Preprint Brief Report Version 1 Preserved in Portico This version is not peer-reviewed

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

Aurélie Fischer
* ORCID logo ,
Nolwenn Badier
ORCID logo ,
Lu Zhang
ORCID logo ,
Abir Elbeji
,
Paul Wilmes
ORCID logo ,
Pauline Oustric
,
Charles Benoy
,
Markus Ollert
,
Guy Fagherazzi
Version 1 : Received: 31 October 2022 / Approved: 2 November 2022 / Online: 2 November 2022 (01:02:16 CET)

A peer-reviewed article of this Preprint also exists.

Fischer, A.; Badier, N.; Zhang, L.; Elbéji, A.; Wilmes, P.; Oustric, P.; Benoy, C.; Ollert, M.; Fagherazzi, G. Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study. Int. J. Environ. Res. Public Health 2022, 19, 16018. Fischer, A.; Badier, N.; Zhang, L.; Elbéji, A.; Wilmes, P.; Oustric, P.; Benoy, C.; Ollert, M.; Fagherazzi, G. Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study. Int. J. Environ. Res. Public Health 2022, 19, 16018.

Abstract

The increasing number of people living with Long COVID requires the development of more personalized care, as for now 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 stratifying 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 3 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 a poor sleep and respiratory quality of life. Cluster-Severe was characterized by a higher proportion of women and smokers as in the other clusters, with a higher number of Long COVID symptoms, in particular of vascular, urinary, and skin symptoms. Our study evidenced that Long COVID can be stratified in 3 sub-categories in terms of severity. If replicated in other populations, this simple classification will help clinicians to personalize the care of people with Long COVID.

Keywords

Clustering; COVID-19; Long COVID; disease severity

Subject

Medicine and Pharmacology, Pulmonary and Respiratory Medicine

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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