Clustering of long-term post-COVID syndrome phenotypes and determining some pathophysiological mechanisms of their development

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Abstract

Long-term post-COVID syndrome (LPS) has a wide prevalence (from 30 to 80%) and is represented by more than 200 symptoms. At the same time, the structure of risk factors and pathogenetic mechanisms of LPS have not been sufficiently studied. The study of these aspects is a promising direction in therapy, which can help in preventing the occurrence of long-term LPS and ensuring its effective treatment.

The aim: to determine the phenotypes (clusters) of LPS and some pathophysiological mechanisms of their development basing on cluster analysis.

Material and methods. Clinical cross-sectional screening study included 802 patients with LPS symptoms who consecutively visited outpatient clinic from April 2020 to April 2024. LPS was established in the presence of typical signs and symptoms (including in combination with symptoms of comorbid conditions) that developed during or after COVID-19 and lasted more than 12 weeks.

Results. The average duration of the period after COVID-19 in the study participants was 8.5 [3.4; 14.1] months. 802 patients were clustered into 6 LPS phenotypes: 1) polysymptomatic with chronic comorbid pathology; 2) chronic respiratory; 3) cardiovascular; 4) thromboembolic; 5) acute respiratory; 6) acute ischemic.

Conclusion. Performing cluster analysis allowed us to identify the following differences in the frequency of development of LPS phenotypes in 802 outpatients who visited the clinic during 4 years of observation: polysymptomatic with chronic comorbid pathology – 66.6%, chronic respiratory – 14.5%, cardiovascular – 5.2%, thromboembolic – 2%, acute respiratory – 8.0%, acute ischemic – 3.7%. For each LPS phenotype, specific predictors of its formation were determined. It has been proven that the pathogenetic mechanisms of LPS development differ depending on the cluster.

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About the authors

Olga V. Masalkina

E.A. Wagner Perm State Medical University of the Ministry of Healthcare of Russia

Email: omasalkina@mail.ru
ORCID iD: 0009-0006-3364-0591
SPIN-code: 4394-5330

MD, PhD (Medicine), associate professor of the Department of internal medicine and cardiology

Russian Federation, 614000, Perm, 26 Petropavlovskaya St.

Natalia A. Koziolova

E.A. Wagner Perm State Medical University of the Ministry of Healthcare of Russia

Author for correspondence.
Email: nakoziolova@mail.ru
ORCID iD: 0000-0001-7003-5186
SPIN-code: 1044-0503

MD, Dr. Sci. (Medicine), professor, head of the Department of internal medicine and cardiology

Russian Federation, 614000, Perm, 26 Petropavlovskaya St.

Elizaveta I. Merk

Fraport JSC

Email: elisaweta.merk@gmail.com
ORCID iD: 0009-0005-7425-9968

senior architect of artificial intelligence solutions

Germany, Frankfurt am Main

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2. Fig. 1. Graphical representation of the relationship between the sum of squared distances of each data point to their centroid and the number of clusters (phenotypes of long COVID syndrome) with determination of the number of clusters by the elbow method. Note: WCSS – the sum of squared distances of each data point to its centroid (Within-Cluster Sum of Squares)

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3. Fig. 2. Graphical representation of the visual component of 6 clusters (phenotypes of long COVID syndrome) using the principal component analysis method*. Note: * – each cluster is marked with a specific color, and each cluster point corresponds to an individual patient. PCA – principal component analysis (Principal Component Analysis)

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