Evidence map›Paper›PMID 36183522›Full record

ArticleJournal of electrocardiology

Arrhythmia in hypertrophic cardiomyopathy: Risk prediction using contrast enhanced MRI, T1 mapping, and personalized virtual heart technology.

Ryan P O'Hara, Adityo Prakosa, Edem Binka, Audrey Lacy, Natalia A Trayanova

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of electrocardiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 12% 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.

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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

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  7. State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025
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  15. Motion-compensated TFrontiers in cardiovascular medicine · 2023
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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

5 authors at 1 institution in 1 country.

Ryan P O'HaraDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America.
Adityo PrakosaDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America.
Edem BinkaDivision of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD 21205, United States of America.
Audrey LacyDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America.
Natalia A TrayanovaDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America. Electronic address: ntrayanova@jhu.edu.
Johns Hopkins University · US

Funding

Infarct-related Ventricular Tachycardia Mechanisms: From Micro to ClinicalR01HL142496 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI TRAYANOVA, NATALIA A. · 2019 to 2022
$3.1M
NHLBI NIH HHS R01 HL142496
6 · The paper itself

Abstract

backgroundHypertrophic cardiomyopathy (HCM), a disease with myocardial fibrosis manifestation, is a common cause of sudden cardiac death (SCD) due to ventricular arrhythmias (VA). Current clinical risk stratification criteria are inadequate in identifying patients who are at risk for VA and in need of an implantable cardioverter defibrillator (ICD) for primary prevention.

objectiveWe aimed to develop a risk prediction approach based on imaging biomarkers from the combination of late gadolinium contrast-enhanced (LGE) MRI and T1 mapping. We then aimed to compare the prediction to a virtual heart computational risk assessment approach based on LGE-T1 virtual heart models.

methodsThe methodology involved combining short-axis LGE-MRI with post-contrast T1 maps to define personalized thresholds for diffuse and dense fibrosis. The combined LGE-T1 maps were used to evaluate imaging biomarkers for VA risk prediction. The risk prediction capability of the biomarkers was compared with that of the LGE-T1 virtual heart arrhythmia inducibility simulation. VA risk prediction performance from both approaches was compared to clinical outcome (presence of clinical VA).

resultsImage-based biomarkers, including hypertrophy, signal intensity heterogeneity, and fibrotic border complexity, could not discriminate high vs low VA risk. LGE-T1 virtual heart technology outperformed all the image-based biomarker metrics and was statistically significant in predicting VA risk in HCM.

conclusionsWe combined two MR imaging techniques to analyze imaging biomarkers in HCM. Raw and processed image-based biomarkers cannot discriminate patients with VA from those without VA. Hybrid LGE-T1 virtual heart models could correctly predict VA risk for this cohort and may improve SCD risk stratification to better identify HCM patients for primary preventative ICD implantation.

Indexed as

Cardiomyopathy, HypertrophicElectrocardiographyHumansMagnetic Resonance ImagingTechnologyArrhythmogenesisDiffuse fibrosisDigital twinHypertrophic cardiomyopathy

Identifiers

PMID36183522
PMCPMC9729380
OpenAlexW4296181751

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.