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10.7554/eLife.60519 http://scihub22266oqcxt.onion/10.7554/eLife.60519 33044170!7595731!33044170     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       Elife 2020 ; 9 (ä): ä Nephropedia Template TP {{tp|p=33044170|t=2020. Early prediction of level-of-care requirements in patients with COVID-19 |pdf=|usr=}}{{33044170}} gab.com Text Early prediction of level-of-care requirements in patients with COVID-19 JO: Elife http://www.kidney.de/pget.php?&tw=1&pmid=33044170 #FOAvip This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease. Twit Text FOAVip Early prediction of level-of-care requirements in patients with COVID-19 ????Elife http://www.kidney.de/pget.php?&tw=1&pmid=33044170 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33044170 #FOAvip English Wikipedia <ref>{{Cite pmid|33044170}}</ref> Early prediction of level-of-care requirements in patients with COVID-19 #MMPMID33044170 Hao B; Sotudian S; Wang T; Xu T; Hu Y; Gaitanidis A; Breen K; Velmahos GC; Paschalidis IC Elife 2020[Oct]; 9 (ä): ä PMID33044170show ga This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease. |*Betacoronavirus[MESH] |*Health Services Needs and Demand[MESH] |*Pandemics[MESH] |Adult[MESH] |Aged[MESH] |Area Under Curve[MESH] |Body Mass Index[MESH] |COVID-19[MESH] |Comorbidity[MESH] |Coronavirus Infections/epidemiology/*therapy[MESH] |Diabetes Mellitus/epidemiology[MESH] |Female[MESH] |Hospitalization/statistics & numerical data[MESH] |Humans[MESH] |Intensive Care Units/statistics & numerical data/supply & distribution[MESH] |Male[MESH] |Massachusetts/epidemiology[MESH] |Middle Aged[MESH] |Nonlinear Dynamics[MESH] |Pneumonia, Viral/epidemiology/*therapy[MESH] |Procedures and Techniques Utilization[MESH] |ROC Curve[MESH] |Respiration, Artificial/statistics & numerical data[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH]Early prediction of level-of-care requirements in patients with COVID-(epmc).pdf DeepDyve Pubget Overpricing