A longitudinal analysis of symptom burden trajectories among breast cancer patients undergoing postoperative chemotherapy and related factors: a latent class growth model

Vollständiger Abstract

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Background Symptom burden among breast cancer patients receiving postoperative chemotherapy shows marked individual variability, yet its longitudinal trajectory has not been fully characterized. Aim To delineate heterogeneous trajectories of symptom burden and identify their influencing factors in postoperative breast cancer patients undergoing chemotherapy. Methods We recruited postoperative breast cancer patients receiving chemotherapy from a tertiary hospital in Ningxia, China. Participants completed a general information questionnaire and the Chinese version of the M. D. Anderson Symptom Inventory (MDASI-C) at five time points: T1 (3 days post-surgery), T2 (14 days post-surgery), T3 (1–3 days before the first chemotherapy session), T4 (7–14 days after the 4th chemotherapy session), and T5 (7–14 days after the 8th chemotherapy session). We applied latent class growth models to identify distinct symptom burden trajectories and used logistic regression to examine factors associated with trajectory membership. Results Three distinct trajectories of symptom burden were identified: the high-risk group ( n = 18, 8.1%), the steadily increasing group ( n = 154, 67.3%), and the gradually improving group ( n = 53, 24.6%). Multinomial logistic regression analysis, using the gradually improving group as the reference, indicated that employment status (OR = 0.464, 95% CI: 0.234–0.922, p = 0.028) and a higher level of disease-related knowledge (OR = 0.351, 95% CI: 0.161–0.764, p = 0.008) serve as protective factors against adverse symptom burden trajectories. Conclusion There is notable heterogeneity within the population regarding symptom burden trajectories. In comparison to patients in the gradually improving group, those who are employed and possess a higher level of disease-related knowledge are significantly less likely to be classified within the steadily increasing group. Clinicians should prioritize the early identification of high-risk populations, engage in dynamic symptom monitoring, and implement precision management strategies based on trajectory classifications to mitigate symptom burden and enhance quality of life.