Do Longitudinally Collected Symptom Scores Predict Time to Death in Advanced Breast Cancer: A Joint Modeling Analysis

Patients with advanced breast cancer have low rates of survival that can be associated with symptom burden.

This study seeks to characterize the effect of longitudinally collected symptom scores on predicting time to death for patients with advanced breast cancer.

A cohort of 993 Stage IV breast cancer patients was constructed using linked population-level health administrative databases that captured longitudinally collected symptom data using the Edmonton Symptom Assessment System. Data were captured on individual symptom scores (20,371 assessments) for pain, tiredness, drowsiness, nausea, appetite, dyspnea, depression, anxiety, and wellbeing, as well as three summative scores of total symptom distress score, physical subscore, and psychological subscore. A joint modeling approach was undertaken to simultaneously model repeated-measures longitudinal data and time-to-event data.

Of patients who died in the study, 56.11% survived for a mean time of less than three years and had lower mean symptom scores for all symptoms except shortness of breath, in comparison with patients who lived for more than three years. Symptom burden was predictive of patient time to death for all symptoms, with risk of death increasing with worsening symptom scores. For total symptom distress score, age at diagnosis (0.009; P < 0.05), chemotherapy (-0.63; P < 0.001), and palliative care (3.15; P < 0.001) were significant predictors of patient time to death.

Patients with advanced breast cancer experience chronic ongoing low symptom burden, which predicts patient time to death. Future research should examine the mechanisms by which patient characteristics, treatment, and supportive and palliative care can have an impact on patient survival.