Computable Phenotype Implementation for a National, Multicenter Pragmatic Clinical Trial

Abstract

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.

An Informatics Consult approach for generating clinical evidence for treatment decisions

“…Table 1 shows additional opportunities for pipelining each of the four streams of evidence identification and generation to return the Consult report. Computable EHR phenotypes and computable clinical trial protocols can be used to automate the process of trial identification and trial recruitment[25,26]. For meta-analyses of observational studies, approaches for semi-automated systematic reviews[27,28], batch extraction of data from articles[29,30] and mapping of SNOMED-CT terms to MeSH descriptors in PubMed[31] can be used in the pipelining process.…”

Characterizing Variability of EHR-Driven Phenotype Definitions

“…While often developed and executed at a single institution, research networks such as the National Patient-Centered Clinical Research Network (PCORnet),[Fleurence2014] the electronic Medical Records and Genomics (eMERGE) Network, [McCarty2011, Gottesman2013, Zouk2019] and the Observational Health Data Sciences and Informatics (OHDSI) program [Hripcsak2015] have run distributed studies to pool their results to improve statistical power and cohort diversity, leveraging shared phenotype definitions to meet these goals. [Ahmad2020, Hripcsak2019, Burn2020]…”

Innovation in the design of large-scale hybrid randomized clinical trials

“…For example, an initial set of ICD-9 and ICD-10 codes used by the ADAPTABLE trial to identify patients with established atherosclerotic CVD did not adequately capture the study population of interest. By working with participating health systems, investigators were able to create computer algorithms termed "computable phenotypes" to identify their population of interest more appropriately [23].…”