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: Tags: Brief Report Source Type: research