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

Background. Identifying symptom clusters in post-COVID-19 condition (PCC) is crucial for developing targeted therapeutic interventions and gaining a better understanding of the underlying pathophysiological mechanisms. 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 ” and “ Few Symptoms ”) and two symptom-specific clusters (“ Neurocognitive ” and “ Pain ”). Patients in the “ Systemic ” cluster reported greater psychological distress and more chronic comorbidities. Compared to the “ Pain ” cluster, the “ Neurocognitive ” cluster included more younger women. Conclusion. In PCC, different symptom clusters can be identified that differ in terms of sociodemographic, clinical, and psychological factors. Future research should then investigate biologically defined subgroups to better understand the underlying pathophysiological mechanisms.