Cluster analysis of long COVID symptoms for deciphering a syndrome and its long-term consequence.

Autor: Niewolik, J., Mikuteit, M., Klawitter, S., Schröder, D., Stölting, A., Vahldiek, K., Heinemann, S., Müller, F., Behrens, GMN., Klawonn, F., Dopfer-Jablonka, A., Steffens, S.
Zdroj: Immunologic Research; Aug2024, Vol. 72 Issue 4, p605-613, 9p
Abstrakt: The long-term symptoms of COVID-19 are the subject of public and scientific discussions. Understanding how those long COVID symptoms co-occur in clusters of syndromes may indicate the pathogenic mechanisms of long COVID. Our study objective was to cluster the different long COVID symptoms. We included persons who had a COVID-19 and assessed long-term symptoms (at least 4 weeks after first symptoms). Hierarchical clustering was applied to the symptoms as well as to the participants based on the Euclidean distance h of the log-values of the answers on symptom severity. The distribution of clusters within our cohort is shown in a heat map. From September 2021 to November 2023, 2371 persons with persisting long COVID symptoms participated in the study. Self-assessed long COVID symptoms were assigned to three symptom clusters. Cluster A unites rheumatological and neurological symptoms, cluster B includes neuro-psychological symptoms together with cardiorespiratory symptoms, and a third cluster C shows an association of general infection signs, dermatological and otology symptoms. A high proportion of the participants (n = 1424) showed symptoms of all three clusters. Clustering of long COVID symptoms reveals similarities to the symptomatology of already described syndromes such as the Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) or rheumatological autoinflammatory diseases. Further research may identify serological parameters or clinical risk factors associated with the shown clusters and might improve our understanding of long COVID as a systemic disease. Furthermore, multimodal treatments can be developed and scaled for symptom clusters and associated impairments. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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