Evidence map›Paper›PMID 42341290›Full record

ArticleJMIR formative research2026

Leveraging Self-Reporting in an Existing e-Cohort to Identify Clinically Relevant Mitral Valve Prolapse: Pilot Questionnaire Study.

Rohit Jhawar, Amy Rich, Luca Cristin, Lionel Tastet, Mark J Pletcher, Gregory M Marcus, Francesca N Delling

Abstract read
In one paragraph

Article in JMIR formative research, 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

7 authors.

Rohit JhawarDepartment of Medicine, Division of Cardiology, University of California San Francisco, 535 Mission Bay Boulevard South, San Francisco, CA, 94158, United States, 1 4155141162.ORCID 0009-0000-6021-6418
Amy RichDepartment of Medicine, Division of Cardiology, University of California San Francisco, 535 Mission Bay Boulevard South, San Francisco, CA, 94158, United States, 1 4155141162.ORCID 0009-0004-0109-5125
Luca CristinDepartment of Medicine, Division of Cardiology, University of California San Francisco, 535 Mission Bay Boulevard South, San Francisco, CA, 94158, United States, 1 4155141162.ORCID 0009-0003-2932-8659
Lionel TastetDepartment of Medicine, Division of Cardiology, University of California San Francisco, 535 Mission Bay Boulevard South, San Francisco, CA, 94158, United States, 1 4155141162.ORCID 0000-0002-6374-5398
Mark J PletcherDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States.ORCID 0000-0002-6966-1312
Gregory M MarcusDepartment of Medicine, Division of Cardiology, University of California San Francisco, 535 Mission Bay Boulevard South, San Francisco, CA, 94158, United States, 1 4155141162.ORCID 0000-0001-5197-7696
Francesca N DellingDepartment of Medicine, Division of Cardiology, University of California San Francisco, 535 Mission Bay Boulevard South, San Francisco, CA, 94158, United States, 1 4155141162.ORCID 0000-0002-5224-4096

Funding

Prospective sudden cardiac death risk stratification using CMR and echocardiography machine learning in mitral valve prolapseR01HL153447 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI DELLING, FRANCESCA N · 2020 to 2024
$4.7M
Sudden cardiac death in mitral valve prolapse: a genotype-phenotype pedigree studyR03HL145238 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI DELLING, FRANCESCA N · 2018 to 2019
$160k
NHLBI NIH HHS R01 HL153447NHLBI NIH HHS R03 HL145238
6 · The paper itself

Abstract

Background: Mitral valve prolapse (MVP) is a common valvulopathy associated, in a minority of cases, with heart failure, severe mitral regurgitation (MR), and sudden arrhythmic death. Digital tools hold promise for faster and more efficient recruitment of study participants into a large-scale MVP Registry. Objective: This study sought to evaluate the feasibility of surveying participants in an existing e-cohort to identify and clinically characterize MVP cases based on self-reporting and to recruit them in an MVP Registry at the University of California, San Francisco. Methods: We surveyed Northern Californian participants of the Health eHeart Study, a large e-cohort using the Eureka digital research infrastructure, about a prior diagnosis of MVP. MVP-positive respondents were asked to provide relevant medical records to confirm their eligibility and were invited to enroll in an MVP Registry if evidence of MVP was confirmed. A follow-up survey was sent after 1 month and after 5 years to collect data about clinical outcomes, including arrhythmias and the need for mitral valve repair. Results: The survey was delivered to 5746 participants, and 520 completed responses were collected. A prior diagnosis of MVP was self-reported by 16.3% (85/520) of respondents. Echocardiograms were obtained from 51.8% (44/85) of participants, and evidence of MVP was confirmed in 32.9% (n=28) of individuals, all of whom joined the registry. Participants with more severe MR had a higher number of correct responses regarding both MVP (odds ratio [OR] 10.58, 95% CI 3.58-63.04; P<.001) and MR diagnosis (OR 4.86, 95% CI 2.11-16.14; P=.002). Longitudinal data were available from most patients through responses to a follow-up survey sent 1 month and 5 years later (18/28, 64.3% and 17/28, 60.7% of MVP confirmed respondents, respectively). Among the patients with electronic health records available, 75% (3/4) had a correct self-reported diagnosis of arrhythmia. Conclusions: e-Cohort methods with self-reported clinical data can be used to prescreen candidates for a research study of MVP. These methods can rapidly identify and retain, among many cases of benign MVP, the minority with clinically relevant presentations such as significant MR and ventricular arrhythmias. These cases may be missed, especially when asymptomatic, by small-scale clinic-based recruitment or family screening methods.

Indexed as

Clinical RelevanceMass ScreeningMitral Valve ProlapsePatient SelectionSelf ReportSurveys and QuestionnairesAgedCaliforniaCohort StudiesEchocardiographyFeasibility StudiesFemaleFollow-Up StudiesHumansLongitudinal StudiesMalearrhythmiasdigital healthe-cohortrecruitmentvalvular heart disease

Identifiers

PMID42341290
PMCPMC13293562

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.