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Computable Phenotype Implementation for a National, Multicenter Pragmatic Clinical Trial: Lessons Learned From
Faraz S Ahmad1, Iben M Ricket2, Bradley G Hammill3,4
1Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (F.S.A.).
Insights
Implementing a computable phenotype in large clinical trials like ADAPTABLE efficiently identifies eligible patients for cardiovascular research, overcoming enrollment challenges.
Area of Science:
- Cardiovascular Clinical Trials
- Health Informatics
- Clinical Trial Recruitment
Background:
- Large-scale cardiovascular trials face high costs and low patient enrollment.
- Computable phenotypes, using algorithms on electronic health records, are crucial for efficient recruitment.
- This paper details the computable phenotype development for the ADAPTABLE trial.
Purpose of the Study:
- To describe the development and implementation of a computable phenotype for the ADAPTABLE trial.
- To assess the efficiency of computable phenotypes in recruiting for pragmatic clinical trials.
- To identify key lessons learned during the computable phenotype development and implementation process.
Main Methods:
- A computable phenotype was developed to identify adults meeting ADAPTABLE trial eligibility criteria.
- The phenotype identified over 650,000 potentially eligible patients across 40 sites.
- 15,076 participants were enrolled between April 2016 and June 2019.
Main Results:
- The computable phenotype successfully identified a large cohort of eligible patients.
- Patient enrollment in the ADAPTABLE trial reached 15,076 participants.
- Variability in source data quality and local coding patterns necessitated site-specific validation and modification.
Conclusions:
- The ADAPTABLE computable phenotype proved an efficient recruitment tool for a multisite pragmatic trial.
- The development and implementation process offers valuable insights for future large-scale trials.
- Sustained multidisciplinary collaboration is essential for successful computable phenotype implementation.
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.
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