Evidence map›Paper›PMID 32466729›Full record

ArticleCirculation. Cardiovascular quality and outcomes2020

Computable Phenotype Implementation for a National, Multicenter Pragmatic Clinical Trial: Lessons Learned From ADAPTABLE.

Faraz S Ahmad, Iben M Ricket, Bradley G Hammill, Lisa Eskenazi, Holly R Robertson, Lesley H Curtis, Cecilia D Dobi, Saket Girotra, Kevin Haynes, Jorge R Kizer and 6 more

Registry-linked trialOpen access · bronzeAbstract readClinical Trial Protocol
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
4.6field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

NCT02697916 nacompletednot on this map

Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-term Effectiveness

TypeinterventionalSponsorDuke UniversityRan2016 to 2020Enrolled15,076ConditionsAtherosclerotic Cardiovascular DiseaseArmsaspirin
3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 35 citations in OpenAlex.

  1. Trial
  2. Article
  3. PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  4. Article
  5. 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 · 2026
    Article
  6. Article
  7. Review
  8. Article
  9. 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 · 2025
    Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Observational
  19. Characterizing variability of electronic health record-driven phenotype definitions.Journal of the American Medical Informatics Association : JAMIA · 2023
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 11 institutions in 1 country.

Faraz S Ahmad *Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (F.S.A.).
Iben M Ricket *Louisiana Public Health Institute, New Orleans (I.M.R.).
Bradley G HammillDuke University School of Medicine, Durham, NC (B.G.H., M.T.R., W.S.J.).
Lisa EskenaziDuke Clinical Research Institute, Durham, NC (B.G.H., L.E., H.R., L.H.C., M.T.R., W.S.J.).
Holly R RobertsonDuke Clinical Research Institute, Durham, NC (B.G.H., L.E., H.R., L.H.C., M.T.R., W.S.J.).
Lesley H CurtisDuke Clinical Research Institute, Durham, NC (B.G.H., L.E., H.R., L.H.C., M.T.R., W.S.J.).
Cecilia D DobiDepartment of Clinical Sciences, Lewis Katz School of Medicine at Temple University, Philadelphia, PA (C.D.D.).
Saket GirotraUniversity of Iowa Carver College of Medicine, Iowa City (S.G.).
Kevin HaynesScientific Affairs, HealthCore, Inc., Wilmington, DE (K.H.).
Jorge R KizerCardiology Section, San Francisco Veterans Affairs Health Care System, CA (J.R.K.).
Sunil KripalaniDepartment of Medicine, Vanderbilt University Medical Center, Veterans Health Administration-Tennessee Valley Healthcare System Geriatric Research Education Clinical Center, Health Services Research and Development Center, Nashville, TN (S.K., C.L.R.).
Mathew T RoeDuke University School of Medicine, Durham, NC (B.G.H., M.T.R., W.S.J.).
Christianne L RoumieDepartment of Medicine, Vanderbilt University Medical Center, Veterans Health Administration-Tennessee Valley Healthcare System Geriatric Research Education Clinical Center, Health Services Research and Development Center, Nashville, TN (S.K., C.L.R.).
Russ WaitmanDepartment of Internal Medicine, University of Kansas Medical Center, Kansas City, KS (R.W.).
W Schuyler JonesDuke University School of Medicine, Durham, NC (B.G.H., M.T.R., W.S.J.).
Mark G WeinerDepartment of Population Health Sciences, Weill Cornell Medicine, New York Presbyterian-Weill Cornell Campus, New York (M.G.W.).
Clinical Research Institute · USDuke University · USVA Tennessee Valley Healthcare System · USCornell University · USLouisiana Public Health Institute · USNorthwestern University · USTemple University · USUniversity of California, San Francisco · USUniversity of Iowa · USUniversity of Kansas Medical Center · USWilmington University · US

Funding

Northwestern University Clinical and Translational Science Institute (NUCATS)UL1TR001422 · NCATS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI D'AQUILA, RICHARD · 2015 to 2023
$56.8M
PA-20-072: Supplement to A Chicago Center of Excellence in Learning Health Systems Research Training (ACCELERAT)K12HS026385 · AHRQ · NORTHWESTERN UNIVERSITY AT CHICAGO · PI FRANKLIN, PATRICIA D, KHO, ABEL N. · 2018 to 2022
$3.4M
AHRQ HHS K12 HS026385NCATS NIH HHS UL1 TR001422
6 · The paper itself

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.

Indexed as

AlgorithmsElectronic Health RecordsPatient SelectionAspirinCardiovascular DiseasesData MiningHumansMulticenter Studies as TopicPhenotypePlatelet Aggregation InhibitorsPragmatic Clinical Trials as TopicAspirinPlatelet Aggregation Inhibitorsaspirinelectronic health recordsheart diseasepatient selectionpragmatic clinical trial

Identifiers

PMID32466729
PMCPMC7321832
OpenAlexW3032794980

What OpenQuestion holds

Textmetadata
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Read underepoch 390

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.