Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients

Enrico De Lorenzis; Paolo Parente; Gerlando Natalello; Salvatore Soldati; Silvia Laura Bosello; Andrea Barbara; Chiara Sorge; Svetlana Axelrod; Lucrezia Verardi; Pier Giacomo Cerasuolo; Giusy Peluso; Antonella Gemma; Marina Davoli; Donatella Biliotti; Vincenzo Bruzzese; Mauro Goletti; Mirko Di Martino; Maria Antonietta D'Agostino

doi:10.1016/j.jbspin.2022.105497

Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients

Enrico De Lorenzis, Paolo Parente, Gerlando Natalello, Salvatore Soldati, Silvia Laura Bosello, Andrea Barbara, Chiara Sorge, Svetlana Axelrod, Lucrezia Verardi, Pier Giacomo Cerasuolo, Giusy Peluso, Antonella Gemma, Marina Davoli, Donatella Biliotti, Vincenzo Bruzzese, Mauro Goletti, Mirko Di Martino, Maria Antonietta D'Agostino

Research output: Contribution to journal › Article

Original language	English
Pages (from-to)	105497-105499
Number of pages	3
Journal	Joint Bone Spine
Volume	90
DOIs	https://doi.org/10.1016/j.jbspin.2022.105497
Publication status	Published - 2023

Keywords

COVID-19
Epidemiology
Public health
Systemic autoimmune rheumatic disease

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.jbspin.2022.105497

Cite this

De Lorenzis, E., Parente, P., Natalello, G., Soldati, S., Bosello, S. L., Barbara, A., Sorge, C., Axelrod, S., Verardi, L., Cerasuolo, P. G., Peluso, G., Gemma, A., Davoli, M., Biliotti, D., Bruzzese, V., Goletti, M., Di Martino, M., & D'Agostino, M. A. (2023). Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients. Joint Bone Spine, 90, 105497-105499. https://doi.org/10.1016/j.jbspin.2022.105497

@article{d984594012d8439c93ffdf64ee3a76ff,

title = "Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients",

keywords = "COVID-19, Epidemiology, Public health, Systemic autoimmune rheumatic disease, COVID-19, Epidemiology, Public health, Systemic autoimmune rheumatic disease",

author = "{De Lorenzis}, Enrico and Paolo Parente and Gerlando Natalello and Salvatore Soldati and Bosello, {Silvia Laura} and Andrea Barbara and Chiara Sorge and Svetlana Axelrod and Lucrezia Verardi and Cerasuolo, {Pier Giacomo} and Giusy Peluso and Antonella Gemma and Marina Davoli and Donatella Biliotti and Vincenzo Bruzzese and Mauro Goletti and {Di Martino}, Mirko and D'Agostino, {Maria Antonietta}",

year = "2023",

doi = "10.1016/j.jbspin.2022.105497",

language = "English",

volume = "90",

pages = "105497--105499",

journal = "Joint Bone Spine",

issn = "1297-319X",

publisher = "Paris : Elsevier 2001-",

}

De Lorenzis, E, Parente, P, Natalello, G, Soldati, S, Bosello, SL, Barbara, A, Sorge, C, Axelrod, S, Verardi, L, Cerasuolo, PG, Peluso, G, Gemma, A, Davoli, M, Biliotti, D, Bruzzese, V, Goletti, M, Di Martino, M & D'Agostino, MA 2023, 'Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients', Joint Bone Spine, vol. 90, pp. 105497-105499. https://doi.org/10.1016/j.jbspin.2022.105497

Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients. / De Lorenzis, Enrico; Parente, Paolo; Natalello, Gerlando et al.
In: Joint Bone Spine, Vol. 90, 2023, p. 105497-105499.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients

AU - De Lorenzis, Enrico

AU - Parente, Paolo

AU - Natalello, Gerlando

AU - Soldati, Salvatore

AU - Bosello, Silvia Laura

AU - Barbara, Andrea

AU - Sorge, Chiara

AU - Axelrod, Svetlana

AU - Verardi, Lucrezia

AU - Cerasuolo, Pier Giacomo

AU - Peluso, Giusy

AU - Gemma, Antonella

AU - Davoli, Marina

AU - Biliotti, Donatella

AU - Bruzzese, Vincenzo

AU - Goletti, Mauro

AU - Di Martino, Mirko

AU - D'Agostino, Maria Antonietta

PY - 2023

Y1 - 2023

KW - COVID-19

KW - Epidemiology

KW - Public health

KW - Systemic autoimmune rheumatic disease

KW - COVID-19

KW - Epidemiology

KW - Public health

KW - Systemic autoimmune rheumatic disease

UR - http://hdl.handle.net/10807/237507

U2 - 10.1016/j.jbspin.2022.105497

DO - 10.1016/j.jbspin.2022.105497

M3 - Article

SN - 1297-319X

VL - 90

SP - 105497

EP - 105499

JO - Joint Bone Spine

JF - Joint Bone Spine

ER -

De Lorenzis E, Parente P, Natalello G, Soldati S, Bosello SL, Barbara A et al. Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients. Joint Bone Spine. 2023;90:105497-105499. doi: 10.1016/j.jbspin.2022.105497

Chronic related group classification system as a new public health tool to predict risk and outcome of COVID-19 in patients with systemic rheumatic diseases: A population-based study of more than forty thousand patients

Keywords

UN SDGs

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