Division of Gerontology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
National Cancer Registry Ireland, Cork, Ireland.
J Am Geriatr Soc. 2018 Jul;66(6):1115-1122. doi: 10.1111/jgs.15340. Epub 2018 Mar 13.
To develop a predictive model and risk score for 10-year mortality using health-related quality of life (HRQOL) in a cohort of older women with early-stage breast cancer.
Prospective cohort.
Community.
U.S. women aged 65 and older diagnosed with Stage I to IIIA primary breast cancer (N=660).
We used medical variables (age, comorbidity), HRQOL measures (10-item Physical Function Index and 5-item Mental Health Index from the Medical Outcomes Study (MOS) 36-item Short-Form Survey; 8-item Modified MOS Social Support Survey), and breast cancer variables (stage, surgery, chemotherapy, endocrine therapy) to develop a 10-year mortality risk score using penalized logistic regression models. We assessed model discriminative performance using the area under the receiver operating characteristic curve (AUC), calibration performance using the Hosmer-Lemeshow test, and overall model performance using Nagelkerke R (NR).
Compared to a model including only age, comorbidity, and cancer stage and treatment variables, adding HRQOL variables improved discrimination (AUC 0.742 from 0.715) and overall performance (NR 0.221 from 0.190) with good calibration (p=0.96 from HL test).
In a cohort of older women with early-stage breast cancer, HRQOL measures predict 10-year mortality independently of traditional breast cancer prognostic variables. These findings suggest that interventions aimed at improving physical function, mental health, and social support might improve both HRQOL and survival.
利用健康相关生活质量(HRQOL),为早期乳腺癌的老年女性队列建立一个预测模型和 10 年死亡率风险评分。
前瞻性队列研究。
社区。
美国年龄在 65 岁及以上的患有 I 期至 IIIA 期原发性乳腺癌的女性(N=660)。
我们使用了医学变量(年龄、合并症)、HRQOL 测量(医疗结局研究(MOS)36 项短式量表中的 10 项身体功能指数和 5 项心理健康指数;8 项改良 MOS 社会支持量表)以及乳腺癌变量(分期、手术、化疗、内分泌治疗),利用惩罚逻辑回归模型开发了一个 10 年死亡率风险评分。我们使用接收者操作特征曲线下的面积(AUC)评估模型的判别性能,使用 Hosmer-Lemeshow 检验评估校准性能,使用 Nagelkerke R(NR)评估整体模型性能。
与仅包含年龄、合并症和癌症分期及治疗变量的模型相比,添加 HRQOL 变量可提高预测能力(AUC 从 0.715 提高到 0.742)和整体性能(NR 从 0.190 提高到 0.221),校准效果良好(HL 检验的 p=0.96)。
在早期乳腺癌的老年女性队列中,HRQOL 测量独立于传统的乳腺癌预后变量预测 10 年死亡率。这些发现表明,旨在改善身体功能、心理健康和社会支持的干预措施可能会提高 HRQOL 和生存率。