Profiling Covid-19 patients with respect to level of severity: an integrated statistical approach.

Autor: Cugnata F; University Centre of Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milan, Italy., Scarale MG; University Centre of Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milan, Italy., De Lorenzo R; School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.; Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy., Simonini M; Nephrology and Dialysis Unit, Genomics of Renal Diseases and Hypertension Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy., Citterio L; Nephrology and Dialysis Unit, Genomics of Renal Diseases and Hypertension Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy., Querini PR; School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.; Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy., Castagna A; School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.; Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy., Di Serio C; University Centre of Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milan, Italy. diserio.clelia@hsr.it.; Biomedical Faculty, Università Della Svizzera Italiana, Lugano, Switzerland. diserio.clelia@hsr.it., Lanzani C; School of Medicine, Vita-Salute San Raffaele University, Milan, Italy.; Nephrology and Dialysis Unit, Genomics of Renal Diseases and Hypertension Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Apr 04; Vol. 13 (1), pp. 5498. Date of Electronic Publication: 2023 Apr 04.
DOI: 10.1038/s41598-023-32089-3
Abstrakt: A full understanding of the characteristics of Covid-19 patients with a better chance of experiencing poor vital outcomes is critical for implementing accurate and precise treatments. In this paper, two different advanced data-driven statistical approaches along with standard statistical methods have been implemented to identify groups of patients most at-risk for death or severity of respiratory distress. First, the tree-based analysis allowed to identify profiles of patients with different risk of in-hospital death (by Survival Tree-ST analysis) and severity of respiratory distress (by Classification and Regression Tree-CART analysis), and to unravel the role on risk stratification of highly dependent covariates (i.e., demographic characteristics, admission values and comorbidities). The ST analysis identified as the most at-risk group for in-hospital death the patients with age > 65 years, creatinine [Formula: see text] 1.2 mg/dL, CRP [Formula: see text] 25 mg/L and anti-hypertensive treatment. Based on the CART analysis, the subgroups most at-risk of severity of respiratory distress were defined by patients with creatinine level [Formula: see text] 1.2 mg/dL. Furthermore, to investigate the multivariate dependence structure among the demographic characteristics, the admission values, the comorbidities and the severity of respiratory distress, the Bayesian Network analysis was applied. This analysis confirmed the influence of creatinine and CRP on the severity of respiratory distress.
(© 2023. The Author(s).)
Databáze: MEDLINE
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