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 HP pneumonia MESHD 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 respiratory distress MESHD syndrome and results in a high mortality rate if pneumonia HP is involved. Currently, it is difficult to quickly identify asymptomatic TRANS cases or COVID-19 patients with pneumonia HP pneumonia MESHD 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 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 HP pneumonia MESHD. 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 HP', ' Fatigue HP', 'Breathlessness', 'No body temperature decrease after antibiotic treatment', 'Fingertip blood SERO oxygen saturation<=93%', ' Lymphopenia HP Lymphopenia MESHD', 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 TRANS patients at the community level.

    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 respiratory distress MESHD syndrome ( ARDS MESHD) was the most common complication of coronavirus disease-2019(COVID-19), leading to poor clinical outcomes. However, the model to predict the in-hospital incidence of ARDS MESHD 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 MESHD 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 HP hypertension MESHD, chronic obstructive pulmonary disease HP obstructive pulmonary disease MESHD ( COPD MESHD), cough HP cough MESHD, 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.

    COVID-19 diagnosis by routine blood SERO tests using machine learning

    Authors: Matjaž Kukar; Gregor Gunčar; Tomaž Vovko; Simon Podnar; Peter Černelč; Miran Brvar; Mateja Zalaznik; Mateja Notar; Sašo Moškon; Marko Notar

    id:2006.03476v1 Date: 2020-06-04 Source: arXiv

    Physicians taking care of patients with coronavirus disease MESHD (COVID-19) have described different changes in routine blood SERO parameters. However, these changes, hinder them from performing COVID-19 diagnosis. We constructed a machine learning predictive model for COVID-19 diagnosis. The model was based and cross-validated on the routine blood SERO tests of 5,333 patients with various bacterial and viral infections MESHD, and 160 COVID-19-positive patients. We selected operational ROC point at a sensitivity SERO of 81.9% and specificity of 97.9%. The cross-validated area under the curve (AUC) was 0.97. The five most useful routine blood SERO parameters for COVID19 diagnosis according to the feature importance scoring of the XGBoost algorithm were MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. tSNE visualization showed that the blood SERO parameters of the patients with severe COVID-19 course are more like the parameters of bacterial than viral infection MESHD. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever HP fever MESHD, cough HP cough MESHD, myalgia HP myalgia MESHD, and other symptoms can now have initial routine blood SERO tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results present a significant contribution to improvements in COVID-19 diagnosis.

    An emerging marker predicting the severity of COVID-19: Neutrophil-Lymphocyte Count Ratio

    Authors: Minping Zhang; Enhua Xiao; Jiayi Liu; Yeyu Cai; Qizhi Yu

    doi:10.21203/ Date: 2020-05-14 Source: ResearchSquare

    Background: To analyze clinical features and laboratory indicators and identify the markers of exacerbation in COVID-19. Methods: We reviewed clinical histories of 177 patients with confirmed COVID-19. The patients were categorized into mild group (153 patients) and severe group (24 patients). The baseline demographic and laboratory indicators of all patients were collected, including the neutrophil-lymphocyte count ratio (NLCR) and C-reactive protein to albumin ratio (CAR). Receiver operating characteristic curve (ROC) analysis was performed to search for indicators predicting exacerbation in COVID-19 patients, and acquiring the area under the curves (AUCs), sensitivity SERO, specificity and cut-off value. Results: The age TRANS of the severe group were significantly older than those of the mild group (P <0.01). Fever HP was the typical symptom in all COVID-19 patients. Cough HP and fatigue HP were manifested in mild group, yet severe patients were more prominent in dyspnea HP. The laboratory indicators showing that the mild group mainly had an elevated C-reactive protein; the severe group had a decreased lymphocyte count and lymphocyte ratio. WBC, neutrophil count, neutrophil ratio, D-dimer, AST, ALT, LDH,  BUN, CRP levels increased. Furthermore, compared to mild group, WBC, neutrophil count, neutrophil ratio (Neut%), D-dimer, total bilirubin, albumin, AST, ALT, LDH, BUN, creatine kinase, CRP, CAR, NLCR were significantly higher, the lymphocyte count, lymphocyte ratio, and APTT were significantly lower  in  severe group (P<0.05). The ROC indicating that NLCR, Neut%, CAR, CRP, and LDH were better at distinguishing mild and severe patients. The AUCs of NLCR was larger than others (NLCR>Neut%>CAR>CRP>LDH: 0.939>0.925>0.908>0.895>0.873), which suggested that NLCR was the optimal maker; a cut-off value for NLCR of  6.15  had 87.5% sensitivity SERO and 97.6% specificity for predicting exacerbation in COVID-19 patients. Conclusions: The different types of COVID-19 had significant differences in age TRANS, clinical symptoms and laboratory indicators, and severe patients might be easier to suffer from the multiple organ damage. An elevated NLCR may indicate that the disease was progressing towards exacerbation. It was essential to dynamically monitor the serum SERO NLCR levels which contributed to evaluate the patient's condition and efficacy. NLCR could be used as a novel, highly specific and sensitive marker for predicting severity of COVID-19 patients.

    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.

    Clinical Characteristics of 34 Children TRANS with Coronavirus Disease MESHD-2019 in the West of China: a Multiple-center Case Series

    Authors: Che Zhang; Jiaowei Gu; Quanjing Chen; Na Deng; Jingfeng Li; Li Huang; Xihui Zhou

    doi:10.1101/2020.03.12.20034686 Date: 2020-03-16 Source: medRxiv

    BACKGROUND Up to 9 March, 2020, 109577 patients were diagnosed with coronavirus disease MESHD-2019 (COVID-19) globally. The clinical and epidemiological characteristics of adult TRANS patients have been revealed recently. However, the information of paediatric patients remains unclear. We describe the clinical and epidemiological characteristics of paediatric patients to provide valuable insight into early diagnosis of COVID-19 in children TRANS, as well as epidemic control policy making. METHODS and FINDINGS This retrospective, observational study was a case series performed at 4 hospitals in the west of China. Thirty-four paediatric patients with COVID-19 were included from January 1 to February 25, 2020. And the final follow-up visit was completed by February 28, 2020. Clinical and epidemiological characteristics were analyzed on the basis of demographic data, medical history, laboratory tests, radiological findings, and treatment information. Data analysis was performed on 34 paediatrics patients with COVID-19 aged TRANS from 1 to 144 months (median 33.00, IQR 10.00 - 94.25), among whom 14 males TRANS (41.18%) were included. 47.60% of patients were noticed without any exposure history. The median incubation period TRANS was 10.50 (7.75 - 25.25) days. Infections of other respiratory pathogens were reported in 16 patients (47.06%). The most common initial symptoms were fever HP fever MESHD (76.47%), cough HP (58.82%), and expectoration (20.59%). Vomiting HP Vomiting MESHD (11.76%) and diarrhea HP diarrhea MESHD (11.76%) were also reported in a considerable portion of cases. A remarkable increase was detected in serum SERO amyloid A for 17 patients (85.00%) and high- sensitivity SERO C-reactive protein for 17 patients (58.62%), while a decrease of prealbumin was noticed in 25 patients (78.13%). In addition, the levels of lactate dehydrogenase was increased significantly in 28 patients (82.35%), as well as -hydroxybutyrate dehydrogenase in 25 patients (73.53%). Patchy lesions in lobules were detected by chest computed tomographic scans in 28 patients (82.36%). The typical feature of ground-glass opacity for adults TRANS was rare in paediatric patients (2.94%). A late-onset pattern of lesions in lobules were also noticed. Stratified analysis of the clinical features were not performed due to relatively limited samples. CONCLUSIONS Our data presented the clinical and epidemiological features of paediatric patients systemically. The findings offer new insight into the early identification and intervention of paediatric patients with COVID-19.

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MeSH Disease
Human Phenotype

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