Classes of women experiencing similar symptom trajectories while receiving chemotherapy for breast cancer

Breast cancer is characterized by significant disease-related and treatment-related symptoms. The objectives of this study were to identify classes of women experiencing similar symptom trajectories while undergoing chemotherapy for breast cancer and to determine if demographic, clinical, and symptom variables are associated with specific subgroups. Through a secondary analysis, Latent Growth Mixture Modeling (LGMM) was used to examine potential subgroups of women experiencing similar symptom patterns. Daily symptom severity data for 10 symptoms reported by 166 women during cycles 2 and 3 were analyzed. The women's mean age was 53 and most were Caucasian with Stage II disease. Factors related to subgroup membership were explored for association with subgroups using independent-samples t tests, ANOVA, and chi-square analysis. The multisymptom model did not reveal distinct subgroups of women with similar symptom profiles. A 3-class solution was found for fatigue and 2-class models were found for disturbed sleep, depressed mood, and anxiety. Education was the only associated demographic factor with women with no college more likely to be in the moderate anxiety group (p=.03). Type of chemotherapy was the only associated clinical variable with women who received doxorubicin more likely to be in higher severity classes for fatigue (p=.01), depressed mood (p<.01), and anxiety (p=.04). More hours spent lying down was associated with membership in the higher fatigue (p=.02) and anxiety (p=.03) classes. iv A variety of other symptoms were associated with worsening or higher severity symptom classes for each of the four symptoms in one or both treatment cycles. Higher overall symptoms in cycle 2 were associated with the higher severity subgroups for all four symptoms in cycle 3. Limitations were found using LGMM with daily symptom data and caution is warranted in interpreting study results. Further research with longitudinal data, advanced statistical methods, and large samples is needed. Findings suggest symptoms are common and co-occur. Clinicians should be aware that symptoms at higher severity levels persist over cycles and require intensified management earlier to reduce continuing symptom burden.