PO.CL09.02 · 临床研究
HR+/HER2-乳腺癌中区域剥夺指数（ADI）与CDK4/6抑制剂（CDK4/6i）治疗持续性（TP）之间的关联
Association between area deprivation index (ADI) and treatment persistence (TP) with CDK4/6 inhibitors (CDK4/6i)in HR+/HER2- breast cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：CDK4/6i可改善HR阳性/HER2阴性乳腺癌的浸润性无病生存期和无进展生存期，但真实世界的治疗持续性欠佳。ADI是一个经过验证的邻里社会经济劣势综合指标，已被证实与较差的用药依从性和健康结局相关。评估ADI与CDK4/6i持续性之间的关联可能识别出对公平性敏感的干预靶点。主要目的是评估早期（EBC）和转移性（MBC）乳腺癌中ADI与TP之间的关联。
方法：我们对2018年2月至2024年12月在Yale New Haven Health接受CDK4/6i处方的患者进行了一项回顾性队列研究。使用人工智能和自然语言处理提取结构化电子健康记录数据。TP使用Epic Beacon治疗计划的开始和结束日期定义，并分别对EBC和MBC队列进行分析。分析使用Python和R。起始时的居住地址在R（ezADI）中批量处理，通过美国人口普查地理编码器地理编码为12位人口普查区块组GEOID，并链接到2020年Neighborhood Atlas ADI百分位。ADI被抑制或缺失的记录被排除。ADI在队列中位数处进行二分（高于中位数=更剥夺；低于中位数=较少剥夺）。使用Cox比例风险模型，校正药物、年龄和性别，估算治疗中断的风险比（HR）。Kaplan-Meier分析按ADI和CDK4/6i总结TP。
结果：MBC队列共纳入1,363例患者，EBC队列纳入298例。两个队列的中位ADI均为30，用作剥夺临界值。在MBC中，各药物（abemaciclib、palbociclib、ribociclib）间较少剥夺组的中位ADI值为19（IQR 11-25），较剥夺组为47（IQR 39-62）。在校正药物、年龄和性别的多变量Cox模型中，较高剥夺与更早的治疗中断无显著关联（HR=0.97；95% CI，0.86-1.08；p=0.56）。较大年龄与较短TP相关（HR=1.01/年；95% CI，1.00-1.01；p<0.001）。
在EBC中，abemaciclib治疗患者的中位ADI值为19（IQR 14-25）对47（IQR 39-63），ribociclib治疗患者的中位ADI为16（IQR 10-22）对50（IQR 41-59）。ADI与中断增加无关（HR=0.98；95% CI，0.78-1.22；p=0.83）。
结论：在这一大型真实世界队列中，CDK4/6i的TP不因邻里社会经济剥夺而异。纳入个体层面健康社会决定因素的进一步工作可能更好地阐明依从性和持续性差异的驱动因素。
致谢：使用ChatGPT（OpenAI）协助文本修订和编辑以提高清晰度。
查看英文原文 English abstract
Background: CDK4/6i improve invasive disease-free and progression-free survival in HR-positive/HER2-negative breast cancer, yet real-world treatment persistence is suboptimal. ADI is a validated composite of neighborhood socioeconomic disadvantage that has been linked to worse medication adherence and health outcomes. Evaluating the association between ADI and persistence with CDK4/6i may identify equity-sensitive targets for intervention. The primary objective was to assess the association between ADI and TP in early (EBC) and metastatic (MBC) breast cancer.
Methods: We conducted a retrospective cohort study of patients prescribed CDK4/6i at Yale New Haven Health from February 2018 through December 2024. Artificial intelligence and natural language processing were used to extract structured electronic health record data. TP was defined using Epic Beacon treatment plan start and end dates and analyzed separately for EBC and MBC cohorts. Analyses used Python and R. Residential addresses at initiation were batch processed in R (ezADI), geocoded to 12-digit census block group GEOIDs via the U.S. Census Geocoder, and linked to 2020 Neighborhood Atlas ADI percentiles. Records with suppressed or missing ADI were excluded. ADI was dichotomized at the cohort median (above-median = more deprived; below-median = less deprived). Cox proportional hazards models, adjusting for medication, age, and gender were used to estimate hazard ratios (HR) for therapy discontinuation. Kaplan-Meier analyses summarized TP by ADI and CDK4/6i.
Results: A total of 1,363 patients were included in the MBC cohort and 298 in the EBC cohort. The median ADI across both cohorts was 30, which was used as the deprivation cutoff. In MBC, median ADI values were 19 (IQR 11-25) for the less-deprived groups and 47 (IQR 39-62) for the more-deprived groups across agents (abemaciclib, palbociclib, ribociclib). In multivariable Cox models adjusting for medication, age, and gender, higher deprivation was not significantly associated with earlier therapy discontinuation (HR = 0.97; 95 % CI, 0.86-1.08; p = 0.56). Older age was associated with shorter TP (HR = 1.01 per year; 95% CI, 1.00-1.01; p < 0.001).
In EBC, median ADI values were 19 (IQR 14-25) versus 47 (IQR 39-63) among abemaciclib-treated patients, and median ADI was 16 (IQR 10-22) versus 50 (IQR 41-59) among ribociclib-treated patients. ADI was not associated with increased discontinuation (HR = 0.98; 95 % CI, 0.78-1.22; p = 0.83).
Conclusions: In this large, real-world cohort, TP with CDK4/6i did not differ by neighborhood socioeconomic deprivation. Further work incorporating individual-level social determinants of health may better elucidate drivers of adherence and persistence disparities.
Acknowledgment: ChatGPT (OpenAI) was used to assist with text revision and editing for clarity.
利益披露 Disclosure
M. L. Caetano,
Eli Lilly and Company ).
J. Liu,
Eli Lilly and Company ).
B. R. Brown,
Eli Lilly and Company ).
Bayer ).
Pfizer Other, Planned advisory board participation beginning 12/2025.
G. Gong,
Eli Lilly and Company ).
S. Pandya,
Eli Lilly and Company ).
S. Taghzout,
Eli Lilly and Company ).
M. Ramos, None..
E. Tupper, None..
A. Hood, None.
M. Zummo,
Eli Lilly and Company ).
R. Legare,
Eli Lilly and Company ).
J. Du, None.
M. Lustberg,
Eli Lilly and Company ).