Identification of post-COVID-19 condition phenotypes, and differences in health-related quality of life and healthcare use: a cluster analysis

The aim of this cross-sectional study was to identify post-COVID-19 condition (PCC) phenotypes and to investigate the health-related quality of life (HRQoL) and healthcare use per phenotype. We administered a questionnaire to a cohort of PCC patients that included items on socio-demographics, medical characteristics, health symptoms, healthcare use, and the EQ-5D-5L. A principal component analysis (PCA) of PCC symptoms was performed to identify symptom patterns. K-means clustering was used to identify phenotypes. In total, 8630 participants completed the survey. The median number of symptoms was 18, with the top 3 being fatigue, concentration problems, and decreased physical condition. Eight symptom patterns and three phenotypes were identified. Phenotype 1 comprised participants with a lower-than-average number of symptoms, phenotype 2 with an average number of symptoms, and phenotype 3 with a higher-than-average number of symptoms. Compared to participants in phenotypes 1 and 2, those in phenotype 3 consulted significantly more healthcare providers (median 4, 6, and 7, respectively, p < 0.001) and had a significantly worse HRQoL (p < 0.001). In conclusion, number of symptoms rather than type of symptom was the driver in the identification of PCC phenotypes. Experiencing a higher number of symptoms is associated with a lower HRQoL and more healthcare use.