ArticleCirculation. Cardiovascular quality and outcomes2020
Computable Phenotype Implementation for a National, Multicenter Pragmatic Clinical Trial: Lessons Learned From ADAPTABLE.
Article in Circulation. Cardiovascular quality and outcomes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02697916 (Aspirin Dosing), which is not on this map. Cited by 28 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-term Effectiveness
Who cites it
28 citing papers in PubMed, 35 citations in OpenAlex.
- Comparative Effectiveness of Aspirin Dosing in Cardiovascular Disease.The New England journal of medicine · 2021Trial
- Leveraging PCORnet® to Advance Clinical Genetics and the Genomic Learning Health System.Medical care · 2026Article
- PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Accelerating novel therapies for sickle cell disease with RWE: ASH RC Data Hub as a strategically coordinated registry network.Blood advances · 2026Article
- A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Anticoagulation and other risk factors for clinically relevant bleeding after medical hospitalization: the Medical Inpatient Thrombosis and Hemostasis Study.Journal of thrombosis and haemostasis : JTH · 2025Article
- Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management.Hypertension (Dallas, Tex. : 1979) · 2025Review
- Development and validation of computable social phenotypes for health-related social needs.JAMIA open · 2025Article
- Enhancing Cross-Domain Generalizability in Social Determinants of Health Extraction with Prompt-Tuning Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Using electronic health records for clinical pharmacology research: Challenges and considerations.Clinical and translational science · 2024Review
- Ascertaining accurate exposure to aspirin and other antithrombotic medications using structured electronic health record data.Research and practice in thrombosis and haemostasis · 2024Article
- Identifying and Extracting Rare Diseases and Their Phenotypes with Large Language Models.Journal of healthcare informatics research · 2024Article
- Using ICD9/10 codes for identifying ADPKD patients, a validation study.Journal of nephrology · 2024Article
- A flexible symbolic regression method for constructing interpretable clinical prediction models.NPJ digital medicine · 2023Article
- Development of a computable phenotype using electronic health records for venous thromboembolism in medical inpatients: the Medical Inpatient Thrombosis and Hemostasis study.Research and practice in thrombosis and haemostasis · 2023Article
- Refining Clinical Phenotypes to Improve Clinical Decision Support and Reduce Alert Fatigue: A Feasibility Study.Applied clinical informatics · 2023Article
- Trends and opportunities in computable clinical phenotyping: A scoping review.Journal of biomedical informatics · 2023Article
- The relative risk of bleeding after medical hospitalization: the medical inpatient thrombosis and hemorrhage study.Journal of thrombosis and haemostasis : JTH · 2023Observational
- Characterizing variability of electronic health record-driven phenotype definitions.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Comparison of Sodium-Glucose Cotransporter-2 Inhibitor and Glucagon-Like Peptide-1 Receptor Agonist Prescribing in Patients With Diabetes Mellitus With and Without Cardiovascular Disease.The American journal of cardiology · 2023Article
Corrections and comments
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Authors and funding
16 authors at 11 institutions in 1 country.
Funding
Abstract
backgroundMany 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
resultsA 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.
conclusionsThe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.