TY - JOUR
T1 - Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19
T2 - survival analysis and machine learning-based findings from the multicentre Italian CORIST Study
AU - COVID-19 RISK and Treatments (CORIST) Collaboration
AU - Di Castelnuovo, Augusto
AU - Bonaccio, Marialaura
AU - Costanzo, Simona
AU - Gialluisi, Alessandro
AU - Antinori, Andrea
AU - Berselli, Nausicaa
AU - Blandi, Lorenzo
AU - Bruno, Raffaele
AU - Cauda, Roberto
AU - Guaraldi, Giovanni
AU - My, Ilaria
AU - Menicanti, Lorenzo
AU - Parruti, Giustino
AU - Patti, Giuseppe
AU - Perlini, Stefano
AU - Santilli, Francesca
AU - Signorelli, Carlo
AU - Stefanini, Giulio G
AU - Vergori, Alessandra
AU - Abdeddaim, Amina
AU - Ageno, Walter
AU - Agodi, Antonella
AU - Agostoni, Piergiuseppe
AU - Aiello, Luca
AU - Al Moghazi, Samir
AU - Aucella, Filippo
AU - Barbieri, Greta
AU - Bartoloni, Alessandro
AU - Bologna, Carolina
AU - Bonfanti, Paolo
AU - Brancati, Serena
AU - Cacciatore, Francesco
AU - Caiano, Lucia
AU - Cannata, Francesco
AU - Carrozzi, Laura
AU - Cascio, Antonio
AU - Cingolani, Antonella
AU - Cipollone, Francesco
AU - Colomba, Claudia
AU - Crisetti, Annalisa
AU - Fantoni, Massimo
AU - Fusco, Francesco M
AU - Landi, Francesco
AU - Mapelli, Massimo
AU - Musso, Maria
AU - Odone, Anna
AU - Rossato, Marco
AU - Rossi, Marianna
AU - Scorzolini, Laura
AU - Iacoviello, Licia
N1 - Copyright © 2020 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.
PY - 2020/10/30
Y1 - 2020/10/30
N2 - 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 m2; 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.
AB - 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 m2; 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.
KW - Adolescent
KW - Adult
KW - Age Factors
KW - Aged
KW - Aged, 80 and over
KW - Betacoronavirus
KW - C-Reactive Protein/analysis
KW - COVID-19
KW - Cardiovascular Diseases/etiology
KW - Coronavirus Infections/mortality
KW - Female
KW - Glomerular Filtration Rate
KW - Hospital Mortality
KW - Humans
KW - Machine Learning
KW - Male
KW - Middle Aged
KW - Pandemics
KW - Pneumonia, Viral/mortality
KW - Retrospective Studies
KW - Risk Factors
KW - SARS-CoV-2
KW - Survival Analysis
KW - Young Adult
U2 - 10.1016/j.numecd.2020.07.031
DO - 10.1016/j.numecd.2020.07.031
M3 - Article
C2 - 32912793
VL - 30
SP - 1899
EP - 1913
JO - Nutrition, Metabolism and Cardiovascular Diseases
JF - Nutrition, Metabolism and Cardiovascular Diseases
SN - 0939-4753
IS - 11
ER -