Identifying subtypes of Long COVID: a systematic review
Notice bibliographique
Résumé
Background: Long COVID, a persistent condition following SARS-CoV-2 infection, exhibits diverse symptoms across multiple organ systems. This study aims to summarize the existing clustering and classification approaches to support the management of Long COVID. Methods: Following PRISMA guidelines, we systematically searched PubMed, Embase, Web of Science, and Google Scholar from their inception to January 21, 2025, and updated the search on October 1, 2025, to identify studies that presented a way to categorize Long COVID patients or symptoms. Data extraction and quality assessment were conducted for eligible studies. We presented symptom co-occurrence networks, and performed meta-analysis to estimate the percentage of different organ system-based symptom clusters. In addition, we conducted an exploratory analysis of the determinants of different symptom clusters. The protocol was registered in OSF (https://doi.org/10.17605/OSF.IO/J483F). Findings: Forty-seven cohort studies and 17 cross-sectional studies categorizing Long COVID subtypes or symptoms were included, encompassing 2.43 million participants across 20 countries. The methodological quality of the cohort studies was on average high (mean Newcastle-Ottawa scale score: 7.5/9), and of the 17 cross-sectional studies moderate (mean Joanna Briggs Institute tool score: 0.61/1.00). Patients or symptoms were categorized either according to the co-occurrence of symptoms (n = 30 studies, 46.9%); by the affected organ system (n = 16, 25.0%); by severity stratification (n = 9, 14.1%); by clinical indicators (n = 3, 4.7%); or by using other ways of classification (n = 6, 9.4%). Among the 30 studies defining patient clusters by the co-occurrence of symptoms, fatigue was the most frequently used descriptor for a cluster, either alone or together with other symptoms (n = 15 studies). Pairwise co-occurrence analysis revealed some commonly used symptom dyads, including olfactory-gustatory dysfunction (n = 10 times), anxiety-depression (n = 10) and joint pain/swelling-muscle pain (n = 9). Fatigue was a recurrent core symptom, frequently co-occurring with joint pain/swelling (n = 9 times) or muscle pain (n = 7), cognitive symptoms (n = 7), and dyspnea (n = 7). Meta-analysis of the organ system-based subtypes showed that respiratory symptom cluster had the highest pooled percentage (47% [95% CI: 29%-65%]), followed by neurological (31% [95% CI: 3%-60%]) and gastrointestinal clusters (28% [95% CI: 0%-57%]). These percentages represent the proportion of Long COVID patients with each symptom cluster within the 16 included organ system-based subtyping studies, not population-level prevalence of Long COVID. Exploratory analysis indicated that symptom subtypes were influenced by factors such as sex, age, virus variant, and comorbidities. Interpretation: This review identified four major approaches for categorizing Long COVID patients and their symptoms. Symptom co-occurrence and organ system were the most commonly used subtypes used in categorization. Fatigue and olfactory-gustatory dysfunction emerged as recurrent core symptoms across multiple subtypes of Long COVID. Funding: This work was supported by the K. C. Wong Education Foundation, Hong Kong, the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2024-I2M-ZD-011), the Beijing Nova Program (20240484523), the Elite Medical Professionals Project of China-Japan Friendship Hospital (NO. ZRJY2024-GG03), and the National High Level Hospital Clinical Research Funding.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».