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Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study

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 classifi...

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Autores principales: Fischer, Aurélie, Badier, Nolwenn, Zhang, Lu, Elbéji, Abir, Wilmes, Paul, Oustric, Pauline, Benoy, Charles, Ollert, Markus, Fagherazzi, Guy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740149/
https://www.ncbi.nlm.nih.gov/pubmed/36498091
http://dx.doi.org/10.3390/ijerph192316018
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author Fischer, Aurélie
Badier, Nolwenn
Zhang, Lu
Elbéji, Abir
Wilmes, Paul
Oustric, Pauline
Benoy, Charles
Ollert, Markus
Fagherazzi, Guy
author_facet Fischer, Aurélie
Badier, Nolwenn
Zhang, Lu
Elbéji, Abir
Wilmes, Paul
Oustric, Pauline
Benoy, Charles
Ollert, Markus
Fagherazzi, Guy
author_sort Fischer, Aurélie
collection PubMed
description 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.
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spelling pubmed-97401492022-12-11 Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study Fischer, Aurélie Badier, Nolwenn Zhang, Lu Elbéji, Abir Wilmes, Paul Oustric, Pauline Benoy, Charles Ollert, Markus Fagherazzi, Guy Int J Environ Res Public Health Brief Report 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. MDPI 2022-11-30 /pmc/articles/PMC9740149/ /pubmed/36498091 http://dx.doi.org/10.3390/ijerph192316018 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Brief Report
Fischer, Aurélie
Badier, Nolwenn
Zhang, Lu
Elbéji, Abir
Wilmes, Paul
Oustric, Pauline
Benoy, Charles
Ollert, Markus
Fagherazzi, Guy
Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study
title Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study
title_full Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study
title_fullStr Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study
title_full_unstemmed Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study
title_short Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study
title_sort long covid classification: findings from a clustering analysis in the predi-covid cohort study
topic Brief Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740149/
https://www.ncbi.nlm.nih.gov/pubmed/36498091
http://dx.doi.org/10.3390/ijerph192316018
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