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 - 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 - The COvid-19 RISk and Treatments (CORIST) collaboration
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 - Fantoni, Massimo
AU - Fusco, Francesco M.
AU - Landi, Francesco
AU - Mapelli, Massimo
AU - Musso, Maria
AU - Odone, Anna
AU - Olivieri, Marco
AU - Rossato, Marco
AU - Rossi, Marianna
AU - Scorzolini, Laura
AU - Veronesi, Giovanni
AU - Iacoviello, Licia
PY - 2020
Y1 - 2020
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 - COVID-19
KW - Epidemiology
KW - In-hospital mortality
KW - Risk factors
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U2 - 10.1016/j.numecd.2020.07.031
DO - 10.1016/j.numecd.2020.07.031
M3 - Article
AN - SCOPUS:85090483492
SP - 1
EP - 15
JO - Nutrition, Metabolism and Cardiovascular Diseases
JF - Nutrition, Metabolism and Cardiovascular Diseases
SN - 0939-4753
ER -