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

Aurélie Fischer (), Nolwenn Badier, Lu Zhang, Abir Elbéji, Paul Wilmes, Pauline Oustric, Charles Benoy, Markus Ollert and Guy Fagherazzi
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Aurélie Fischer: Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg
Nolwenn Badier: Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg
Lu Zhang: Bioinformatics Platform, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg
Abir Elbéji: Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg
Paul Wilmes: Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, L-4362 Esch-sur-Alzette, Luxembourg
Pauline Oustric: Association Après J20 COVID Long France, F-28110 Lucé, France
Charles Benoy: Centre Hospitalier Neuro-Psychiatrique, L-9002 Ettelbruck, Luxembourg
Markus Ollert: Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg
Guy Fagherazzi: Deep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg

IJERPH, 2022, vol. 19, issue 23, 1-10

Abstract: 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.

Keywords: clustering; COVID-19; Long COVID; disease severity (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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