Escarela Gabriel, Jiménez-Balandra Alan, Núñez-Antonio Gabriel, Gordillo-Moscoso Antonio
Departamento de Matemáticas, Universidad Autónoma Metropolitana-Unidad Iztapalapa, Mexico City (CDMX), Mexico.
Facultad de Medicina, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico.
Breast Cancer (Auckl). 2017 Jun 1;11:1178223417711429. doi: 10.1177/1178223417711429. eCollection 2017.
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.
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.
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.
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.
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.
对被诊断为乳腺癌的女性进行长期特定病因死亡率研究很重要，因为它能将人群分为最终死于乳腺癌和死于其他原因的患者。采用这种方法有助于识别最终死于乳腺癌风险较高的患者。
本研究的主要目的是探讨社会人口统计学和临床病理特征与被诊断为乳腺癌的女性因乳腺癌和其他原因死亡的潜在风险之间的关联。第二个目的是根据治疗情况和患者的几个重要特征，提出一种特定病因死亡率的预测生物标志物。
从监测、流行病学和最终结果癌症登记处获得了一组1990年被诊断为乳腺癌的16511名女性患者，并对其进行了20年的随访。使用竞争风险回归分析的混合模型来识别影响最终特定病因死亡率或条件特定病因风险率或两者的因素和混杂因素。缺失数据采用多重填补法处理。
诊断时的年龄与种族、婚姻状况、乳腺癌类型、肿瘤大小、雌激素受体状态、分期、淋巴结状态、手术类型和放疗状态的曲线关系是特定病因死亡率的显著危险因素，分期和淋巴结状态似乎与手术类型和放疗状态的影响相互混杂。从一组预测指标组合中获得的分数显示是一种准确的预测生物标志物。
在被诊断为乳腺癌的女性的特定病因死亡率方面，预后似乎取决于社会人口统计学和临床病理因素。本研究中提出的预测生物标志物可能有助于比传统方法更早地识别疾病的严重程度，潜在地指导未来资源分配，以实现更好的患者护理和管理策略。
