Modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer.

BACKGROUND: Many women with breast cancer also have a high likelihood of cardiovascular mortality, and while there are several cardiovascular risk prediction models, none have been validated in a cohort of breast cancer patients. We first compared the performance of commonly-used cardiovascular models, and then derived a new model where breast cancer and cardiovascular mortality were modeled simultaneously, to account for the competing risk endpoints and commonality of risk factors between the two events. METHODS: We included 20,462 women diagnosed with stage I-III breast cancer between 2000 and 2010 in Kaiser Permanente Northern California (KPNC) with follow-up through April 30, 2015, and examined the performance of the Framingham, CORE and SCOREOP cardiovascular risk models by area under the receiver operating characteristic curve (AUC), and observed-to -expected (O/E) ratio. We developed a multi-state model based on cause-specific hazards (CSH) to jointly model the causes of mortality. RESULTS: The extended models including breast cancer characteristics (grade, tumor size, nodal involvement) with CVD risk factors had better discrimination at 5-years with AUCs of 0.85 (95% CI 0.83, 0.86) for cardiovascular death and 0.80 (95% CI 0.78, 0.87) for breast cancer death compared with the existing cardiovascular models evaluated at 5 years AUCs ranging 0.71-0.78. Five-year calibration for breast and cardiovascular mortality from our multi-state model was also excellent (O/E = 1.01, 95% CI 0.91-1.11). CONCLUSION: A model incorporating cardiovascular risk factors, breast cancer characteristics, and competing events, outperformed traditional models of cardiovascular disease by simultaneously estimating cancer and cardiovascular mortality risks.

Mortality after breast cancer as a function of time since diagnosis by estrogen receptor status and age at diagnosis.

Our aim was to estimate how long-term mortality following breast cancer diagnosis depends on age at diagnosis, tumor estrogen receptor (ER) status, and the time already survived. We used the population-based Australian Breast Cancer Family Study which followed-up 1,196 women enrolled during 1992-1999 when aged <60 years at diagnosis with a first primary invasive breast cancer, over-sampled for younger ages at diagnosis, for whom tumor pathology features and ER status were measured. There were 375 deaths (median follow-up = 15.7; range = 0.8-21.4, years). We estimated the mortality hazard as a function of time since diagnosis using a flexible parametric survival analysis with ER status a time-dependent covariate. For women with ER-negative tumors compared with those with ER-positive tumors, 5-year mortality was initially higher (p < 0.001), similar if they survived to 5 years (p = 0.4), and lower if they survived to 10 years (p = 0.02). The estimated mortality hazard for ER-negative disease peaked at ~3 years post-diagnosis, thereafter declined with time, and at 7 years post-diagnosis became lower than that for ER-positive disease. This pattern was more pronounced for women diagnosed at younger ages. Mortality was also associated with lymph node count (hazard ratio (HR) per 10 nodes = 2.52 [95% CI:2.11-3.01]) and tumor grade (HR per grade = 1.62 [95% CI:1.34-1.96]). The risk of death following a breast cancer diagnosis differs substantially and qualitatively with diagnosis age, ER status and time survived. For women who survive >7 years, those with ER-negative disease will on average live longer, and more so if younger at diagnosis.

Mammography capacity and appointment wait times: barriers to breast cancer screening.

OBJECTIVE: To assess the impact of mammography capacity on appointment wait times. METHODS: We surveyed by telephone all mammography facilities federally certified in 2008 in California, Connecticut, Georgia, Iowa, New Mexico, and New York using a simulated patient format. County-level mammography capacity, defined as the number of mammography machines per 10,000 women aged 40 and older, was estimated from FDA facility certification records and US Census data. RESULTS: 1,614 (86%) of 1,882 mammography facilities completed the survey. Time until next available screening mammogram appointment was <1 week at 55% of facilities, 1-4 weeks at 34% of facilities, and >1 month at 11% of facilities. Facilities in counties with lower capacity had longer wait times, and a one-unit increase in county capacity was associated with 21% lower odds of a facility reporting a wait time >1 month (p < 0.01). There was no association between wait time and the availability of evening or weekend appointments or digital mammography. CONCLUSION: Lower mammography capacity is associated with longer wait times for screening mammograms. IMPACT: Enhancement of mammography resources in areas with limited capacity may reduce wait times for screening mammogram appointments, thereby increasing access to services and rates of breast cancer screening.