Dataset Information

Biological substrate modification suppresses ventricular arrhythmias in a porcine model of chronic ischaemic cardiomyopathy.

ABSTRACT:

Aims

Cardiomyopathy patients are prone to ventricular arrhythmias (VA) and sudden cardiac death. Current therapies to prevent VA include radiofrequency ablation to destroy slowly conducting pathways of viable myocardium which support re-entry. Here, we tested the reverse concept, namely that boosting local tissue viability in zones of slow conduction might eliminate slow conduction and suppress VA in ischaemic cardiomyopathy.

Methods and results

Exosomes are extracellular vesicles laden with bioactive cargo. Exosomes secreted by cardiosphere-derived cells (CDCEXO) reduce scar and improve heart function after intramyocardial delivery. In a VA-prone porcine model of ischaemic cardiomyopathy, we injected CDCEXO or vehicle into zones of delayed conduction defined by electroanatomic mapping. Up to 1-month post-injection, CDCEXO, but not the vehicle, decreased myocardial scar, suppressed slowly conducting electrical pathways, and inhibited VA induction by programmed electrical stimulation. In silico reconstruction of electrical activity based on magnetic resonance images accurately reproduced the suppression of VA inducibility by CDCEXO. Strong anti-fibrotic effects of CDCEXO, evident histologically and by proteomic analysis from pig hearts, were confirmed in a co-culture assay of cardiomyocytes and fibroblasts.

Conclusion

Biological substrate modification by exosome injection may be worth developing as a non-destructive alternative to conventional ablation for the prevention of recurrent ventricular tachyarrhythmias.

SUBMITTER: Dawkins JF

PROVIDER: S-EPMC9649918 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

Biological substrate modification suppresses ventricular arrhythmias in a porcine model of chronic ischaemic cardiomyopathy.

Dawkins James F JF Ehdaie Ashkan A Rogers Russell R Soetkamp Daniel D Valle Jackelyn J Holm Kevin K Sanchez Lizbeth L Tremmel Ileana I Nawaz Asma A Shehata Michael M Wang Xunzhang X Prakosa Adityo A Yu Joseph J Van Eyk Jennifer E JE Trayanova Natalia N Marbán Eduardo E Cingolani Eugenio E

European heart journal 20220601 22

<h4>Aims</h4>Cardiomyopathy patients are prone to ventricular arrhythmias (VA) and sudden cardiac death. Current therapies to prevent VA include radiofrequency ablation to destroy slowly conducting pathways of viable myocardium which support re-entry. Here, we tested the reverse concept, namely that boosting local tissue viability in zones of slow conduction might eliminate slow conduction and suppress VA in ischaemic cardiomyopathy.<h4>Methods and results</h4>Exosomes are extracellular vesicles ...[more]

PMID: 35262692

Similar Datasets

Project description:BackgroundTransition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-dimensional spatial complexity of grayscale patterns on late gadolinium enhanced cardiac magnetic resonance images to predict VAs in patients with ischemic cardiomyopathy.MethodsOne hundred twenty-two consecutive ischemic cardiomyopathy patients with left ventricular ejection fraction ≤35% without prior history of VAs underwent late gadolinium enhanced cardiac magnetic resonance images. From raw grayscale data, we generated graphs encoding the 3-dimensional geometry of the left ventricle. A novel technique, adapted to these graphs, assessed global regularity of signal intensity patterns using Fourier-like analysis and generated a substrate spatial complexity profile for each patient. A machine learning statistical algorithm was employed to discern which substrate spatial complexity profiles correlated with VA events (appropriate implantable cardioverter-defibrillator firings and arrhythmic sudden cardiac death) at 5 years of follow-up. From the statistical machine learning results, a complexity score ranging from 0 to 1 was calculated for each patient and tested using multivariable Cox regression models.ResultsAt 5 years of follow-up, 40 patients had VA events. The machine learning algorithm classified with 81% overall accuracy and correctly classified 86% of those without VAs. Overall negative predictive value was 91%. Average complexity score was significantly higher in patients with VA events versus those without (0.5±0.5 versus 0.1±0.2; P<0.0001) and was independently associated with VA events in a multivariable model (hazard ratio, 1.5 [1.2-2.0]; P=0.002).ConclusionsSubstrate spatial complexity analysis of late gadolinium enhanced cardiac magnetic resonance images may be helpful in refining VA risk in patients with ischemic cardiomyopathy, particularly to identify low-risk patients who may not benefit from prophylactic implantable cardioverter-defibrillator therapy. Visual Overview: A visual overview is available for this article.

Dataset Information

Biological substrate modification suppresses ventricular arrhythmias in a porcine model of chronic ischaemic cardiomyopathy.

Aims

Methods and results

Conclusion

Publications

Biological substrate modification suppresses ventricular arrhythmias in a porcine model of chronic ischaemic cardiomyopathy.

Similar Datasets

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