Characteristics of Patients with Hereditary Angioedema Who Reduced Lanadelumab Treatment Administration Frequency: A Retrospective Observational Study of US Claims Data.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2025-03-01 Epub Date: 2025-01-29 DOI:10.1007/s40801-024-00470-x
Nicole Princic, Kristin A Evans, Chintal H Shah, Krystal Sing, Salomé Juethner, Bob G Schultz
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Abstract

Background: Lanadelumab is the only long-term prophylaxis indicated for reduced administration frequency in patients with hereditary angioedema who have been well controlled for > 6 months. Understanding the characteristics of patients who reduce administration frequency will help identify populations where frequency modifications may be appropriate.

Objective: We aimed to describe characteristics of patients who did and did not reduce lanadelumab administration frequency to inform real-world dosing regimens, and characteristics indicative of sustained frequency reduction.

Methods: A retrospective observational study using healthcare insurance data in the USA from the Merative™ MarketScan® Commercial and Medicare Databases identified patients persistent on lanadelumab for ≥ 18 months. Reduced administration frequency was defined as a ≥ 25% decrease in lanadelumab costs during months 7-12 or 13-18 versus 0-6. Hereditary angioedema attack triggers/symptoms and hereditary angioedema-related healthcare encounters, treatment, and costs were assessed.

Results: Of 54 identified patients, 25 reduced administration frequency. Two patients returned to initial dosing frequency during months 13-18 after reducing during months 7-12. Patients who reduced administration frequency experienced fewer hereditary angioedema attack triggers/symptoms before lanadelumab initiation (baseline) and during months 0-6 than those who did not; they also had a lower mean number of hereditary angioedema-related inpatient admissions, emergency room visits, and outpatient visits during baseline, had fewer claims for acute treatment (60.0% vs 65.5%) and prior long-term prophylaxis (20.0% vs 27.6%), and had lower mean hereditary angioedema treatment costs at baseline ($139,520 vs $233,815) than those who did not.

Conclusions: This real-world analysis suggests that patients with less frequent hereditary angioedema-related healthcare encounters, lower disease activity, and lower costs within 6 months before lanadelumab initiation are more likely to achieve reduced dosing frequency.

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减少Lanadelumab给药频率的遗传性血管性水肿患者的特征:美国索赔数据的回顾性观察研究
背景:Lanadelumab是唯一一种用于减少遗传血管性水肿患者给药频率的长期预防药物,这些患者已控制好6个月。了解减少给药频率的患者的特征将有助于确定适当调整给药频率的人群。目的:我们旨在描述降低和未降低lanadelumab给药频率的患者的特征,以告知现实世界的给药方案,以及表明持续频率降低的特征。方法:一项回顾性观察性研究使用来自美国Merative™MarketScan®商业和医疗保险数据库的医疗保险数据,确定持续使用lanadelumab≥18个月的患者。减少给药频率定义为在7-12月或13-18月期间lanadelumab成本较0-6月降低≥25%。评估遗传性血管性水肿发作的诱因/症状和遗传性血管性水肿相关的医疗保健遭遇、治疗和费用。结果:54例确诊患者中,25例减少给药频率。2例患者在7-12个月减少剂量后,在13-18个月恢复到初始剂量频率。减少给药频率的患者在lanadelumab启动前(基线)和0-6个月期间的遗传性血管性水肿发作触发/症状少于未减少给药频率的患者;在基线期间,他们的遗传性血管性水肿相关住院、急诊室就诊和门诊就诊的平均数量也较低,对急性治疗的索赔(60.0%对65.5%)和之前的长期预防(20.0%对27.6%)较少,并且在基线时的遗传性血管性水肿平均治疗费用(139,520美元对233,815美元)低于未进行治疗的患者。结论:这项现实世界的分析表明,在lanadelumab开始治疗前6个月内,遗传性血管性水肿相关医疗就诊频率较低、疾病活动性较低、费用较低的患者更有可能降低给药频率。
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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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