ArticleHeart rhythm O22022
Association of left ventricular tissue heterogeneity and intramyocardial fat on computed tomography with ventricular arrhythmias in ischemic cardiomyopathy.
Article in Heart rhythm O2, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01076660 (Left Ventricular Structural Predictors of Sudden Cardiac Death [Substudy of), which is not on this map. Cited by 5 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.
Left Ventricular Structural Predictors of Sudden Cardiac Death [Substudy of: Functional Energetics and Imaging for Phenotypic Characterization of Patients at Risk for Sudden Cardiac Death, See Also NCT000181233]
Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.Heart rhythm · 2025Review
- Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve.Current cardiology reports · 2024Review
- Article
- The Silent Player: Adipose Tissue's Connection with Ventricular Arrhythmias.JACC. Case reports · 2023Article
- Noninvasive Assessment of Lipomatous Metaplasia as a Substrate for Ventricular Tachycardia in Chronic Infarct.Circulation. Cardiovascular imaging · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Background: Gray zone, a measure of tissue heterogeneity on late gadolinium enhanced-cardiac magnetic resonance (LGE-CMR) imaging, has been shown to predict ventricular arrhythmias (VAs) in ischemic cardiomyopathy (ICM) patients. However, no studies have described whether left ventricular (LV) tissue heterogeneity and intramyocardial fat mass on contrast-enhanced computed tomography (CE-CT), which provides greater spatial resolution, is useful for assessing the risk of VAs in ICM patients with LV systolic dysfunction and no previous VAs. Objective: The purpose of this proof-of-concept study was to determine the feasibility of measuring global LV tissue heterogeneity and intramyocardial fat mass by CE-CT for predicting the risk of VAs in ICM patients with LV systolic dysfunction and no previous history of VAs. Methods: Patients with left ventricular ejection fraction ≤35% and no previous VAs were enrolled in a prospective, observational registry and underwent LGE-CMR. From this cohort, patients with ICM who additionally received CE-CT were included in the present analysis. Gray zone on LGE-CMR was defined as myocardium with signal intensity (SI) > peak SI of healthy myocardium but <50% maximal SI. Tissue heterogeneity on CE-CT was defined as the standard deviation of the Hounsfield unit image gradients (HU/mm) within the myocardium. Intramyocardial fat on CE-CT was identified as regions of image pixels between -180 and -5 HU. The primary outcome was VAs, defined as appropriate implantable cardioverter-defibrillator shock or sudden arrhythmic death. Results: The study consisted of 47 ICM patients, 13 (27.7%) of whom experienced VA events during mean follow-up of 5.6 ± 3.4 years. Increasing tissue heterogeneity (per HU/mm) was significantly associated with VAs after multivariable adjustment, including for gray zone (odds ratio [OR] 1.22; Conclusion: In ICM patients, CE-CT-derived LV tissue heterogeneity was independently associated with VAs and may represent a novel marker useful for risk stratification.
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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.