Background: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.Methods and Results: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.Conclusions: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.

中文翻译：

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一项全国性，多中心的实用临床试验的可计算表型实施：从ADAPTABLE中学到的经验教训。

背景：许多大规模的心血管临床试验都困扰着不断攀升的成本和较低的注册人数。实现可计算的表型是一组可执行的算法，以识别可从电子健康记录或行政要求记录中导出的一组临床特征，这对于在大规模实用临床试验中成功招募至关重要。该方法论文概述了ADAPTABLE中可计算表型的开发和实施（阿司匹林剂量：以患者为中心的评估益处和长期有效性）–一项实用，随机，开放标签的临床试验，用于测试最佳剂量阿司匹林用于动脉粥样硬化性心血管疾病事件的二级预防。方法与结果：一个多学科小组开发并测试了可计算的表型，以识别具有动脉粥样硬化性心血管疾病病史的18岁以上成年人，而无需担心使用阿司匹林和符合试验资格标准的安全性。使用可计算的表型，研究人员从以患者为中心的结果研究网络（由临床数据研究网络，由患者提供动力的研究网络和卫生计划研究网络组成的网络）的40个参与站点中识别出65万名潜在合格患者。从2016年4月到2019年6月，网站采用了多种招募方法，招募了15076名参与者。在开发和实施ADAPTABLE可计算表型的过程中，吸取了一些重要的经验教训。可计算表型的准确性和实用性取决于源数据的质量，即使使用通用数据模型，其质量也可能会有所不同。需要根据站点因素（例如，招聘策略，数据质量和本地编码模式）进行本地验证和修改。在可计算表型的开发和实施过程中，需要不同研究人员之间的持续合作。结论：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。开发和实施的过程将为将来的大规模，实用的临床试验提供信息。注册：URL：https://www.clinicaltrials.gov; 唯一标识符：NCT02697916。需要根据站点因素（例如，招聘策略，数据质量和本地编码模式）进行本地验证和修改。在可计算表型的开发和实施过程中，需要不同研究人员之间的持续合作。结论：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。开发和实施的过程将为将来的大规模，实用的临床试验提供信息。注册：URL：https://www.clinicaltrials.gov; 唯一标识符：NCT02697916。需要根据站点因素（例如，招聘策略，数据质量和本地编码模式）进行本地验证和修改。在可计算表型的开发和实施过程中，需要不同研究人员之间的持续合作。结论：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。开发和实施的过程将为将来的大规模，实用的临床试验提供信息。注册：URL：https://www.clinicaltrials.gov; 唯一标识符：NCT02697916。结论：ADAPTABLE可计算表型是一项在多地点实用临床试验中招募患者的有效方法。开发和实施的过程将为将来的大规模，实用的临床试验提供信息。注册：URL：https://www.clinicaltrials.gov; 唯一标识符：NCT02697916。结论：ADAPTABLE可计算表型是一项在多地点实用临床试验中招募患者的有效方法。开发和实施的过程将为将来的大规模，实用的临床试验提供信息。注册：URL：https://www.clinicaltrials.gov; 唯一标识符：NCT02697916。