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Keywords

Anxiety, Cancer, Cluster, Depressive symptom, Latent class analysis, Oncology, Posttraumatic stress disorder

 

Authors

  1. Li, Jie MMed
  2. Zhang, Huihui BM
  3. Shao, Di BM
  4. Xue, Jiaomei MMed
  5. Bai, Huayu MMed
  6. Sun, Jiwei BM
  7. Lin, Pingzhen BM
  8. Cao, Fenglin PhD

Abstract

Background: Depressive symptoms are prevalent in patients with cancer and are heterogeneous; however, existing methods of grouping patients with heterogeneous symptoms have limitations.

 

Objectives: The purpose of this study was to identify depressive symptom clusters in patients with cancer using a data-driven method and to explore their relationships with symptoms of anxiety and posttraumatic stress disorder.

 

Methods: Data from 247 patients were analyzed in this cross-sectional study. Latent class analysis was used to identify depressive symptom clusters, using 9 depressive symptoms from the Patient Health Questionnaire. Symptoms of anxiety and posttraumatic stress disorder were measured, and the relationships between them and the clusters were explored through linear regression analyses.

 

Results: Four clusters of depressive symptoms were identified: (1) minimal with sleep and appetite disturbances (23.9%), (2) somatic (22.3%), (3) moderate with sleep disturbance and fatigue (32.4%), and (4) severe (21.5%). The order of severity of anxiety and posttraumatic stress disorder symptoms was comparable across the 4 clusters of depressive symptoms. The anxiety and posttraumatic stress disorder symptoms of patients in clusters 3 and 4 were more severe than those in cluster 1 (B = 4.70-19.19, P < .001).

 

Conclusion: Using latent class analysis, 4 clusters of depressive symptoms were identified in patients with cancer, which were significantly correlated with symptoms of anxiety and posttraumatic stress disorder.

 

Implications for Practice: Latent class analysis can be used to identify clusters of depressive symptoms in patients with cancer. Such groupings may hasten the development of individualized intervention approaches tailored to patients' specific depressive clusters.