Long-Term Cause-Specific Mortality After Surgery for Women With Breast Cancer: A 20-Year Follow-Up Study From Surveillance, Epidemiology, and End Results Cancer Registries.

BACKGROUND

Research into long-term cause-specific mortality of women diagnosed with breast cancer is important because it allows for the splitting of the population into patients who eventually die from breast cancer and from other causes. The adoption of this approach helps to identify patients with an elevated risk of eventual death from breast cancer.

OBJECTIVE

The primary aim of this study was to examine the associations between both sociodemographic and clinicopathologic characteristics and the underlying risks of death from breast cancer and from other causes for women diagnosed with breast cancer. A second aim was to propose a predictive biomarker of cause-specific mortality in terms of treatment and several important characteristics of a patient.

METHODS

A cohort of 16 511 female patients diagnosed with breast cancer in 1990 was obtained from the Surveillance, Epidemiology, and End Results cancer registries and followed for 20 years. A mixture model for the regression analysis of competing risks was used to identify factors and confounders that affected either the eventual cause-specific mortality or conditional cause-specific hazard rates, or both. Missing data were handled with multiple imputation.

RESULTS

Curvilinear relationships of age at diagnosis along with race, marital status, breast cancer type, tumor size, estrogen receptor status, extension, lymph node status, type of surgery, and radiotherapy status were significant risk factors for the cause-specific mortality, with extension and lymph node status appearing to be confounded with the effects of both type of surgery and radiotherapy status. The score obtained from combining a set of predictors showed to be an accurate predictive biomarker.

CONCLUSIONS

In cause-specific mortality of women diagnosed breast cancer, prognosis appears to depend on both sociodemographic and clinicopathologic factors. The predictive biomarker proposed in this study may help identifying the level of seriousness of the disease earlier than traditional methods, potentially guiding future allocation of resources for better patient care and management strategies.