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.
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 / Di Castelnuovo, Augusto; Bonaccio, Marialaura; Costanzo, Simona; Gialluisi, Alessandro; Antinori, Andrea; Berselli, Nausicaa; Blandi, Lorenzo; Bruno, Raffaele; Cauda, Roberto; Guaraldi, Giovanni; My, Ilaria; Menicanti, Lorenzo; Parruti, Giustino; Patti, Giuseppe; Perlini, Stefano; Santilli, Francesca; Signorelli, Carlo; Stefanini, Giulio G; Vergori, Alessandra; Abdeddaim, Amina; Ageno, Walter; Agodi, Antonella; Agostoni, Piergiuseppe; Aiello, Luca; Al Moghazi, Samir; Aucella, Filippo; Barbieri, Greta; Bartoloni, Alessandro; Bologna, Carolina; Bonfanti, Paolo; Brancati, Serena; Cacciatore, Francesco; Caiano, Lucia; Cannata, Francesco; Carrozzi, Laura; Cascio, Antonio; Cingolani, Antonella; Cipollone, Francesco; Colomba, Claudia; Crisetti, Annalisa; Crosta, Francesca; Danzi, Gian B; D'Ardes, Damiano; de Gaetano Donati, Katleen; Di Gennaro, Francesco; Di Palma, Gisella; Di Tano, Giuseppe; Fantoni, Massimo; Filippini, Tommaso; Fioretto, Paola; Fusco, Francesco M; Gentile, Ivan; Grisafi, Leonardo; Guarnieri, Gabriella; Landi, Francesco; Larizza, Giovanni; Leone, Armando; Maccagni, Gloria; Maccarella, Sandro; Mapelli, Massimo; Maragna, Riccardo; Marcucci, Rossella; Maresca, Giulio; Marotta, Claudia; Marra, Lorenzo; Mastroianni, Franco; Mengozzi, Alessandro; Menichetti, Francesco; Milic, Jovana; Murri, Rita; Montineri, Arturo; Mussinelli, Roberta; Mussini, Cristina; Musso, Maria; Odone, Anna; Olivieri, Marco; Pasi, Emanuela; Petri, Francesco; Pinchera, Biagio; Pivato, Carlo A; Pizzi, Roberto; Poletti, Venerino; Raffaelli, Francesca; Ravaglia, Claudia; Righetti, Giulia; Rognoni, Andrea; Rossato, Marco; Rossi, Marianna; Sabena, Anna; Salinaro, Francesco; Sangiovanni, Vincenzo; Sanrocco, Carlo; Scarafino, Antonio; Scorzolini, Laura; Sgariglia, Raffaella; Simeone, Paola G; Spinoni, Enrico; Torti, Carlo; Trecarichi, Enrico M; Vezzani, Francesca; Veronesi, Giovanni; Vettor, Roberto; Vianello, Andrea; Vinceti, Marco; De Caterina, Raffaele; Iacoviello, Licia. - In: NMCD. NUTRITION METABOLISM AND CARDIOVASCULAR DISEASES. - ISSN 0939-4753. - 30:11(2020), pp. 1899-1913. [10.1016/j.numecd.2020.07.031]
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
Cacciatore, Francesco;Gentile, Ivan;Landi, Francesco;Leone, Armando;Olivieri, Marco;Pinchera, Biagio;Pizzi, Roberto;
2020
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
