IJERPH, Vol. 19, Pages 16018: Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study
IJERPH, Vol. 19, Pages 16018: Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study
International Journal of Environmental Research and Public Health doi: 10.3390/ijerph192316018
Authors:
Aurélie Fischer
Nolwenn Badier
Lu Zhang
Abir Elbéji
Paul Wilmes
Pauline Oustric
Charles Benoy
Markus Ollert
Guy Fagherazzi
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...
Source: International Journal of Environmental Research and Public Health - Category: Environmental Health Authors: Aur élie Fischer Nolwenn Badier Lu Zhang Abir Elb éji Paul Wilmes Pauline Oustric Charles Benoy Markus Ollert Guy Fagherazzi Tags: Brief Report Source Type: research
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