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10.1016/j.numecd.2020.07.031 http://scihub22266oqcxt.onion/10.1016/j.numecd.2020.07.031 32912793!7833278!32912793     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32912793&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Nutr+Metab+Cardiovasc+Dis 2020 ; 30 (11): 1899-1913 Nephropedia Template TP {{tp|p=32912793|t=2020. Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study |pdf=|usr=}}{{32912793}} gab.com Text Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study JO: Nutr Metab Cardiovasc Dis http://www.kidney.de/pget.php?&tw=1&pmid=32912793 #FOAvip BACKGROUND AND AIMS: There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death. METHODS AND RESULTS: Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a geographical gradient, Northern Italian regions featuring more than twofold higher death rates as compared to Central/Southern areas (15.6% vs 6.4%, respectively). Machine learning analysis revealed that the most important features in death classification were impaired renal function, elevated C reactive protein and advanced age. These findings were confirmed by multivariable Cox survival analysis (hazard ratio (HR): 8.2; 95% confidence interval (CI) 4.6-14.7 for age >/=85 vs 18-44 y); HR = 4.7; 2.9-7.7 for estimated glomerular filtration rate levels <15 vs >/= 90 mL/min/1.73 m(2); HR = 2.3; 1.5-3.6 for C-reactive protein levels >/=10 vs </= 3 mg/L). No relation was found with obesity, tobacco use, cardiovascular disease and related-comorbidities. The associations between these variables and mortality were substantially homogenous across all sub-groups analyses. CONCLUSIONS: Impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death in a large cohort of unselected patients with COVID-19, admitted to 30 different clinical centres all over Italy. Twit Text FOAVip Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study ????Nutr Metab Cardiovasc Dis http://www.kidney.de/pget.php?&tw=1&pmid=32912793 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32912793 #FOAvip English Wikipedia <ref>{{Cite pmid|32912793}}</ref> Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study #MMPMID32912793 Di Castelnuovo A; Bonaccio M; Costanzo S; Gialluisi A; Antinori A; Berselli N; Blandi L; Bruno R; Cauda R; Guaraldi G; My I; Menicanti L; Parruti G; Patti G; Perlini S; Santilli F; Signorelli C; Stefanini GG; Vergori A; Abdeddaim A; Ageno W; Agodi A; Agostoni P; Aiello L; Al Moghazi S; Aucella F; Barbieri G; Bartoloni A; Bologna C; Bonfanti P; Brancati S; Cacciatore F; Caiano L; Cannata F; Carrozzi L; Cascio A; Cingolani A; Cipollone F; Colomba C; Crisetti A; Crosta F; Danzi GB; D'Ardes D; de Gaetano Donati K; Di Gennaro F; Di Palma G; Di Tano G; Fantoni M; Filippini T; Fioretto P; Fusco FM; Gentile I; Grisafi L; Guarnieri G; Landi F; Larizza G; Leone A; Maccagni G; Maccarella S; Mapelli M; Maragna R; Marcucci R; Maresca G; Marotta C; Marra L; Mastroianni F; Mengozzi A; Menichetti F; Milic J; Murri R; Montineri A; Mussinelli R; Mussini C; Musso M; Odone A; Olivieri M; Pasi E; Petri F; Pinchera B; Pivato CA; Pizzi R; Poletti V; Raffaelli F; Ravaglia C; Righetti G; Rognoni A; Rossato M; Rossi M; Sabena A; Salinaro F; Sangiovanni V; Sanrocco C; Scarafino A; Scorzolini L; Sgariglia R; Simeone PG; Spinoni E; Torti C; Trecarichi EM; Vezzani F; Veronesi G; Vettor R; Vianello A; Vinceti M; De Caterina R; Iacoviello L Nutr Metab Cardiovasc Dis 2020[Oct]; 30 (11): 1899-1913 PMID32912793show ga BACKGROUND AND AIMS: There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death. METHODS AND RESULTS: Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a geographical gradient, Northern Italian regions featuring more than twofold higher death rates as compared to Central/Southern areas (15.6% vs 6.4%, respectively). Machine learning analysis revealed that the most important features in death classification were impaired renal function, elevated C reactive protein and advanced age. These findings were confirmed by multivariable Cox survival analysis (hazard ratio (HR): 8.2; 95% confidence interval (CI) 4.6-14.7 for age >/=85 vs 18-44 y); HR = 4.7; 2.9-7.7 for estimated glomerular filtration rate levels <15 vs >/= 90 mL/min/1.73 m(2); HR = 2.3; 1.5-3.6 for C-reactive protein levels >/=10 vs |*Betacoronavirus[MESH] |*Hospital Mortality[MESH] |*Machine Learning[MESH] |Adolescent[MESH] |Adult[MESH] |Age Factors[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |C-Reactive Protein/analysis[MESH] |COVID-19[MESH] |Cardiovascular Diseases/*etiology[MESH] |Coronavirus Infections/*mortality[MESH] |Female[MESH] |Glomerular Filtration Rate[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*mortality[MESH] |Retrospective Studies[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH] |Survival Analysis[MESH]Common cardiovascular risk factors and in-hospital mortality in 3,894 (epmc).pdf DeepDyve Pubget Overpricing