Evidence map›Paper›PMID 36498091›Full record

ArticleInternational journal of environmental research and public health2022

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, Guy Fagherazzi

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
5.4field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 4 countries.

Aurélie FischerDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg.ORCID 0000-0002-2822-2874
Nolwenn BadierDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg.ORCID 0000-0002-6303-457X
Lu ZhangBioinformatics Platform, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg.ORCID 0000-0001-7141-6730
Abir ElbéjiDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg.
Paul WilmesLuxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, L-4362 Esch-sur-Alzette, Luxembourg.ORCID 0000-0002-6478-2924
Pauline OustricAssociation Après J20 COVID Long France, F-28110 Lucé, France.ORCID 0000-0003-2004-4222
Charles BenoyCentre Hospitalier Neuro-Psychiatrique, L-9002 Ettelbruck, Luxembourg.
Markus OllertDepartment of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg.ORCID 0000-0002-8055-0103
Guy FagherazziDeep Digital Phenotyping Research Unit, Department of Population Health, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg.ORCID 0000-0001-5033-5966
Luxembourg Institute of Health · LUADIR Association · FRCentre Hospitalier de Luxembourg · LUUniversity of Luxembourg · LUUniversity of Southern Denmark · DK

Funding

Luxembourg National Research Fund (FNR) 14716273
6 · The paper itself

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.

Indexed as

COVID-19SARS-CoV-2Cohort StudiesFemaleHumansPost-Acute COVID-19 SyndromeQuality of LifeclusteringCOVID-19disease severityLong COVID

Identifiers

PMID36498091
PMCPMC9740149
OpenAlexW4311012223

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.