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10.1007/s15010-020-01446-z http://scihub22266oqcxt.onion/10.1007/s15010-020-01446-z 32440918!7240242!32440918     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32440918&cmd=llinks): Failed to open stream: HTTP request failed! 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A simple algorithm helps early identification of SARS-CoV-2 infection patients with severe progression tendency |pdf=|usr=}}{{32440918}} gab.com Text A simple algorithm helps early identification of SARS-CoV-2 infection patients with severe progression tendency JO: Infection http://www.kidney.de/pget.php?&tw=1&pmid=32440918 #FOAvip OBJECTIVES: We aimed to develop a simple algorithm to help early identification of SARS-CoV-2 infection patients with severe progression tendency. METHODS: The univariable and multivariable analysis were computed to identify the independent predictors of COVID-19 progression. The prediction model was established in a retrospective training set of 322 COVID-19 patients and was re-evaluated in a prospective validation set of 317 COVID-19 patients. RESULTS: The multivariable analysis identified age (OR = 1.061, p = 0.028), lactate dehydrogenase (LDH) (OR = 1.006, p = 0.037), and CD4 count (OR = 0.993, p = 0.006) as the independent predictors of COVID-19 progression. Consequently, the age-LDH-CD4 algorithm was derived as (age x LDH)/CD4 count. In the training set, the area under the ROC curve (AUROC) of age-LDH-CD4 model was significantly higher than that of single CD4 count, LDH, or age (0.92, 0.85, 0.80, and 0.75, respectively). In the prospective validation set, the AUROC of age-LDH-CD4 model was also significantly higher than that of single CD4 count, LDH, or age (0.92, 0.75, 0.81, and 0.82, respectively). The age-LDH-CD4 >/= 82 has high sensitive (81%) and specific (93%) for the early identification of COVID-19 patients with severe progression tendency. CONCLUSIONS: The age-LDH-CD4 model is a simple algorithm for early identifying patients with severe progression tendency following SARS-CoV-2 infection, and warrants further validation. Twit Text FOAVip A simple algorithm helps early identification of SARS-CoV-2 infection patients with severe progression tendency ????Infection http://www.kidney.de/pget.php?&tw=1&pmid=32440918 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32440918 #FOAvip English Wikipedia <ref>{{Cite pmid|32440918}}</ref> A simple algorithm helps early identification of SARS-CoV-2 infection patients with severe progression tendency #MMPMID32440918 Li Q; Zhang J; Ling Y; Li W; Zhang X; Lu H; Chen L Infection 2020[Aug]; 48 (4): 577-584 PMID32440918show ga OBJECTIVES: We aimed to develop a simple algorithm to help early identification of SARS-CoV-2 infection patients with severe progression tendency. METHODS: The univariable and multivariable analysis were computed to identify the independent predictors of COVID-19 progression. The prediction model was established in a retrospective training set of 322 COVID-19 patients and was re-evaluated in a prospective validation set of 317 COVID-19 patients. RESULTS: The multivariable analysis identified age (OR = 1.061, p = 0.028), lactate dehydrogenase (LDH) (OR = 1.006, p = 0.037), and CD4 count (OR = 0.993, p = 0.006) as the independent predictors of COVID-19 progression. Consequently, the age-LDH-CD4 algorithm was derived as (age x LDH)/CD4 count. In the training set, the area under the ROC curve (AUROC) of age-LDH-CD4 model was significantly higher than that of single CD4 count, LDH, or age (0.92, 0.85, 0.80, and 0.75, respectively). In the prospective validation set, the AUROC of age-LDH-CD4 model was also significantly higher than that of single CD4 count, LDH, or age (0.92, 0.75, 0.81, and 0.82, respectively). The age-LDH-CD4 >/= 82 has high sensitive (81%) and specific (93%) for the early identification of COVID-19 patients with severe progression tendency. CONCLUSIONS: The age-LDH-CD4 model is a simple algorithm for early identifying patients with severe progression tendency following SARS-CoV-2 infection, and warrants further validation. |*Algorithms[MESH] |*Disease Progression[MESH] |Adult[MESH] |Age Factors[MESH] |Betacoronavirus[MESH] |CD4 Lymphocyte Count[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnosis[MESH] |Female[MESH] |Humans[MESH] |L-Lactate Dehydrogenase/analysis[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis[MESH] |Predictive Value of Tests[MESH] |Prospective Studies[MESH] |Retrospective Studies[MESH] |Risk Assessment[MESH] |Risk Factors[MESH]A simple algorithm helps early identification of SARS-CoV-2 infection (epmc).pdf DeepDyve Pubget Overpricing