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      Mechanisms of ischaemia-induced arrhythmias in hypertrophic cardiomyopathy: a large-scale computational study

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          Abstract

          Aims

          Lethal arrhythmias in hypertrophic cardiomyopathy (HCM) are widely attributed to myocardial ischaemia and fibrosis. How these factors modulate arrhythmic risk remains largely unknown, especially as invasive mapping protocols are not routinely used in these patients. By leveraging multiscale digital twin technologies, we aim to investigate ischaemic mechanisms of increased arrhythmic risk in HCM.

          Methods and results

          Computational models of human HCM cardiomyocytes, tissue, and ventricles were used to simulate outcomes of Phase 1A acute myocardial ischaemia. Cellular response predictions were validated with patch-clamp studies of human HCM cardiomyocytes ( n = 12 cells, N = 5 patients). Ventricular simulations were informed by typical distributions of subendocardial/transmural ischaemia as analysed in perfusion scans ( N = 28 patients). S1-S2 pacing protocols were used to quantify arrhythmic risk for scenarios in which regions of septal obstructive hypertrophy were affected by (i) ischaemia, (ii) ischaemia and impaired repolarization, and (iii) ischaemia, impaired repolarization, and diffuse fibrosis. HCM cardiomyocytes exhibited enhanced action potential and abnormal effective refractory period shortening to ischaemic insults. Analysis of ∼75 000 re-entry induction cases revealed that the abnormal HCM cellular response enabled establishment of arrhythmia at milder ischaemia than otherwise possible in healthy myocardium, due to larger refractoriness gradients that promoted conduction block. Arrhythmias were more easily sustained in transmural than subendocardial ischaemia. Mechanisms of ischaemia–fibrosis interaction were strongly electrophysiology dependent. Fibrosis enabled asymmetric re-entry patterns and break-up into sustained ventricular tachycardia.

          Conclusion

          HCM ventricles exhibited an increased risk to non-sustained and sustained re-entry, largely dominated by an impaired cellular response and deleterious interactions with the diffuse fibrotic substrate.

          Graphical Abstract

          Graphical Abstract

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          Most cited references64

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          The ‘Digital Twin’ to enable the vision of precision cardiology

          Abstract Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.
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            2023 ESC Guidelines for the management of cardiomyopathies

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              Enhanced American College of Cardiology/American Heart Association Strategy for Prevention of Sudden Cardiac Death in High-Risk Patients With Hypertrophic Cardiomyopathy

              Is it possible to identify most patients with hypertrophic cardiomyopathy (HCM) at risk of arrhythmic sudden cardiac death (SCD) and to prevent such events with prophylactic implantable cardioverter/defibrillators (ICDs)? In this cohort study of 2094 patients with HCM, ICD decision making was prospectively assessed based on conventional major risk markers derived from the literature and enhanced from 2011 American College of Cardiology/American Heart Association guidelines over a 17-year experience at a single HCM center. Rates of appropriate ICD therapy terminating potentially lethal ventricular tachyarrhythmias exceeded SCDs in patients without ICDs by almost 50-fold. A prospective individual risk factor strategy predicted SCD events in nearly all at-risk patients with HCM, resulting in prophylactically implanted ICDs that prevented arrhythmic SCD events. This cohort study assesses the reliability of sudden cardiac death prediction methods leading to prophylactic implantable cardioverter/defibrillator recommendations to reduce the number of sudden cardiac deaths occurring in patients with hypertrophic cardiomyopathy. Strategies for reliable selection of high-risk patients with hypertrophic cardiomyopathy (HCM) for prevention of sudden cardiac death (SCD) with implantable cardioverter/defibrillators (ICDs) are incompletely resolved. To assess the reliability of SCD prediction methods leading to prophylactic ICD recommendations to reduce the number of SCDs occurring in patients with HCM. In this observational longitudinal study, 2094 predominantly adult patients with HCM consecutively evaluated over 17 years in a large HCM clinical center were studied. All patients underwent prospective ICD decision making relying on individual major risk markers derived from the HCM literature and an enhanced American College of Cardiology/American Heart Association (ACC/AHA) guidelines–based risk factor algorithm with complete clinical outcome follow-up. Data were collected from June 2017 to February 2018, and data were analyzed from February to July 2018. Arrhythmic SCD or appropriate ICD intervention for ventricular tachycardia or ventricular fibrillation. Of the 2094 study patients, 1313 (62.7%) were male, and the mean (SD) age was 51 (17) years. Of 527 patients with primary prevention ICDs implanted based on 1 or more major risk markers, 82 (15.6%) experienced device therapy–terminated ventricular tachycardia or ventricular fibrillation episodes, which exceeded the 5 HCM-related SCDs occurring among 1567 patients without ICDs (0.3%), including 2 who declined device therapy, by 49-fold (95% CI, 20-119; P  = .001). Cumulative 5-year probability of an appropriate ICD intervention was 10.5% (95% CI, 8.0-13.5). The enhanced ACC/AHA clinical risk factor strategy was highly sensitive for predicting SCD events (range, 87%-95%) but less specific for identifying patients without SCD events (78%). The C statistic calculated for enhanced ACC/AHA guidelines was 0.81 (95% CI, 0.77-0.85), demonstrating good discrimination between patients who did or did not experience an SCD event. Compared with enhanced ACC/AHA risk factors, the European Society of Cardiology risk score retrospectively applied to the study patients was much less sensitive than the ACC/AHA criteria (34% [95% CI, 22-44] vs 95% [95% CI, 89-99]), consistent with recognizing fewer high-risk patients. A systematic enhanced ACC/AHA guideline and practice-based risk factor strategy prospectively predicted SCD events in nearly all at-risk patients with HCM, resulting in prophylactically implanted ICDs that prevented many catastrophic arrhythmic events in this at-risk population.
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                Author and article information

                Contributors
                Journal
                Cardiovasc Res
                Cardiovasc Res
                cardiovascres
                Cardiovascular Research
                Oxford University Press (UK )
                0008-6363
                1755-3245
                May 2024
                22 April 2024
                22 April 2024
                : 120
                : 8
                : 914-926
                Affiliations
                Department of Computer Science, University of Oxford , Oxford, UK
                Department of Computer Science, University of Oxford , Oxford, UK
                Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford , Oxford, UK
                Department of NeuroFarBa, University of Florence , Florence, Italy
                Department of Computer Science, Federal University of São João del-Rei , São João del-Rei, Minas Gerais, Brazil
                Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford , Oxford, UK
                Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford , Oxford, UK
                Department of Computer Science, University of Oxford , Oxford, UK
                Author notes
                Corresponding author. Tel: +0044 1865 683575; fax: +0044 1865 273839, E-mail: alfonso.bueno@ 123456cs.ox.ac.uk

                Betty Raman and Alfonso Bueno-Orovio joint senior authors.

                Conflict of interest: none declared.

                Author information
                https://orcid.org/0000-0002-7695-2046
                https://orcid.org/0000-0003-4187-4970
                https://orcid.org/0000-0002-4260-7420
                https://orcid.org/0000-0002-4096-3173
                https://orcid.org/0000-0002-5287-9016
                https://orcid.org/0000-0002-1239-9608
                https://orcid.org/0000-0002-1634-3601
                Article
                cvae086
                10.1093/cvr/cvae086
                11218689
                38646743
                cdfee5ee-0d7e-466c-ba8a-2e75465e9a46
                © The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 19 October 2023
                : 31 January 2024
                : 17 March 2024
                : 06 May 2024
                Page count
                Pages: 13
                Funding
                Funded by: UK Engineering and Physical Sciences Research Council, DOI 10.13039/501100000266;
                Award ID: 2421745
                Funded by: British Heart Foundation, DOI 10.13039/501100000274;
                Award ID: FS/CRTF/21/24144
                Award ID: RE/18/3/34214
                Award ID: FS/17/22/32644
                Award ID: RE/13/1/30181
                Funded by: Swiss National Supercomputing Centre, Switzerland;
                Award ID: icp005
                Award ID: icp013
                Award ID: icp019
                Funded by: University of Oxford Advanced Research Computing;
                Categories
                Original Article
                AcademicSubjects/MED00200
                Eurheartj/8
                Eurheartj/1
                Eurheartj/7

                Cardiovascular Medicine
                hypertrophic cardiomyopathy,ischaemia,arrhythmic risk,modelling and simulation

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