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10.1007/s00330-020-07022-1 http://scihub22266oqcxt.onion/10.1007/s00330-020-07022-1 32607634!7326621!32607634     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32607634&cmd=llinks): Failed to open stream: HTTP request failed! 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A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19 |pdf=|usr=}}{{32607634}} gab.com Text A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19 JO: Eur Radiol http://www.kidney.de/pget.php?&tw=1&pmid=32607634 #FOAvip OBJECTIVES: To develop a predictive model and scoring system to enhance the diagnostic efficiency for coronavirus disease 2019 (COVID-19). METHODS: From January 19 to February 6, 2020, 88 confirmed COVID-19 patients presenting with pneumonia and 80 non-COVID-19 patients suffering from pneumonia of other origins were retrospectively enrolled. Clinical data and laboratory results were collected. CT features and scores were evaluated at the segmental level according to the lesions' position, attenuation, and form. Scores were calculated based on the size of the pneumonia lesion, which graded at the range of 1 to 4. Air bronchogram, tree-in-bud sign, crazy-paving pattern, subpleural curvilinear line, bronchiectasis, air space, pleural effusion, and mediastinal and/or hilar lymphadenopathy were also evaluated. RESULTS: Multivariate logistic regression analysis showed that history of exposure (beta = 3.095, odds ratio (OR) = 22.088), leukocyte count (beta = - 1.495, OR = 0.224), number of segments with peripheral lesions (beta = 1.604, OR = 1.604), and crazy-paving pattern (beta = 2.836, OR = 2.836) were used for establishing the predictive model to identify COVID-19-positive patients (p < 0.05). In this model, values of area under curve (AUC) in the training and testing groups were 0.910 and 0.914, respectively (p < 0.001). A predicted score for COVID-19 (PSC-19) was calculated based on the predictive model by the following formula: PSC-19 = 2 x history of exposure (0-1 point) - 1 x leukocyte count (0-2 points) + 1 x peripheral lesions (0-1 point) + 2 x crazy-paving pattern (0-1 point), with an optimal cutoff point of 1 (sensitivity, 88.5%; specificity, 91.7%). CONCLUSIONS: Our predictive model and PSC-19 can be applied for identification of COVID-19-positive cases, assisting physicians and radiologists until receiving the results of reverse transcription-polymerase chain reaction (RT-PCR) tests. KEY POINTS: * Prediction of RT-PCR positivity is crucial for fast diagnosis of patients suspected of having coronavirus disease 2019 (COVID-19). * Typical CT manifestations are advantageous for diagnosing COVID-19 and differentiation of COVID-19 from other types of pneumonia. * A predictive model and scoring system combining both clinical and CT features were herein developed to enable high diagnostic efficiency for COVID-19. Twit Text FOAVip A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19 ????Eur Radiol http://www.kidney.de/pget.php?&tw=1&pmid=32607634 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32607634 #FOAvip English Wikipedia <ref>{{Cite pmid|32607634}}</ref> A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19 #MMPMID32607634 Qin L; Yang Y; Cao Q; Cheng Z; Wang X; Sun Q; Yan F; Qu J; Yang W Eur Radiol 2020[Dec]; 30 (12): 6797-6807 PMID32607634show ga OBJECTIVES: To develop a predictive model and scoring system to enhance the diagnostic efficiency for coronavirus disease 2019 (COVID-19). METHODS: From January 19 to February 6, 2020, 88 confirmed COVID-19 patients presenting with pneumonia and 80 non-COVID-19 patients suffering from pneumonia of other origins were retrospectively enrolled. Clinical data and laboratory results were collected. CT features and scores were evaluated at the segmental level according to the lesions' position, attenuation, and form. Scores were calculated based on the size of the pneumonia lesion, which graded at the range of 1 to 4. Air bronchogram, tree-in-bud sign, crazy-paving pattern, subpleural curvilinear line, bronchiectasis, air space, pleural effusion, and mediastinal and/or hilar lymphadenopathy were also evaluated. RESULTS: Multivariate logistic regression analysis showed that history of exposure (beta = 3.095, odds ratio (OR) = 22.088), leukocyte count (beta = - 1.495, OR = 0.224), number of segments with peripheral lesions (beta = 1.604, OR = 1.604), and crazy-paving pattern (beta = 2.836, OR = 2.836) were used for establishing the predictive model to identify COVID-19-positive patients (p < 0.05). In this model, values of area under curve (AUC) in the training and testing groups were 0.910 and 0.914, respectively (p < 0.001). A predicted score for COVID-19 (PSC-19) was calculated based on the predictive model by the following formula: PSC-19 = 2 x history of exposure (0-1 point) - 1 x leukocyte count (0-2 points) + 1 x peripheral lesions (0-1 point) + 2 x crazy-paving pattern (0-1 point), with an optimal cutoff point of 1 (sensitivity, 88.5%; specificity, 91.7%). CONCLUSIONS: Our predictive model and PSC-19 can be applied for identification of COVID-19-positive cases, assisting physicians and radiologists until receiving the results of reverse transcription-polymerase chain reaction (RT-PCR) tests. KEY POINTS: * Prediction of RT-PCR positivity is crucial for fast diagnosis of patients suspected of having coronavirus disease 2019 (COVID-19). * Typical CT manifestations are advantageous for diagnosing COVID-19 and differentiation of COVID-19 from other types of pneumonia. * A predictive model and scoring system combining both clinical and CT features were herein developed to enable high diagnostic efficiency for COVID-19. |*Betacoronavirus[MESH] |Adult[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnosis/epidemiology[MESH] |Female[MESH] |Humans[MESH] |Lung/*diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis/epidemiology[MESH] |Predictive Value of Tests[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH]A predictive model and scoring system combining clinical and CT charac(epmc).pdf DeepDyve Pubget Overpricing