Kimmick Gretchen G, Major Brittny, Clapp Jonathan, Sloan Jeff, Pitcher Brandelyn, Ballman Karla, Barginear Myra, Freedman Rachel A, Artz Andrew, Klepin Heidi D, Lafky Jacqueline M, Hopkins Judith, Winer Eric, Hudis Clifford, Muss Hyman, Cohen Harvey, Jatoi Aminah, Hurria Arti, Mandelblatt Jeanne
Duke Cancer Institute, Duke University Medical Center, Box 3204, Durham, NC, 29910, USA.
Alliance Statistics and Data Center, Mayo Clinic, Rochester, MN, USA.
Breast Cancer Res Treat. 2017 Jun;163(2):391-398. doi: 10.1007/s10549-017-4188-6. Epub 2017 Mar 10.
Tools to estimate survival, such as ePrognosis ( http://eprognosis.ucsf.edu/carey2.php ), were developed for general, not cancer, populations. In older patients with breast cancer, accurate overall survival estimates would facilitate discussions about adjuvant therapies.
Secondary analyses were performed of data from two parallel breast cancer studies (CALGB/Alliance 49907/NCT000224102 and CALGB/Alliance 369901/NCT00068328). We included patients (n = 971) who were age 70 years and older with complete baseline quality of life data (194 from 49907; 777 from 369901). Estimated versus observed all-cause two-year mortality rates were compared. ePrognosis score was calculated based on age, sex, and daily function (derived from EORTC QLQ-C30). ePrognosis scores range from 0 to 10, with higher scores indicating worse prognosis based on mortality of community-dwelling elders and were categorized into three groups (0-2, 3-6, 7-10). Observed mortality rates were estimated using Kaplan-Meier methods.
Patient mean age was 75.8 years (range 70-91) and 73% had stage I-IIA disease. Most patients were classified by ePrognosis as good prognosis (n = 562, 58% 0-2) and few (n = 18, 2% 7-10) poor prognosis. Two-year observed mortality rates were significantly lower than ePrognosis estimates for patients scoring 0-2 (2% vs 5%, p = 0.001) and 3-6 (8% vs 12%, p = 0.01). The same trend was seen with scores of 7-10 (23% vs 36%, p = 0.25).
ePrognosis tool only modestly overestimates mortality rate in older breast cancer patients enrolled in two cooperative group studies. This tool, which estimates non-cancer mortality risk based on readily available clinical information may inform adjuvant therapy decisions but should be validated in non-clinical trial populations.
诸如ePrognosis(http://eprognosis.ucsf.edu/carey2.php)等用于评估生存率的工具是为普通人群而非癌症人群开发的。在老年乳腺癌患者中,准确的总生存率评估将有助于辅助治疗的讨论。
对两项平行乳腺癌研究(CALGB/Alliance 49907/NCT000224102和CALGB/Alliance 369901/NCT00068328)的数据进行二次分析。我们纳入了年龄在70岁及以上且有完整基线生活质量数据的患者(n = 971)(49907中有194例;369901中有777例)。比较了估计的与观察到的全因两年死亡率。根据年龄、性别和日常功能(源自欧洲癌症研究与治疗组织QLQ-C30)计算ePrognosis评分。ePrognosis评分范围为0至10分,分数越高表明基于社区居住老年人死亡率的预后越差,并分为三组(0 - 2分、3 - 6分、7 - 10分)。使用Kaplan-Meier方法估计观察到的死亡率。
患者的平均年龄为75.8岁(范围70 - 91岁),73%患有I - IIA期疾病。大多数患者根据ePrognosis被分类为预后良好(n = 562,58%,0 - 2分),少数(n = 18,2%,7 - 10分)预后不良。对于评分为0 - 2分(2%对5%,p = 0.001)和3 - 6分(8%对12%,p = 0.01)的患者,两年观察到的死亡率显著低于ePrognosis估计值。7 - 10分的患者也呈现相同趋势(23%对36%,p = (此处原文有误,应为0.025)0.025)。
在两项合作组研究中纳入的老年乳腺癌患者中,ePrognosis工具仅适度高估了死亡率。该工具基于易于获得的临床信息估计非癌症死亡风险,可能为辅助治疗决策提供参考,但应在非临床试验人群中进行验证。