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10.1038/s41598-021-81844-x http://scihub22266oqcxt.onion/10.1038/s41598-021-81844-x 33547335!7864944!33547335     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 3246 Nephropedia Template TP {{tp|p=33547335|t=2021. Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients |pdf=|usr=}}{{33547335}} gab.com Text Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33547335 #FOAvip Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics-Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings. Twit Text FOAVip Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33547335 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33547335 #FOAvip English Wikipedia <ref>{{Cite pmid|33547335}}</ref> Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients #MMPMID33547335 Jimenez-Solem E; Petersen TS; Hansen C; Hansen C; Lioma C; Igel C; Boomsma W; Krause O; Lorenzen S; Selvan R; Petersen J; Nyeland ME; Ankarfeldt MZ; Virenfeldt GM; Winther-Jensen M; Linneberg A; Ghazi MM; Detlefsen N; Lauritzen AD; Smith AG; de Bruijne M; Ibragimov B; Petersen J; Lillholm M; Middleton J; Mogensen SH; Thorsen-Meyer HC; Perner A; Helleberg M; Kaas-Hansen BS; Bonde M; Bonde A; Pai A; Nielsen M; Sillesen M Sci Rep 2021[Feb]; 11 (1): 3246 PMID33547335show ga Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics-Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings. |*Computer Simulation[MESH] |*Machine Learning[MESH] |Age Factors[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Body Mass Index[MESH] |COVID-19/complications/*diagnosis/*mortality/physiopathology[MESH] |Comorbidity[MESH] |Critical Care[MESH] |Female[MESH] |Hospitalization[MESH] |Humans[MESH] |Hypertension/complications[MESH] |Intensive Care Units[MESH] |Male[MESH] |Middle Aged[MESH] |Prognosis[MESH] |Prospective Studies[MESH] |ROC Curve[MESH] |Respiration, Artificial[MESH] |Risk Factors[MESH]Developing and validating COVID-19 adverse outcome risk prediction mod(epmc).pdf DeepDyve Pubget Overpricing