COVID-19; Long COVID; clustering; disease severity; Humans; Female; Post-Acute COVID-19 Syndrome; Cohort Studies; Quality of Life; SARS-CoV-2; COVID-19/epidemiology; Pollution; Public Health, Environmental and Occupational Health; Health, Toxicology and Mutagenesis
Abstract :
[en] The increasing number of people living with Long COVID requires the development of more personalized care; currently, limited treatment options and rehabilitation programs adapted to the variety of Long COVID presentations are available. Our objective was to design an easy-to-use Long COVID classification to help stratify people with Long COVID. Individual characteristics and a detailed set of 62 self-reported persisting symptoms together with quality of life indexes 12 months after initial COVID-19 infection were collected in a cohort of SARS-CoV-2 infected people in Luxembourg. A hierarchical ascendant classification (HAC) was used to identify clusters of people. We identified three patterns of Long COVID symptoms with a gradient in disease severity. Cluster-Mild encompassed almost 50% of the study population and was composed of participants with less severe initial infection, fewer comorbidities, and fewer persisting symptoms (mean = 2.9). Cluster-Moderate was characterized by a mean of 11 persisting symptoms and poor sleep and respiratory quality of life. Compared to the other clusters, Cluster-Severe was characterized by a higher proportion of women and smokers with a higher number of Long COVID symptoms, in particular vascular, urinary, and skin symptoms. Our study evidenced that Long COVID can be stratified into three subcategories in terms of severity. If replicated in other populations, this simple classification will help clinicians improve the care of people with Long COVID.
Disciplines :
Immunology & infectious disease
Author, co-author :
Fischer, Aurélie ; Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg
Badier, Nolwenn ; Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg
Zhang, Lu ; Bioinformatics Platform, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg
ELBEJI, Abir; Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg
WILMES, Paul ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology
Oustric, Pauline ; Association Après J20 COVID Long France, F-28110 Lucé, France
Benoy, Charles; Centre Hospitalier Neuro-Psychiatrique, L-9002 Ettelbruck, Luxembourg ; Psychiatric Hospital, University of Basel, 4002 Basel, Switzerland
OLLERT, Markus ; University of Luxembourg ; Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg ; Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis (ORCA), University of Southern Denmark, 5000 C Odense, Denmark
FAGHERAZZI, Guy ; University of Luxembourg ; Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg
External co-authors :
yes
Language :
English
Title :
Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study.
Publication date :
30 November 2022
Journal title :
International Journal of Environmental Research and Public Health
FNR14716273 - 2020 (01/04/2020-30/06/2022) - Guy Fagherazzi
Funders :
Luxembourg National Research Fund
Funding text :
The Predi-COVID study is supported by the Luxembourg National Research Fund (FNR) (Predi-COVID, grant number 14716273), the André Losch Foundation, and the Luxembourg Institute of Health.
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