Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (F.S.A.).
Louisiana Public Health Institute, New Orleans (I.M.R.).
Circ Cardiovasc Qual Outcomes. 2020 Jun;13(6):e006292. doi: 10.1161/CIRCOUTCOMES.119.006292. Epub 2020 May 29.
Many large-scale cardiovascular clinical trials are plagued with escalating costs and low enrollment. Implementing a computable phenotype, which is a set of executable algorithms, to identify a group of clinical characteristics derivable from electronic health records or administrative claims records, is essential to successful recruitment in large-scale pragmatic clinical trials. This methods paper provides an overview of the development and implementation of a computable phenotype in ADAPTABLE (Aspirin Dosing: a Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness)-a pragmatic, randomized, open-label clinical trial testing the optimal dose of aspirin for secondary prevention of atherosclerotic cardiovascular disease events.
A multidisciplinary team developed and tested the computable phenotype to identify adults ≥18 years of age with a history of atherosclerotic cardiovascular disease without safety concerns around using aspirin and meeting trial eligibility criteria. Using the computable phenotype, investigators identified over 650 000 potentially eligible patients from the 40 participating sites from Patient-Centered Outcomes Research Network-a network of Clinical Data Research Networks, Patient-Powered Research Networks, and Health Plan Research Networks. Leveraging diverse recruitment methods, sites enrolled 15 076 participants from April 2016 to June 2019. During the process of developing and implementing the ADAPTABLE computable phenotype, several key lessons were learned. The accuracy and utility of a computable phenotype are dependent on the quality of the source data, which can be variable even with a common data model. Local validation and modification were required based on site factors, such as recruitment strategies, data quality, and local coding patterns. Sustained collaboration among a diverse team of researchers is needed during computable phenotype development and implementation.
The ADAPTABLE computable phenotype served as an efficient method to recruit patients in a multisite pragmatic clinical trial. This process of development and implementation will be informative for future large-scale, pragmatic clinical trials. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02697916.
许多大规模的心血管临床试验都面临着成本不断攀升和参与率低的问题。实施可计算表型（一种可执行算法的集合）对于成功招募大规模实用临床试验至关重要，该表型可从电子健康记录或行政索赔记录中识别出一组临床特征。本文提供了一个方法概述，介绍了在 ADAPTABLE（阿司匹林剂量：评估获益和长期有效性的患者为中心试验）-一项实用、随机、开放标签临床试验中开发和实施可计算表型的方法，该试验旨在测试阿司匹林用于二级预防动脉粥样硬化性心血管疾病事件的最佳剂量。
一个多学科团队开发并测试了可计算表型，以识别出年龄≥18 岁、有动脉粥样硬化性心血管病史且无使用阿司匹林安全性问题且符合试验入组标准的成年人。使用可计算表型，研究人员从 40 个参与地点的患者为中心结局研究网络（一个临床数据研究网络、患者驱动的研究网络和健康计划研究网络的网络）中确定了超过 650,000 名潜在合格患者。利用各种招募方法，各地点于 2016 年 4 月至 2019 年 6 月间共招募了 15,076 名参与者。在开发和实施 ADAPTABLE 可计算表型的过程中，我们学到了一些关键经验。可计算表型的准确性和实用性取决于源数据的质量，即使使用共同的数据模型，源数据的质量也可能存在差异。基于招募策略、数据质量和本地编码模式等因素，需要对本地数据进行验证和修改。在可计算表型的开发和实施过程中，需要一个多样化的研究人员团队之间的持续合作。
ADAPTABLE 可计算表型是在多地点实用临床试验中招募患者的有效方法。这一开发和实施过程将为未来的大规模实用临床试验提供信息。注册：网址：https://www.clinicaltrials.gov；唯一标识符：NCT02697916。
