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.
Di Castelnuovo, A., Bonaccio, M., Costanzo, S., Gialluisi, A., Antinori, A., Berselli, N., Blandi, L., Bruno, R. A., 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., 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, <<NMCD. NUTRITION METABOLISM AND CARDIOVASCULAR DISEASES>>, 2020; 30 (11): 1899-1913. [doi:10.1016/j.numecd.2020.07.031] [http://hdl.handle.net/10807/165267]
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
Bruno, Rosa Anna;Cauda, Roberto;Cingolani, Antonella;De Gaetano Donati, Katleen;Fantoni, Massimo;Landi, Francesco;Murri, Rita;Raffaelli, Francesca;Torti, Carlo;Trecarichi, Enrico Maria;
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.
