Evidence map›Paper›PMID 42746540›Full record

ArticleJournal of cardiac failure - intersections2026

Recruitment Strategies in Registry-Based Observational Research: A Single-Center Experience in the United States from the HeartShare Study.

Vaishnavi Krishnan, Ramzi B Kibbi, Quan Mai, Elizabeth Marquez, Lili Zhao, Nicole Cyrille-Superville, Gregory D Lewis, Faraz S Ahmad, Neela Thangada, Sadiya S Khan and 1 more

Abstract read
In one paragraph

Article in Journal of cardiac failure - intersections, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Vaishnavi KrishnanDepartment of Medicine, Cardiology Division, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Ramzi B KibbiDepartment of Medicine, Cardiology Division, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Quan MaiDepartment of Preventive Medicine; Northwestern University, Chicago, Illinois.
Elizabeth MarquezDepartment of Preventive Medicine; Northwestern University, Chicago, Illinois.
Lili ZhaoDepartment of Preventive Medicine; Northwestern University, Chicago, Illinois.
Nicole Cyrille-SupervilleSanger Heart and Vascular Institute, Atrium Health-Wake Forest, Charlotte, North Carolina.
Gregory D LewisDivision of Cardiology, Massachusetts General Hospital, Boston, Massachusetts.
Faraz S AhmadDepartment of Medicine, Cardiology Division, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Neela ThangadaDepartment of Medicine, Cardiology Division, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Sadiya S KhanDepartment of Medicine, Cardiology Division, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Laura J Rasmussen-TorvikDepartment of Preventive Medicine; Northwestern University, Chicago, Illinois.

Funding

HeartShare DeCODE-HF: Data translation center to Combine Omics, Deep phenotyping, and Electronic health records for Heart Failure subtypes and treatment targetsU54HL160273 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Abel N. Kho, Yuan Luo · 2021 to 2026
$19.0M
Wake Forest Atrium HeartShare Clinical CenterU01HL160272 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI DALANE W KITZMAN · 2021 to 2026
$2.3M
A prospective multiethnic HFpEF cohort from Californias Central ValleyU01HL160274 · NHLBI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Martin Cadeiras, Nipavan Chiamvimonvat · 2021 to 2026
$2.3M
Mass General Brigham HeartShare Clinical CenterU01HL160278 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Akshay Suvas Desai, Michael M Givertz · 2021 to 2026
$1.8M
CHIcago Center for Accelerating nextGen Omics, deep phenotyping, and data science in Heart Failure (CHICAGO-HF)U01HL160279 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Sadiya Sana Khan, Laura J Rasmussen-Torvik · 2021 to 2026
$1.8M
Mayo Clinic HeartShare Clinical CenterU01HL160226 · NHLBI · MAYO CLINIC ROCHESTER · PI Barry A. Borlaug, Margaret M Redfield · 2021 to 2026
$1.8M
HeartShare: Next-Generation Phenomics to Define Heart Failure Subtypes and Treatment Targets - Clinical CentersU01HL160277 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI JULIO ALONSO CHIRINOS MEDINA · 2021 to 2026
$1.7M
NHLBI NIH HHS U01 HL160226NHLBI NIH HHS U01 HL160272NHLBI NIH HHS U01 HL160274NHLBI NIH HHS U01 HL160277NHLBI NIH HHS U01 HL160278NHLBI NIH HHS U01 HL160279NHLBI NIH HHS U54 HL160273
6 · The paper itself

Abstract

Background: Recruiting a representative sample of US adults is challenging, and strategies to improve recruitment have not been well evaluated. This study examined differences in consent rates across 3 recruitment approaches for the HeartShare registry at a single academic center. Methods: The HeartShare registry is a multicenter observational cohort enrolling adults age ≥30 years with or without heart failure. We included participants contacted at a single academic center between August 1, 2023 and August 1, 2024, comparing consent rates across 3 recruitment methods: (1) telephone; (2) electronic health record (EHR) messaging; or (3) in-person. Secondary analyses evaluated differences in consent rates within recruitment strategies by sociodemographic factors. Results: Among 9712 patients contacted (mean age, 72.0; standard deviation [SD] 13.1 years; 46.5% female), 4.2% consented. Of the 1439 patients contacted by telephone, 175 (12.1%) consented. Of the 8080 patients contacted through EHR messaging, 144 (1.8%) consented. Of the 193 contacted in-person, 79 (41%) consented. Compared with phone recruitment, EHR had the lowest consent rate (adjusted odds ratio [aOR], 0.13; 95% confidence interval [CI], 0.10-0.17) while in-person contact had the highest consent rate (aOR, 6.49; 95% CI, 4.42-9.53). In-person recruitment showed no significant differences in consent rates between Black and White participants (aOR, 0.69; 95% CI, 0.30-1.56). Among those approached via EHR or phone, Black individuals had significantly lower odds of consenting compared with White individuals (EHR: aOR, 0.31; 95% CI, 0.12-0.67; Phone: aOR, 0.36; 95% CI, 0.18-0.67). Conclusions: In-person recruitment achieved the highest consent rates across groups in this observational registry-based cohort. Although less time intensive, automated EHR recruitment was less effective in engaging underrepresented groups.

Indexed as

health equityHeart failurestudy recruitment

Identifiers

PMID42746540
PMCPMC13576520

What OpenQuestion holds

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Registered trials

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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.