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10.1016/j.bjid.2020.06.009 http://scihub22266oqcxt.onion/10.1016/j.bjid.2020.06.009 32721387!7381934!32721387     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Braz+J+Infect+Dis 2020 ; 24 (4): 343-348 Nephropedia Template TP {{tp|p=32721387|t=2020. A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data |pdf=|usr=}}{{32721387}} gab.com Text A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data JO: Braz J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32721387 #FOAvip OBJECTIVES: Differential diagnosis of COVID-19 includes a broad range of conditions. Prioritizing containment efforts, protective personal equipment and testing can be challenging. Our aim was to develop a tool to identify patients with higher probability of COVID-19 diagnosis at admission. METHODS: This cross-sectional study analyzed data from 100 patients admitted with suspected COVID-19. Predictive models of COVID-19 diagnosis were performed based on radiology, clinical and laboratory findings; bootstrapping was performed in order to account for overfitting. RESULTS: A total of 29% of patients tested positive for SARS-CoV-2. Variables associated with COVID-19 diagnosis in multivariate analysis were leukocyte count </=7.7x10(3)mm(-3), LDH >273U/L, and chest radiographic abnormality. A predictive score was built for COVID-19 diagnosis, with an area under ROC curve of 0.847 (95% CI 0.77-0.92), 96% sensitivity and 73.5% specificity. After bootstrapping, the corrected AUC for this model was 0.827 (95% CI 0.75-0.90). CONCLUSIONS: Considering unavailability of RT-PCR at some centers, as well as its questionable early sensitivity, other tools might be used in order to identify patients who should be prioritized for testing, re-testing and admission to isolated wards. We propose a predictive score that can be easily applied in clinical practice. This score is yet to be validated in larger populations. Twit Text FOAVip A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data ????Braz J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32721387 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32721387 #FOAvip English Wikipedia <ref>{{Cite pmid|32721387}}</ref> A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data #MMPMID32721387 Vieceli T; Oliveira Filho CM; Berger M; Saadi MP; Salvador PA; Anizelli LB; Crivelaro PCF; Butzke M; Zappelini RS; Seligman BGDS; Seligman R Braz J Infect Dis 2020[Jul]; 24 (4): 343-348 PMID32721387show ga OBJECTIVES: Differential diagnosis of COVID-19 includes a broad range of conditions. Prioritizing containment efforts, protective personal equipment and testing can be challenging. Our aim was to develop a tool to identify patients with higher probability of COVID-19 diagnosis at admission. METHODS: This cross-sectional study analyzed data from 100 patients admitted with suspected COVID-19. Predictive models of COVID-19 diagnosis were performed based on radiology, clinical and laboratory findings; bootstrapping was performed in order to account for overfitting. RESULTS: A total of 29% of patients tested positive for SARS-CoV-2. Variables associated with COVID-19 diagnosis in multivariate analysis were leukocyte count 273U/L, and chest radiographic abnormality. A predictive score was built for COVID-19 diagnosis, with an area under ROC curve of 0.847 (95% CI 0.77-0.92), 96% sensitivity and 73.5% specificity. After bootstrapping, the corrected AUC for this model was 0.827 (95% CI 0.75-0.90). CONCLUSIONS: Considering unavailability of RT-PCR at some centers, as well as its questionable early sensitivity, other tools might be used in order to identify patients who should be prioritized for testing, re-testing and admission to isolated wards. We propose a predictive score that can be easily applied in clinical practice. This score is yet to be validated in larger populations. |*Clinical Laboratory Techniques[MESH] |Adult[MESH] |Aged[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Coronavirus Infections/*diagnosis[MESH] |Cross-Sectional Studies[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis[MESH] |Predictive Value of Tests[MESH] |Radiography, Thoracic[MESH] |SARS-CoV-2[MESH]A predictive score for COVID-19 diagnosis using clinical, laboratory a(epmc).pdf DeepDyve Pubget Overpricing