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

Bologna C.;Carrozzi L.;Mengozzi A.;Petri F.;De Caterina R.;
2020-01-01

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.
2020
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, G. G.; 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, G. B.; D'Ardes, D.; de Gaetano Donati, K.; Di Gennaro, F.; Di Palma, G.; Di Tano, G.; Fantoni, M.; Filippini, T.; Fioretto, P.; Fusco, F. M.; 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, C. A.; 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, P. G.; Spinoni, E.; Torti, C.; Trecarichi, E. M.; Vezzani, F.; Veronesi, G.; Vettor, R.; Vianello, A.; Vinceti, M.; De Caterina, R.; Iacoviello, L.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1082986
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