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.
Adolescent
Adult
Age Factors
Aged
Aged, 80 and over
Betacoronavirus
C-Reactive Protein
/ analysis
COVID-19
Cardiovascular Diseases
/ etiology
Coronavirus Infections
/ mortality
Female
Glomerular Filtration Rate
Hospital Mortality
Humans
Machine Learning
Male
Middle Aged
Pandemics
Pneumonia, Viral
/ mortality
Retrospective Studies
Risk Factors
SARS-CoV-2
Survival Analysis
Young Adult
COVID-19
Epidemiology
In-hospital mortality
Risk factors
Journal
Nutrition, metabolism, and cardiovascular diseases : NMCD
ISSN: 1590-3729
Titre abrégé: Nutr Metab Cardiovasc Dis
Pays: Netherlands
ID NLM: 9111474
Informations de publication
Date de publication:
30 10 2020
30 10 2020
Historique:
received:
14
07
2020
accepted:
20
07
2020
pubmed:
12
9
2020
medline:
4
11
2020
entrez:
11
9
2020
Statut:
ppublish
Résumé
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. 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 m 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.
Sections du résumé
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 m
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.
Identifiants
pubmed: 32912793
pii: S0939-4753(20)30306-9
doi: 10.1016/j.numecd.2020.07.031
pmc: PMC7833278
pii:
doi:
Substances chimiques
C-Reactive Protein
9007-41-4
Types de publication
Journal Article
Multicenter Study
Observational Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1899-1913Informations de copyright
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.
Déclaration de conflit d'intérêts
Declaration of Competing Interest All Authors declare no competing interests.
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