晚期肾细胞癌患者接受血管内皮生长因子治疗后的卡博替尼和阿昔替尼:一项来自英国的回顾性队列研究。

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2024-06-01 Epub Date: 2024-01-24 DOI:10.1007/s40801-023-00415-w
Janet Brown, Brooke Harrow, Anne Marciniak, Christine McCarthy, Aude Houchard, Lori Cirneanu, Andrew Protheroe
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引用次数: 0

摘要

背景和目的:在英国,酪氨酸激酶抑制剂卡博替尼(cabozantinib)和阿昔替尼(axitinib)已被广泛用于治疗既往接受过血管内皮生长因子靶向治疗的晚期肾细胞癌,但有关实际使用情况的数据仍然有限。我们的目的是描述英国临床实践中接受血管内皮生长因子靶向治疗后二线或更晚线(≥ 2L)卡博替尼或阿西替尼治疗的晚期肾细胞癌患者的实际治疗模式和结果:这项回顾性队列研究使用了英格兰癌症分析系统数据库中的临床实践数据(收集时间为 2011-20 年)。对患者特征、治疗顺序和持续时间以及总生存期(从开始卡博替尼/阿西替尼治疗到死亡的时间)进行了评估:分析了1485名符合条件的晚期肾细胞癌成人患者的数据:440人接受了≥2L卡博替尼(其中88.6%的患者接受了2L治疗);1045人接受了≥2L阿西替尼(其中89.5%的患者接受了2L治疗)。最常见的一线治疗是舒尼替尼(2L卡博替尼亚组,48%;2L阿西替尼亚组,46%)和帕唑帕尼(分别为46%和54%);尼伐单抗是最常见的三线治疗(分别为18%和19%)。卡博替尼的2L治疗时间中位数(四分位数间距)为5.52(2.73-11.74)个月,阿西替尼为4.60(1.45-12.36)个月。使用反概率加权法调整潜在混杂因素后,卡博替尼≥2L的总生存期(中位数[四分位距])(11.2 [5.7-28.0]个月)长于阿西替尼≥2L的总生存期(10.4 [4.7-22.0]个月;log-rank p = 0.0034):癌症分析系统数据库是一项宝贵的研究资源,提供了大量真实世界的临床数据。卡博替尼≥2L的实际总生存期长于阿西替尼:临床试验注册:ClinicalTrials.gov,NCT04637204;2020年11月注册。
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Cabozantinib and Axitinib After Vascular Endothelial Growth Factor Therapy in Patients with Advanced Renal Cell Carcinoma: A Retrospective Cohort Study from England.

Background and objective: The tyrosine kinase inhibitors cabozantinib and axitinib have been widely used in England to treat advanced renal cell carcinoma following prior vascular endothelial growth factor-targeted therapy, but data on real-world usage remain limited. Our objective was to describe the real-world treatment patterns and outcomes of patients with advanced renal cell carcinoma who received second-line or later-line (≥ 2L) cabozantinib or axitinib after vascular endothelial growth factor-targeted therapy in clinical practice in England.

Methods: This retrospective cohort study used clinical practice data (collected 2011-20) from the English Cancer Analysis System database. Patient characteristics, treatment sequence and duration, and overall survival (time from initiation of cabozantinib/axitinib treatment to death) were evaluated.

Results: Data from 1485 eligible adults with advanced renal cell carcinoma were analyzed: 440 received ≥  2L cabozantinib (2L for 88.6% of them); 1045 received ≥  2L axitinib (2L for 89.5%). The most common first-line treatments were sunitinib (2L cabozantinib subcohort, 48%; 2L axitinib subcohort, 46%) and pazopanib (46% and 54%, respectively); nivolumab was the most common third-line treatment (18% and 19%, respectively). Median (interquartile range) 2L therapy duration was 5.52 (2.73-11.74) months for cabozantinib and 4.60 (1.45-12.36) months for axitinib. Following adjustment for potential confounders using inverse probability weighting, overall survival (median [interquartile range]) was longer for ≥ 2L cabozantinib (11.2 [5.7-28.0] months) than for ≥  2L axitinib (10.4 [4.7-22.0] months; log-rank p = 0.0034).

Conclusions: The Cancer Analysis System database is a valuable research resource providing extensive real-world clinical data. Real-world overall survival was longer with ≥  2L cabozantinib than with axitinib.

Clinical trial registration: ClinicalTrials.gov, NCT04637204; registered November 2020.

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来源期刊
Drugs - Real World Outcomes
Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-
CiteScore
3.60
自引率
5.00%
发文量
49
审稿时长
8 weeks
期刊介绍: Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.
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