Factors affecting treatment decision making for women with breast cancer

This study examined the factors affecting treatment decision making for young women with early stage breast cancer. Thirty women, aged 35 to 52 years, were presented information about two equally effective chemotherapy treatments following surgery for breast cancer using an educational instrument called a "decision board." Although equally effective, the treatments differ with regards to side effects and treatment schedule. The purpose of this research was to investigate what factors affect the decision-making process. Following administration of the decision board, women were given a take-home version to review and asked to return one to two weeks later with a decision, at which time they completed a questionnaire. theoretical framework for this study was constructed from the literature on self-directed learning and critical thinking. The Overall, the factors rated most important to the treatment decision were related to quality of life, side effects, and length of treatment. Five factors were found to be rated significantly different by the women who chose one treatment versus the other in terms of importance to their decision. These were side effects in general, vomiting, hair loss, family role, and the number of trips to the cancer centre required for treatment.Implications and recommendations for patient education, research, and practice evolved from the findings of this study.

The relationship between body mass index and breast cancer recurrence/progression and breast cancer-specific death

The aim of this study was to describe the nonlinear association between body mass index (BMI) and breast cancer outcomes and to determine whether BMI improves prediction of outcomes. A cohort of906 breast cancer patients diagnosed at Henry Ford Health System, Detroit (1985-1990) were studied. The median follow-up was 10 years. Multivariate logistic regression was used to model breast cancer recurrence/progression and breast cancer-specific death. Restricted cubic splines were used to model nonlinear effects. Receiver operator characteristic areas under the curves (ROC AUC) were used to evaluate prediction. BMI was nonlinearly associated with recurrence/progression and death (p= 0.0230 and 0.0101). Probability of outcomes increased with increase or decrease ofBMI away from 25. BMI splines were suggestive of improved prediction of death. The ROC AUCs for nested models with and without BMI were 0.8424 and 0.8331 (p= 0.08). I f causally associated, modifying patients BMI towards 25 may improve outcomes.