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Enregistrement W4383500609 · doi:10.1101/2023.07.06.23292296

Long-term symptom profiles after COVID-19 <i>vs</i> other acute respiratory infections: a population-based observational study (COVIDENCE UK)

2023· preprint· en· W4383500609 sur OpenAlexfundno aff
Giulia Vivaldi, Paul Pfeffer, Mohammad Talaei, Jayson Basera, Seif O. Shaheen, Adrian R. Martineau

Notice bibliographique

RevuemedRxiv · 2023
Typepreprint
Langueen
DomaineMedicine
ThématiqueLong-Term Effects of COVID-19
Établissements canadiensnon disponible
Organismes subventionnairesAsthma and Lung UKBritish Heart FoundationNational Institute for Health and Care ResearchCancer Research UKBritish Lung FoundationBarts CharityDSM Nutritional ProductsDiabetes UKUK Research and InnovationArthritis SocietyVasculitis UK
Mots-clésMedicineLogistic regressionOdds ratioObservational studyPopulationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Internal medicineRespiratory infectionSeverity of illnessQuality of life (healthcare)Respiratory systemPediatricsDiseaseEnvironmental healthInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

Summary Background Long COVID is a well recognised, if heterogeneous, entity. Acute respiratory infections (ARIs) due to other pathogens may cause long-term symptoms, but few studies compare post-acute sequelae between SARS-CoV-2 and other ARIs. We aimed to compare symptom profiles between people with previous SARS-CoV-2 infection, people with previous non-COVID-19 ARIs, and contemporaneous controls, and to identify clusters of long-term symptoms. Methods COVIDENCE UK is a prospective, population-based UK study of ARIs in adults. We analysed data on 16 potential long COVID symptoms and health-related quality of life (HRQoL), reported in January, 2021, by participants unvaccinated against SARS-CoV-2. We classified participants as having previous SARS-CoV-2 infection or previous non-COVID-19 ARI (≥4 weeks prior) or no reported ARI. We compared symptoms by infection status using logistic and fractional regression, and identified symptom clusters using latent class analysis (LCA). Findings We included 10,203 participants (1343 [13.2%] with SARS-CoV-2 infection, 472 [4.6%] with non-COVID-19 ARI). Both types of infection were associated with increased prevalence/severity of most symptoms and decreased HRQoL compared with no infection. Participants with SARS-CoV-2 infection had increased odds of taste/smell problems and hair loss compared with participants with non-COVID-19 ARIs. Separate LCA models identified three symptom severity groups for each infection type. In the most severe groups (including 23% of participants with SARS-CoV-2, and 21% with non-COVID-19 ARI), SARS-CoV-2 infection presented with a higher probability of memory problems, difficulty concentrating, hair loss, and taste/smell problems than non-COVID-19 ARI. Interpretation Both SARS-CoV-2 and non-COVID-19 ARIs are associated with a wide range of long-term symptoms. Research on post-acute sequelae of ARIs should extend from SARS-CoV-2 to include other pathogens. Funding Barts Charity. Research in context Evidence before this study We searched PubMed and Google Scholar for studies on post-acute sequelae of COVID-19 and other acute respiratory infections (ARIs), published up to May 24, 2023. We used search terms relating to COVID-19 and other ARIs (“COVID-19”, “SARS”, “severe acute respiratory syndrome”, “Middle East respiratory”, “MERS”, “respiratory infection”, “influenza”, “flu”) and post-acute symptoms (“long COVID”, “post-acute”, “PACS”, “sequelae”, “long-term”). Previous studies have shown a wide range of post-acute sequelae for COVID-19, affecting people with all severities of the acute disease. The few studies that have compared long-term symptoms between people with COVID-19 and non-COVID-19 ARIs have generally found a higher symptom burden among people with COVID-19; however, these studies have been restricted to hospitalised patients or electronic health record data, and thus do not capture the full picture in the community. Research into long COVID phenotypes has been inconclusive, with some analyses classifying people with long COVID according to the types of symptoms experienced, and others classifying them according to the overall severity of their symptoms. Added value of this study In this population-based study of ARIs in the community, we observed high symptom burden among people with previous SARS-CoV-2 infection when compared with controls, highlighting the extensive reach of long COVID. Our finding of a similar symptom burden among people with non-COVID-19 ARIs suggests that post-acute sequelae of other ARIs may be going unrecognised, particularly given that the vast majority did not experience a severe acute infection. Latent class analyses of symptoms identified groupings based on overall symptom severity, rather than symptom types, for both SARS-CoV-2 infections and non-COVID-19 ARIs, suggesting that overall symptom burden may best characterise the experience of people with post-acute sequelae. Notably, among participants with the most severe symptoms, only half of those with previous SARS-CoV-2 infection attributed their symptoms to long COVID, suggesting they either did not believe the infection was the cause, or they did not consider their symptoms severe enough to qualify as long COVID. Implications of all the available evidence The long-term symptoms experienced by some people with previous ARIs, including SARS-CoV-2, highlights the need for improved understanding, diagnosis, and treatment of post-acute infection syndromes. As much-needed research into long COVID continues, we must take the opportunity to investigate and consider the post-acute burden of ARIs due to other pathogens.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,045
Score d'incertitude au seuil0,090

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,097
Tête enseignante GPT0,388
Écart entre enseignants0,290 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2023
Routes d'admission1
Résumé présentoui

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