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A mixed-method study of identifying symptom clusters in adult patients with advanced cancer

  • Mojtaba Miladinia,
  • Kourosh Zarea,
  • Mahin Gheibizadeh,
  • Mina Jahangiri,
  • Hossein Karimpourian,
  • Darioush Rokhafroz

摘要

Purpose

This study aimed to identify symptom clusters (SCs) in adults with advanced cancer using a mixed-method approach.

Methods

This parallel mixed-method study was conducted in academic cancer care centers in Ahvaz, Iran. In the quantitative phase, 640 patients completed the Memorial Symptom Assessment Scale, and SCs were extracted using hierarchical cluster analysis with dendrogram and heatmap visualization. In the qualitative phase, 24 semi-structured interviews were conducted with 18 participants, and data were analyzed using conventional content analysis. Integration followed Creswell’s approach through simultaneous comparison and joint display of findings. A multidisciplinary expert panel guided the data merging process.

Results

Quantitative analysis identified six clusters: gastrointestinal problems, appearance changes, respiratory issues, psychoneurological symptoms, mind–body symptoms, and hormonal. Qualitative analysis revealed seven clusters: mind–body symptoms, respiratory issues, appearance changes, psychoneurological symptoms, gastrointestinal problems, psychosocial symptoms, and spiritual-emotional symptoms. Integration led to eight final clusters. Five clusters were identified by both methods, mutually reinforcing and complementing one another. The hormonal cluster was exclusive to quantitative analysis, while psychosocial and spiritual-emotional clusters emerged only through qualitative analysis. Symptoms such as lack of energy, difficulty sleeping, worrying, feeling sad, and feeling nervous appeared across multiple clusters and were identified as overlapping (bridge) symptoms.

Conclusion

The mixed-method approach enabled a multidimensional understanding of SCs in advanced cancer, allowing for both confirmation and expansion of findings. This study facilitates effective symptom management through the identification of SCs. Recognizing bridge symptoms offers key targets for intervention, with potential to impact multiple clusters simultaneously.