Symptom-based clusters in people with post-COVID-19 condition (PCC).

BACKGROUND

Identifying symptom clusters in post-COVID-19 condition (PCC) is a necessary step toward for developing more targeted therapeutic interventions for this heterogeneous condition. Therefore, the aim of this study was to identify symptom clusters based on 14 specific PCC symptoms, accounting for both symptom presence and impairment. The identified clusters were then compared with respect to sociodemographic, clinical, and psychological factors.

METHODS

A clinical sample of individuals with a PCC diagnosis lasting at least one year was included (final n = 1673). A two-step cluster analysis was performed to identify symptom clusters. Subsequent comparisons between clusters were performed using Mann-Whitney U tests for continuous variables and chi-square tests for categorical variables.

RESULTS

A total of four clusters were identified: two symptom burden clusters (Systemic (high burden) and Few Symptoms (low burden)) and two symptom-specific clusters (Neurocognitive and Pain). Participants in the Systemic (high burden) cluster exhibited the highest levels of psychological distress, reported the most severe fatigue, and were most frequently unemployed.

CONCLUSION

In PCC, different symptom clusters can be identified that differ in terms of sociodemographic, clinical, and psychological factors. Future research using biomarker, imaging, and longitudinal designs is needed to determine whether these symptom-based clusters correspond to distinct biological subgroups.