Comparing survival rates for clusters of depressive symptoms found by Network analysis' community detection algorithms: Results from a prospective population-based study among 9774 cancer survivors from the PROFILES-registry

Abstract

Objectives: Previous studies have shown that depression is associated with mortality in patients with cancer. Depression is however a heterogeneous construct and it may be more helpful to look at different (clusters) of depressive symptoms than to look at depression as a discrete condition. The aim of the present study is to investigate whether clusters of depressive symptoms can be identified using advanced statistics and to investigate how these symptom clusters are associated with all-cause mortality in a large group of patients with cancer. Method: Data from a large population-based cohort study (PROFILES) including various cancer types were used. Eligible patients completed self-report questionnaires (i.e. Fatigue assessment scale, Hospital anxiety and depression scale, EORTC QOL-C30) after diagnosis. Survival status was determined on 31 January 2022. Results: In total, 9744 patients were included. Network analyses combining different community detection algorithms showed that clusters of depressive symptoms could be detected that correspond with motivational anhedonia, consummatory anhedonia and negative affect. Survival analyses using the variables that represented these clusters best showed that motivational and consummatory anhedonia were associated with survival. Even after controlling for clinical and sociodemographic variables items assessing motivational anhedonia were significantly associated with mortality over time. Conclusion: Separate clusters of symptoms that correspond with motivational and consummatory anhedonia and negative affect can be distinguished and anhedonia may be associated with mortality more than negative affect. Looking at particular (clusters of) depressive symptoms may be more informative and clinically relevant than using depression as a single construct (i.e. syndrome).