Clustering Analysis Identified Three Long COVID Phenotypes and Their Association with General Health Status and Working Ability

Abstract

Background/aim: This study aimed to distinguish different phenotypes of long COVID through the post-COVID syndrome (PCS) score based on long-term persistent symptoms following COVID-19 and evaluate whether these symptoms affect general health and work ability. In addition, the study identified predictors for severe long COVID.

Method: This cluster analysis included cross-sectional data from three cohorts of patients after COVID-19: non-hospitalized (n = 401), hospitalized (n = 98) and those enrolled at the post-COVID outpatient's clinic (n = 85). All the subjects responded to the survey on persistent long-term symptoms and sociodemographic and clinical factors. K-Means cluster analysis and ordinal logistic regression were used to create PCS scores that were used to distinguish patients' phenotypes.

Results: 506 patients with complete data on persistent symptoms were divided into three distinct phenotypes: none/mild (59%), moderate (22%) and severe (19%). The patients with severe phenotype, with the predominating symptoms were fatigue, cognitive impairment and depression, had the most reduced general health status and work ability. Smoking, snuff, body mass index (BMI), diabetes, chronic pain and symptom severity at COVID-19 onset were factors predicting severe phenotype.

Conclusion: This study suggested three phenotypes of long COVID, where the most severe was associated with the highest impact on general health status and working ability. This knowledge on long COVID phenotypes could be used by clinicians to support their medical decisions regarding prioritizing and more detailed follow-up of some patient groups.

Keywords: COVID-19; general health status; long COVID phenotypes; persistent long-term symptoms; working ability.

Figure 1

The flow chart of the study population.

Figure 2

The Sankey chart for individuals from the cohorts (left) to the PCS clusters (right). In the non-hospitalized cohort, 77.9% individuals go to cluster none/mild, 19.5% of individuals go to cluster moderate (PCS cluster) and 2.6% go to cluster severe. In the hospitalized cohort, 32.5% of individuals go to cluster none/mild, 43.4% go to cluster moderate (PCS cluster) and 24.1% go to cluster severe. Lastly, in post-COVID cohort, 0% of individuals go to cluster none/mild, 8.0% go to cluster moderate and 92.0% of individuals go to cluster severe.

Figure 3

(A) The frequency of long-term remining symptoms in three cohorts and in (B) PCS score groups/phenotypes.

Figure 4

(A) The estimated Pearson correlation between PCS scores and the differences in working ability is −0.620 (p < 0.001), with a bootstrapped confidence interval of −0.691 to −0.531. The color of the dots denotes which groups they come from. (B) The estimated Pearson correlation between the PCS scores and the differences in health is −0.429 (p < 0.001), with a bootstrapped confidence interval of −0.523 to −0.326. The color of the dots denotes which groups they come from.

Figure 4

(A) The estimated Pearson correlation between PCS scores and the differences in working ability is −0.620 (p < 0.001), with a bootstrapped confidence interval of −0.691 to −0.531. The color of the dots denotes which groups they come from. (B) The estimated Pearson correlation between the PCS scores and the differences in health is −0.429 (p < 0.001), with a bootstrapped confidence interval of −0.523 to −0.326. The color of the dots denotes which groups they come from.