Widening demographic gaps in CAR-T therapy utilization for multiple myeloma in the United States.
Widening demographic gaps in CAR-T therapy utilization for multiple myeloma in the United States.
复制标题
美国多发性骨髓瘤 CAR-T 疗法利用方面的人口差距不断扩大。
DOI：
10.1038/s41409-023-02102-4
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发表时间：
2023
影响因子：
4.8
通讯作者：
D'Souza,Anita
中科院分区：
文献类型：
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作者：
Lin,Mingqian;Estrada-Merly,Noel;Eapen,Mary;Zamora,AnthonyE;Pezzin,LilianaE;Winn,AaronN;Philip,Joyce;Schinke,Carolina;Drobyski,WilliamR;AndersonJr,LarryD;D'Souza,Anita
嵌合抗原受体T细胞(CAR-T)治疗是一种很有前途的抗多发性骨髓瘤(MM)治疗方案。根据对复发/难治性MM患者的临床试验结果[1，2]，FDA已经批准了两种CAR-T疗法：2021年3月的依达卡他汀和2022年2月的自体西他卡他汀。有证据表明，新的治疗方法往往会导致社会经济和种族差异[3]。有了CAR-T疗法对MM的卓越控制的承诺，确保所有患者平等地获得治疗是重要的。少量但不断增长的文献表明，CAR-T的使用存在种族差异[4，5]。截至2022年12月，美国联邦药品管理局颁布了第117-328号公法，要求临床试验包括多样性行动计划，以确保更好地代表全国人口。然而，一旦一种药物获得批准，几乎没有机制(如果有的话)来强制平等获得。我们试图使用一个具有代表性的大型数据集来探索不同年龄、性别、种族和民族的CAR-T疗法使用情况的差异。多发性骨髓瘤患者是通过TriNetX识别的，TriNetX是一种电子医疗记录数据库，它结合了参与医疗组织(HCO)的实时健康记录数据，以生成纵向临床数据。在TriNetX内部，我们选择了美国协作网络，该网络目前由56个HCO组成。MM患者使用ICD-10代码C90进行识别。0在2020年1月1日至2022年12月31日之间。从这个数据集中，我们确定了随后使用RxNorm、ICD-10-PCS和HCPCS代码接受CAR-T治疗的患者。这一分析反映了截至2023年6月19日实时收集的数据。通过TriNetX获得的患者人口统计数据包括年龄、性别、种族(白人、黑人或非裔美国人、亚洲人、美国印第安人或阿拉斯加原住民、夏威夷原住民或其他太平洋岛民，以及未知)和种族(不是西班牙裔、西班牙裔和未知)。连续变量(标准差均值，SD)和分类变量(比例)分别采用t检验和Z检验进行描述性分析。为了降低患者识别的风险，TriNetX在一个组中有10名或更少的患者，并且年龄上限为90岁时，将这个数字舍入为10。因此，对于种族和民族群体，其中群体规模有时是10或更少，我们显示了多数群体相对于所有其他群体总和的比例。在研究期间，有6164名患者被确认患有多发性骨髓瘤。平均年龄为68岁(21-90岁)；54%为男性。大多数(60%，N=3718)是白人，26%(N=1573)是黑人，6%(N=341)美国印第安人或阿拉斯加原住民，1%(N=111)亚洲人，以及≤10夏威夷原住民或其他人
Chimeric Antigen Receptor T-cell (CAR-T) therapy is a promising anti-multiple myeloma (MM) treatment option. Based on results from clinical trials of patients in relapsed/refractory MM [1, 2] two CAR-T therapies have been FDA-approved: idecabtagene vicleucel in 3/2021 and ciltacabtagene autoleucel in 2/2022. Evidence suggests that novel therapies often result in socioeconomic and racial disparities in access [3]. With the promise of superior MM control with CAR-T therapy, it is important to ensure equitable access to the treatment for all patients. A small but growing body of literature suggests racial disparities in the utilization of CAR-T [4, 5]. As of December 2022, Public Law 117-328 was enacted by the US Federal Drug Administration requiring clinical trials to include diversity action plans in order to ensure better representation of the national population. However, once a drug is approved, there are few, if any, mechanisms in place to enforce equitable access. We sought to explore differences in CAR-T therapy utilization by age, sex, race, and ethnicity using a large and representative dataset. Patients with MM were identified through TriNetX, an electronic healthcare record database, which combines real-time health records data from participating healthcare organizations (HCO) to generate longitudinal clinical data. Within TriNetX, we selected the United States collaborative network, which currently consists of 56 HCOs. Patients with MM were identified using the ICD-10 code C90. 0 between 1/1/2020 and 12/31/2022. From this dataset, we identified patients who subsequently received CAR-T therapy using RxNorm, ICD-10-PCS, and HCPCS codes. This analysis reflects data collected in real-time as of 6/19/2023. Patient demographics available through TriNetX included age, sex, race (White, Black or African American, Asian, American Indian or Alaska Native, and Native Hawaiian or Other Pacific Islander, and Unknown), and ethnicity (Not Hispanic, Hispanic, and Unknown). Descriptive analysis of continuous variables (mean with standard deviation, SD) and categorical variables (proportions) using t-test and Z-test, respectively was performed. To reduce the risk of patient identification, TriNetX rounds the number to 10 when there are 10 or fewer patients in a group and upper age is capped at 90 years. Thus, for race and ethnic groups, where the group size was sometimes 10 or fewer, we show the proportions for the majority versus all other groups combined. There were 6,164 patients identified as having MM during the study period. The mean age was 68 years (range, 21-90 years); 54% were male. The majority (60%, N= 3718) was White, 26%(N= 1573) were Black, 6%(N= 341) American Indian or Alaska Native, 1%(N= 111) Asians, and≤ 10 Native Hawaiian or Other