patients presenting with flu-like symptoms who tested positive by RT-PCR for COVID-19 between May 1st and 31st, 2020. At the time of presentation and enrollment, patients were asked about the presence of OD, fever MESHD fever HP, cough MESHD cough HP, shortness of breath, headache MESHD headache HP, rhinorrhea HP and sore throat. The severity of OD was assessed on an 11-point scale from 0 (none) to 10 ( anosmia HP). Patients were either hospitalized or sent home for outpatient care based on standardized criteria. Results: Of 203 patients, who presented at a mean of 6 days into the COVID-19 disease MESHD course, 25 patients (12.3%) had new OD and 138 patients (68.0%) were admitted for their COVID-19. Patients admitted for COVID-19 had a higher prevalence SERO of all symptoms assessed, including OD (p<0.05 in all cases), and OD identified admitted patients with 84.0% sensitivity SERO and 34.3% specificity. On univariate logistic regression, hospitalization was associated with OD (odds ratio [OR] = 2.47, 95%CI: 1.085-6.911, p=0.049). However, hospitalization for COVID-19 was not associated with OD (OR=3.22, 95% CI: 0.57-18.31, p=0.188) after controlling for confounding demographics and comorbidities. Conclusion: OD may be associated with hospitalization for (and therefore more severe) COVID-19. However, this association between OD and COVID-19 severity is more likely driven by patient characteristics linked to OD, such as greater numbers of COVID-19 symptoms experienced or high-risk comorbidities.

    Analyzing the dominant SARS-CoV-2 transmission TRANS routes towards an ab-initio SEIR model

    Authors: Swetaprovo Chaudhuri; Saptarshi Basu; Abhishek Saha

    id:2007.13596v2 Date: 2020-07-27 Source: arXiv

    Identifying the relative importance of the different transmission TRANS routes of the SARS-CoV-2 virus is an urgent research priority. To that end, the different transmission TRANS routes, and their role in determining the evolution of the Covid-19 pandemic are analyzed in this work. Probability of infection MESHD caused by inhaling virus-laden droplets (initial, ejection diameters between $0.5-750\mu m$) and the corresponding desiccated nuclei that mostly encapsulate the virions post droplet evaporation, are individually calculated. At typical, air-conditioned yet quiescent indoor space, for average viral loading, cough MESHD cough HP droplets of initial diameter between $10-50 \mu m$ have the highest infection MESHD probability. However, by the time they are inhaled, the diameters reduce to about $1/6^{th}$ of their initial diameters. While the initially near unity infection MESHD probability due to droplets rapidly decays within the first $25s$, the small yet persistent infection MESHD probability of desiccated nuclei decays appreciably only by $\mathcal{O} (1000s)$, assuming the virus sustains equally well within the dried droplet nuclei as in the droplets. Combined with molecular collision theory adapted to calculate frequency of contact TRANS frequency of contact SERO between the susceptible population and the droplet/nuclei cloud, infection MESHD rate constants are derived ab-initio, leading to a SEIR model applicable for any respiratory event - vector combination. Viral load, minimum infectious dose, sensitivity SERO of the virus half-life to the phase of its vector and dilution of the respiratory jet/puff by the entraining air are shown to mechanistically determine specific physical modes of transmission TRANS and variation in the basic reproduction number TRANS $\mathcal{R}_0$, from first principle calculations.

    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/ 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.

    EPICOVID19: Psychometric assessment and validation of a short diagnostic scale for a rapid Covid-19 screening based on reported symptoms

    Authors: Luca Bastiani; Loredana Fortunato; Stefania Pieroni; Fabrizio Bianchi; Fulvio Adorni; Federica Prinelli; Andrea Giacomelli; Gabriele Pagani; Stefania Maggi; Caterina Trevisan; Marianna Noale; Nithiya Jesuthasan; Aleksandra Sojic; Carla Pettenati; Massimo Andreoni; Raffaele Antonelli Incalzi; Massimo Galli; Sabrina Molinaro

    doi:10.1101/2020.07.22.20159590 Date: 2020-07-25 Source: medRxiv

    Background Confirmed COVID-19 cases have been reported in 213 countries and regions and as of 12 July 2020, over 12 million cases, with 561617 deaths MESHD have been reported worldwide. The number of cases changes quickly and varies depending upon which source you use to track, so in the current epidemiological context, the early recognition is critical for the rapid identification of suspected cases (with SARS-CoV-2 infection MESHD-like symptoms and signs MESHD) to be immediately subjected to quarantine measures. Although surveys are widely used for identifying COVID-19 cases, outcomes and associated risks, no validated epidemiological tool exists for surveying SARS-CoV-2 infection MESHD in the population so far. Methods Our study is the phase II of the EPICOVID19 national survey, launched in April 2020 including a national convenience sample of 201121 adults TRANS, who voluntarily filled the EPICOVID19 questionnaire. The phase II questionnaire was mailed to all subjects who underwent tests for COVID-19 by nasopharyngeal swab (NPS) and who accepted to be involved in the second phase of the study, focused on the results reported for NPS and/or serological IgG/IgM tests. We evaluated the capability of the self-reported symptoms collected through the EPICOVID19 questionnaire to discriminate the COVID-19 among symptomatic subjects, in order to identify possible cases to undergo instrumental measurements and clinical examinations. We defined a method for the identification of a total score and validated it with reference to the serological and molecular clinical diagnosis, using four standard steps: identification of critical factors, confirmation of presence of latent variable, development of optimal scoring algorithm and validation of the scoring algorithm. Findings 2703 subjects [66% response rate] completed the Phase II questionnaire. Of 2703 individuals, 694 (25.7%) were NPS(+) and of these 84 (12.1% of the 694 NPS(+)) were asymptomatic TRANS. In the individuals who performed serological testing SERO, of the 472 who did IgG(+) and 421 who did IgM(+), 22.9% and 11.6% tested positive, respectively. Among IgG(+) 1 of 108 subjects was asymptomatic TRANS (0.9%) while 5/49 subjects among IgM(+) were asymptomatic TRANS (10.2%). Compared with NPS(-), among NPS(+) subjects there was a higher rate for Fever MESHD Fever HP (421 [60.7%] vs 391[19.5% ]; p<0.0001), Loss of Taste and/or Smell (365 [52.6%] vs 239 [11.9% ]; p<0.0001) and Cough MESHD Cough HP (352 [50.7%] vs 580 [28.9% ]; p<0.0001). Also for other symptoms the frequencies were significantly higher in NPS(+) subjects than in NPS(-) ones (p<0.001). Among groups with serological tests SERO, the symptoms with higher percentages in the subjects IgG(+) were Fever MESHD Fever HP (65 [60.2%] vs 43[11.8% ]; p<0.0001) and Pain MESHD Pain HP in muscles, bones, joints (73 [67.6%] vs 71 [19.5% ]; p<0.0001). For the COVID-19 self-reported symptoms items, exploratory (proportion variance explained [89.9%]) and confirmatory factor analysis results (SMSR 0.072; RMSEA 0.052) highlights the presence of one latent variable (factor) underlying the symptoms. We define the one-factor solution as EPICOVID19 diagnostic scale and optimal score for each items was identified: Respiratory problems (1.03), Chest pain MESHD Chest pain HP (1.07), Loss of Taste and/or Smell (0.97) and Tachycardia MESHD Tachycardia HP ( palpitations HP) (1.05) were the most important symptoms. The cut-off score was 2.56 ( Sensitivity SERO 76.56%; Specificity 68.24%) in NPS(+) and 2.59 (Se 80.37; Sp 80.17) in IgG(+) subjects.

    Impact of Meteorological factors and population size on the transmission TRANS of Micro-size respiratory droplets based Coronavirus: A brief study of highly infected cities in Pakistan

    Authors: Iram Shahzadi; Anum Shahzadi; Junaid Haider; Sadia Naz; Rai M Aamir; Ali Haider; Hafiz Rizwan Sharif; Imran Mahmood Khan; Muhammad Ikram

    doi:10.1101/2020.07.14.20153544 Date: 2020-07-17 Source: medRxiv

    Ongoing Coronavirus epidemic (COVID-19) identified first in Wuhan, China posed huge impact on public health and economy around the globe. Both cough MESHD cough HP and sneeze MESHD sneeze HP based droplets or aerosols encapsulated COVID-19 particles are responsible for air borne transmission TRANS of this virus and caused unexpected escalation and high mortality worldwide. Current study intends to investigate correlation of COVID-19 epidemic with meteorological parameters particularly, temperature, rainfall, humidity, and wind speed along with population size. Data set of COVID-19 for highly infected cities of Pakistan was collected from the official website of National Institute of health (NIH). Spearman rank (rs) correlation coefficient test employed for data analysis revealed significant correlation between temperature minimum (TM), temperature average (TA), wind speed (WS) and population size (PS) with COVID-19 pandemic. Furthermore, receiver operating characteristics (ROC) curve was used to analyze the sensitivity SERO of TA, WS, and PS on transmission TRANS rate of COVID-19 in selected cities of Pakistan. The results obtained for sensitivity SERO and specificity analysis for all selected parameters signifies sensitivity SERO and direct correlation of COVID-19 transmission TRANS with temperature variation, WS and PS. Positive correlation and strong association of PS parameter with COVID-19 pandemic suggested need of more strict actions and control measures for highly populated cities. These findings will be helpful for health regulatory authorities and policymakers to take specific measures to combat COVID-19 epidemic in Pakistan.

    COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model

    Authors: Kiran Purohit; Abhishek Kesarwani; Dakshina Ranjan Kisku; Mamata Dalui

    doi:10.1101/2020.07.15.205567 Date: 2020-07-17 Source: bioRxiv

    COVID-19 is posed as very infectious and deadly pneumonia MESHD pneumonia HP type disease MESHD until recent time. Novel coronavirus or SARS-COV-2 strain is responsible for COVID-19 and it has already shown the deadly nature of respiratory disease MESHD by threatening the health of millions of lives across the globe. Clinical study reveals that a COVID-19 infected person may experience dry cough MESHD cough HP, muscle pain MESHD pain HP, headache MESHD headache HP, fever MESHD fever HP, sore throat and mild to moderate respiratory illness. At the same time, it affects the lungs badly with virus infection MESHD. So, the lung can be a prominent internal organ to diagnose the gravity of COVID-19 infection MESHD using X-Ray and CT scan images of chest. Despite having lengthy testing time, RT-PCR is a proven testing methodology to detect coronavirus infection MESHD. Sometimes, it might give more false positive and false negative results than the desired rates. Therefore, to assist the traditional RT-PCR methodology for accurate clinical diagnosis, COVID-19 screening can be adopted with X-Ray and CT scan images of lung of an individual. This image based diagnosis will bring radical change in detecting coronavirus infection MESHD in human body with ease and having zero or near to zero false positives and false negatives rates. This paper reports a convolutional neural network (CNN) based multi-image augmentation technique for detecting COVID-19 in chest X-Ray and chest CT scan images of coronavirus suspected individuals. Multi-image augmentation makes use of discontinuity information obtained in the filtered images for increasing the number of effective examples for training the CNN model. With this approach, the proposed model exhibits higher classification accuracy around 95.38% and 98.97% for CT scan and X-Ray images respectively. CT scan images with multi-image augmentation achieves sensitivity SERO of 94.78% and specificity of 95.98%, whereas X-Ray images with multi-image augmentation achieves sensitivity SERO of 99.07% and specificity of 98.88%. Evaluation has been done on publicly available databases containing both chest X-Ray and CT scan images and the experimental results are also compared with ResNet-50 and VGG-16 models.

    An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis MESHD Detection Using X-Ray Images in Resource Limited Settings

    Authors: Ali H. Al-Timemy; Rami N. Khushaba; Zahraa M. Mosa; Javier Escudero

    id:2007.08223v1 Date: 2020-07-16 Source: arXiv

    Clinicians in the frontline need to assess quickly whether a patient with symptoms indeed has COVID-19 or not. The difficulty of this task is exacerbated in low resource settings that may not have access to biotechnology tests. Furthermore, Tuberculosis MESHD (TB) remains a major health problem in several low- and middle-income countries and its common symptoms include fever MESHD fever HP, cough MESHD cough HP and tiredness, similarly to COVID-19. In order to help in the detection of COVID-19, we propose the extraction of deep features (DF) from chest X-ray images, a technology available in most hospitals, and their subsequent classification using machine learning methods that do not require large computational resources. We compiled a five-class dataset of X-ray chest images including a balanced number of COVID-19, viral pneumonia MESHD pneumonia HP, bacterial pneumonia MESHD pneumonia HP, TB, and healthy cases. We compared the performance SERO of pipelines combining 14 individual state-of-the-art pre-trained deep networks for DF extraction with traditional machine learning classifiers. A pipeline consisting of ResNet-50 for DF computation and ensemble of subspace discriminant classifier was the best performer in the classification of the five classes, achieving a detection accuracy of 91.6+ 2.6% (accuracy + 95% Confidence Interval). Furthermore, the same pipeline achieved accuracies of 98.6+1.4% and 99.9+0.5% in simpler three-class and two-class classification problems focused on distinguishing COVID-19, TB and healthy cases; and COVID-19 and healthy images, respectively. The pipeline was computationally efficient requiring just 0.19 second to extract DF per X-ray image and 2 minutes for training a traditional classifier with more than 2000 images on a CPU machine. The results suggest the potential benefits of using our pipeline in the detection of COVID-19, particularly in resource-limited settings and it can run with limited computational resources.

    RT-PCR testing to detect a COVID-19 outbreak in Austria: rapid, accurate and early diagnosis in primary care (The REAP study)

    Authors: Werner Leber; Oliver Lammel; Monika Redlberger-Fritz; Maria Elisabeth Mustafa-Korninger; Karin Stiasny; Reingard Christina Glehr; Eva-Maria Hochstrasser; Christian Hoellinger; Andrea Siebenhofer; Chris Griffiths; Jasmina Panovska-Griffiths

    doi:10.1101/2020.07.13.20152439 Date: 2020-07-15 Source: medRxiv

    Background Delay in COVID-19 detection has led to a major pandemic. We report rapid early detection of SARS-CoV-2 by reverse transcriptase-polymerase chain reaction (RT-PCR), comparing it to the serostatus of convalescent infection MESHD, at an Austrian National Sentinel Surveillance Practice in an isolated ski-resort serving a population of 22,829 people. Methods Retrospective dataset of all 73 patients presenting with mild to moderate flu-like symptoms to a sentinel practice in the ski-resort of Schladming-Dachstein, Austria, between 24 February and 03 April, 2020. We split the outbreak in two halves, by dividing the period from the first to the last case by two, to characterise the following three cohorts of patients with confirmed infection TRANS infection MESHD: people with reactive RT-PCR presenting during the first half (early acute infection MESHD) vs. those presenting in the second half (late acute), and people with non-reactive RT-PCR (late convalescent). For each cohort we report the number of cases detected, the accuracy of RT-PCR and the duration of symptoms. We also report multivariate regression of 15 clinical symptoms as covariates, comparing all people with convalescent infection MESHD to those with acute infection MESHD. Findings All 73 patients had SARS-CoV-2 RT-PCR testing. 22 patients were diagnosed with COVID-19, comprising: 8 patients presenting early acute, and 7 presenting late acute and 7 late convalescent respectively; 44 patients tested SARS-COV-2 negative, and 7 were excluded. RT-PCR sensitivity SERO was high (100%) among acute presenters, but dropped to 50% in the second half of the outbreak; specificity was 100%. The mean duration of symptoms was 2 days (range 1-4) among early acute presenters, and 4.4 days (1-7) among late acute and 8 days (2-12) among late convalescent presenters respectively. Convalescent infection MESHD was only associated with loss of taste (ORs=6.02;p=0.047). Acute infection MESHD was associated with loss of taste (OR=571.72;p=0.029), nausea MESHD nausea and vomiting HP and vomiting MESHD (OR=370.11;p=0.018), breathlessness (OR=134.46;p=0.049), and myalgia MESHD myalgia HP (OR=121.82;p=0.032); but not loss of smell, fever MESHD fever HP or cough MESHD cough HP. Interpretation RT-PCR rapidly and reliably detects early COVID-19 among people presenting with viral illness and multiple symptoms in primary care, particularly during the early phase of an outbreak. RT-PCR testing in primary care should be prioritised for effective COVID-19 prevention and control.

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MeSH Disease
Human Phenotype

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