Lim Yvonne Mei Fong, Asselbergs Folkert W, Bagheri Ayoub, Denaxas Spiros, Tay Wan Ting, Voors Adriaan, Lam Carolyn Su Ping, Koudstaal Stefan, Grobbee Diederick E, Vaartjes Ilonca
Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Institute for Clinical Research, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Malaysia.
ESC Heart Fail. 2024 Dec;11(6):3559-3571. doi: 10.1002/ehf2.14751. Epub 2024 Jul 10.
Traditional approaches to designing clinical trials for heart failure (HF) have historically relied on expertise and past practices. However, the evolving landscape of healthcare, marked by the advent of novel data science applications and increased data availability, offers a compelling opportunity to transition towards a data-driven paradigm in trial design. This research aims to evaluate the scope and determinants of disparities between clinical trials and registries by leveraging natural language processing for the analysis of trial eligibility criteria. The findings contribute to the establishment of a robust design framework for guiding future HF trials.
Interventional phase III trials registered for HF on ClinicalTrials.gov as of the end of 2021 were identified. Natural language processing was used to extract and structure the eligibility criteria for quantitative analysis. The most common criteria for HF with reduced ejection fraction (HFrEF) were applied to estimate patient eligibility as a proportion of registry patients in the ASIAN-HF (N = 4868) and BIOSTAT-CHF registries (N = 2545). Of the 375 phase III trials for HF, 163 HFrEF trials were identified. In these trials, the most frequently encountered inclusion criteria were New York Heart Association (NYHA) functional class (69%), worsening HF (23%), and natriuretic peptides (18%), whereas the most frequent comorbidity-based exclusion criteria were acute coronary syndrome (64%), renal disease (55%), and valvular heart disease (47%). On average, 20% of registry patients were eligible for HFrEF trials. Eligibility distributions did not differ (P = 0.18) between Asian [median eligibility 0.20, interquartile range (IQR) 0.08-0.43] and European registry populations (median 0.17, IQR 0.06-0.39). With time, HFrEF trials became more restrictive, where patient eligibility declined from 0.40 in 1985-2005 to 0.19 in 2016-2022 (P = 0.03). When frequency among trials is taken into consideration, the eligibility criteria that were most restrictive were prior myocardial infarction, NYHA class, age, and prior HF hospitalization.
Based on 14 trial criteria, only one-fifth of registry patients were eligible for phase III HFrEF trials. Overall eligibility rates did not differ between the Asian and European patient cohorts.
心力衰竭(HF)临床试验的传统设计方法历来依赖专业知识和以往经验。然而,随着新型数据科学应用的出现和数据可得性的增加,医疗保健格局不断演变,为在试验设计中转向数据驱动范式提供了诱人契机。本研究旨在通过利用自然语言处理分析试验纳入标准,评估临床试验与注册登记之间差异的范围和决定因素。研究结果有助于建立一个强大的设计框架,以指导未来的HF试验。
确定截至2021年底在ClinicalTrials.gov上注册的HF干预性III期试验。使用自然语言处理提取并构建纳入标准,以进行定量分析。将射血分数降低的心力衰竭(HFrEF)最常见的标准应用于估计亚洲心力衰竭注册研究(N = 4868)和生物统计学-CHF注册研究(N = 2545)中符合试验条件的患者占注册患者的比例。在375项HF III期试验中,确定了163项HFrEF试验。在这些试验中,最常遇到的纳入标准是纽约心脏协会(NYHA)心功能分级(69%)、HF病情恶化(23%)和利钠肽(18%),而基于合并症最常见的排除标准是急性冠状动脉综合征(64%)、肾脏疾病(55%)和心脏瓣膜病(47%)。平均而言,20%的注册患者符合HFrEF试验条件。亚洲[中位符合率0.20,四分位间距(IQR)0.08 - 0.43]和欧洲注册人群(中位0.17,IQR 0.06 - 0.39)的符合率分布无差异(P = 0.18)。随着时间推移,HFrEF试验变得更加严格,患者符合率从1985 - 2005年的0.40降至2016 - 2022年的0.19(P = 0.03)。考虑到试验中的出现频率,最严格的纳入标准是既往心肌梗死、NYHA分级、年龄和既往HF住院史。
基于14项试验标准,只有五分之一的注册患者符合III期HFrEF试验条件。亚洲和欧洲患者队列的总体符合率无差异。