Varying severities of symptoms underline the relevance of personalized follow-up care in breast cancer survivors: latent class cluster analyses in a cross-sectional cohort

Abstract

Purpose: Insights into the severity of co-existing symptoms can help in identifying breast cancer survivors in need of symptom management. We aimed to identify subgroups of breast cancer survivors based on patterns of symptom severity, and characteristics associated with these subgroups.

Methods: We selected surgically treated stage I-III breast cancer survivors 1-5 years post-diagnosis from the Netherlands Cancer Registry (N = 876). We assessed experienced severity of fatigue, nausea, pain, dyspnea, insomnia, appetite, constipation, diarrhea, and emotional and cognitive symptoms through the EORTC-QLQ-C30 Quality of Life Questionnaire on a scale of 0-100. We determined subgroups of survivors using latent class cluster analyses (LCA) based on severity of co-existing symptoms and compared their mean severity to the age-matched female reference population to interpret clinical relevance. We assessed subgroup characteristics by multinomial logistic regression analyses.

Results: From 404 respondents (46%), three subgroups of survivors with distinct symptom severity were identified: low severity (n = 116, 28.7%), intermediate severity (n = 224, 55.4%), and high severity (n = 59, 14.6%). The low subgroup reported lower symptom severity than the general population; the intermediate subgroup reported a similar symptom severity, although scores for fatigue, insomnia, and cognitive symptoms were worse (small-medium clinical relevance). The high subgroup had worse symptom severity (medium-large clinical relevance). Compared to the intermediate subgroup, one (RRR: 2.75; CI: 1.22-6.19; p = 0.015) or more (RRR: 9.19; CI: 3.70-22.8; p = < 0.001) comorbidities were significantly associated with the high subgroup. We found no associated treatment characteristics.

Conclusion: We identified distinct subgroups of breast cancer survivors based on symptom severity, underlining the relevance of further exploring personalized follow-up strategies.

Keywords: Breast cancer; Cluster analysis; Follow-up care; Health-related quality of life; Long-term adverse effects.