Lymphedema after breast cancer is common but mostly mild. Subtle differences in self-reported hand/arm size and symptoms can be early signs of progressing lymphedema.

Background: As cancer treatments evolve, it is important to reevaluate their effect on lymphedema risk in breast cancer survivors.Methods: A population-based random sample of 631 women from metropolitan Philadelphia, Pennsylvania, diagnosed with incident breast cancer in 1999 to 2001, was followed for 5 years. Risk factor information was obtained by questionnaire and medical record review. Lymphedema was assessed with a validated questionnaire. Using Cox proportional hazards models, we estimated the relative incidence rates [hazard ratios (HR)] of lymphedema with standard adjusted multivariable analyses ignoring interactions, followed by models including clinically plausible treatment interactions.Results: Compared with no lymph node surgery, adjusted HRs for lymphedema were increased following axillary lymph node dissection [ALND; HR, 2.61; 95% confidence interval (95% CI), 1.77-3.84] but not sentinel lymph node biopsy (SLNB; HR, 1.04; 95% CI, 0.58-1.88). Risk was not increased following irradiation [breast/ chest wall only: HR, 1.18 (95% CI, 0.80-1.73); breast/chest wall plus supraclavicular field (+/− full axilla): HR, 0.86 (95% CI, 0.48-1.54)]. Eighty-one percent of chemotherapy was anthracycline based. The HR for anthracycline chemotherapy versus no chemotherapy was 1.46 (95% CI, 1.04-2.04), persisting after stratifying on stage at diagnosis or number of positive nodes. Treatment combinations involving ALND or chemotherapy resulted in approximately 4-to 5-fold increases in HRs for lymphedema [e.g., HR of 4.16 (95% CI, for SLNB/chemotherapy/no radiation] compared with no treatment.Conclusion: With standard multivariable analyses, ALND and chemotherapy increased lymphedema risk whereas radiation therapy and SLNB did not. However, risk varied by combinations of exposures.Impact: Treatment patterns should be considered when counseling and monitoring patients for lymphedema.

Background A relationship between excessive daytime sleepiness (EDS) and poor treatment adherence has been suspected but not confirmed. We hypothesized that medication adherence would be poorer in adults with heart failure (HF) and EDS and that cognitive status would be the mechanism of effect. Methods A sample of 280 adults with chronic HF was enrolled into a prospective cohort comparison study. We identified a cohort with EDS and a control group without EDS and further divided both groups into those with and without mild cognitive decline. Data on medication adherence was obtained at baseline, 3- and 6-months using the Basel Assessment of Adherence Scale (BAASIS). Regression analysis was used to clarify the contribution of EDS and cognition to medication adherence and to assess relationships over six months after adjusting for age, enrollment site, gender, race, functional class, depression, and premorbid intellect. Results At baseline, 62% of subjects were nonadherent to their medication regime. Nonadherence was significantly more common in those with EDS, regardless of cognitive status (p=0.035). The odds of nonadherence increased by 11% for each unit increase in EDS (AOR=1.11, 95% CI=1.05–1.19, p=0.001). In longitudinal models there was a 10% increase in the odds of nonadherence for each unit increase in EDS (p=0.008). The only cognition measure significantly associated with medication adherence was attention (p=0.047). Conclusion Adults with HF and EDS are more likely to have problems adhering to their medication regimen than those without EDS, regardless of their cognitive status. Identifying and correcting factors that interfere with sleep may improve medication adherence.

Background Medication nonadherence rates are high. The factors predicting nonadherence in heart failure (HF) remain unclear. Methods and Results A sample of 202 adults with HF was enrolled from the Northeastern U.S. and followed for 6 months. Specific aims were to describe the types of objectively measured medication nonadherence (e.g. taking, timing, dosing, drug holidays) and to identify contributors to nonadherence 6 months after enrollment. Latent growth mixture modeling (GMM) was used to identify distinct trajectories of adherence. Indicators of the five World Health Organization (WHO) dimensions of adherence (socioeconomic, condition, therapy, patient, and health care system) were tested to identify contributors to nonadherence. Two distinct trajectories were identified and labeled persistent adherence (77.8%) and steep decline (22.3%). Three contributors to the steep decline in adherence were identified. Participants with lapses in attention (adjusted odds ratio (OR) = 2.65, p=0.023), those with excessive daytime sleepiness (OR = 2.51, p=0.037), and those with two or more medication dosings per day (OR = 2.59, p=0.016) were more likely to have a steep decline in adherence over time than to have persistent adherence. Conclusions Two distinct patterns of adherence were identified. Three potentially modifiable contributors to nonadherence have been identified.