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Background <p>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.</p> Methods <p>A clinical sample of individuals with a PCC diagnosis lasting at least one year was included (final <i>n</i> = 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.</p> Results <p>A total of four clusters were identified: two symptom burden clusters (<i>Systemic</i> (high burden) and <i>Few Symptoms</i> (low burden)) and two symptom-specific clusters (<i>Neurocognitive</i> and <i>Pain</i>). Participants in the <i>Systemic</i> (high burden) cluster exhibited the highest levels of psychological distress, reported the most severe fatigue, and were most frequently unemployed.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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  • Charlotte Kuczyk,
  • Christoph Herrmann-Lingen,
  • Maike Stolz,
  • Christian Krauth,
  • Birte Burger,
  • Jona Theodor Stahmeyer,
  • Mariel Nöhre,
  • Aline Debener,
  • Christopher Käufer,
  • Franziska Richter,
  • Carlotta Derad,
  • Martin Hellmich,
  • Martina de Zwaan

摘要

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.