Clusters of post-acute COVID-19 symptoms: a latent class analysis across 9 databases and 7 countries.

López-Güell K., Català M., Dedman D., Duarte-Salles T., Kolde R., López-Blasco R., Martínez Á., Mercier G., Abellan A., Arinze JT., Burkard T., Burn E., Cuccu Z., Delmestri A., Delseny D., Khalid S., Kim C., Kim J-W., Kostka K., Loste C., Mayer MA., Meléndez-Cardiel J., Mercadé-Besora N., Mosseveld M., Nishimura A., Nordeng HM., Oyinlola JO., Paredes R., Pérez-Crespo L., Pineda-Moncusí M., Ramírez-Anguita JM., Trinh NTH., Uusküla A., Valdivieso B., Prieto-Alhambra D., Xie J., Mateu L., Jödicke AM.

OBJECTIVE: Prior evidence has suggested the multisystem symptomatic manifestations of post-acute COVID-19 condition (PCC). Here we conducted a network cluster analysis of 24 World Health Organization-proposed symptoms to identify potential latent subclasses of PCC. STUDY DESIGN AND SETTING: Individuals with a positive test of or diagnosed with SARS-CoV-2 after September 2020 and with at least 1 symptom within ≥90 to 365 days following infection were included. Subanalyses were conducted among people with ≥3 different symptoms. Summary characteristics were provided for each cluster. All analyses were conducted separately in 9 databases from 7 countries, including data from primary care, hospitals, national health claims and national health registries, allowing to compare clusters across the different healthcare settings. RESULTS: This study included 787,078 persons with PCC. Single-symptom clusters were common across all databases, particularly for joint pain, anxiety, depression and allergy. Complex clusters included anxiety-depression and abdominal-gastrointestinal symptoms. CONCLUSION: Substantial heterogeneity within and between PCC clusters was seen across health-care settings. Current definitions of PCC should be critically reviewed to reflect this variety in clinical presentation.

DOI

10.1016/j.jclinepi.2025.111867

Type

Journal article

Publication Date

2025-09-01T00:00:00+00:00

Volume

185

Keywords

Clustering, Latent class analysis, Long COVID, Post-acute COVID-19 condition, Real-world data, Humans, COVID-19, Male, Female, Middle Aged, Cluster Analysis, Post-Acute COVID-19 Syndrome, Databases, Factual, Latent Class Analysis, Adult, SARS-CoV-2, Aged

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