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Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis.

ABSTRACT: Cardiac fibrosis and other scarring of the heart, arising from conditions ranging from myocardial infarction to ageing, promotes dangerous arrhythmias by blocking the healthy propagation of cardiac excitation. Owing to the complexity of the dynamics of electrical signalling in the heart, however, the connection between different arrangements of blockage and various arrhythmic consequences remains poorly understood. Where a mechanism defies traditional understanding, machine learning can be invaluable for enabling accurate prediction of quantities of interest (measures of arrhythmic risk) in terms of predictor variables (such as the arrangement or pattern of obstructive scarring). In this study, we simulate the propagation of the action potential (AP) in tissue affected by fibrotic changes and hence detect sites that initiate re-entrant activation patterns. By separately considering multiple different stimulus regimes, we directly observe and quantify the sensitivity of re-entry formation to activation sequence in the fibrotic region. Then, by extracting the fibrotic structures around locations that both do and do not initiate re-entries, we use neural networks to determine to what extent re-entry initiation is predictable, and over what spatial scale conduction heterogeneities appear to act to produce this effect. We find that structural information within about 0.5 mm of a given point is sufficient to predict structures that initiate re-entry with more than 90% accuracy.

SUBMITTER: Halfar R

PROVIDER: S-EPMC8415115 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:AimsTreatment of arrhythmias evoked by hypothermia/rewarming remains challenging, and the underlying mechanisms are unclear. This in vitro experimental study assessed cardiac electrophysiology in isolated rabbit hearts at temperatures occurring in therapeutic and accidental hypothermia.Methods and resultsDetailed ECG, surface electrogram, and panoramic optical mapping were performed in isolated rabbit hearts cooled to moderate (31°C) and severe (17°C) hypothermia. Ventricular activation was unchanged at 31°C while action potential duration (APD) was significantly prolonged (176.9 ± 4.2 ms vs. 241.0 ± 2.9 ms, P < 0.05), as was ventricular repolarization. At 17°C, there were proportionally similar delays in both activation and repolarization. These changes were reflected in the QRS and QT intervals of ECG recordings. Ventricular fibrillation threshold was significantly reduced at 31°C (16.3 ± 3.1 vs. 35 ± 3.5 mA, P < 0.05) but increased at 17°C (64.2 ± 9.9, P < 0.05). At 31°C, transverse conduction was relatively unchanged by cooling compared to longitudinal conduction, but at 17°C both transverse and longitudinal conduction were proportionately reduced to a similar extent. The gap junction uncoupler heptanol had a larger relative effect on transverse than longitudinal conduction and was able to restore the transverse/longitudinal conduction ratio, returning ventricular fibrillation threshold to baseline values (16.3 ± 3.1 vs. 36.3 ± 4.3 mA, P < 0.05) at 31°C. Rewarming to 37°C restored the majority of the electrophysiological parameters.ConclusionsModerate hypothermia does not significantly change ventricular conduction time but prolongs repolarization and is pro-arrhythmic. Further cooling to severe hypothermia causes parallel changes in ventricular activation and repolarization, changes which are anti-arrhythmic. Therefore, relative changes in QRS and QT intervals (QR/QTc) emerge as an ECG-biomarker of pro-arrhythmic activity. Risk for ventricular fibrillation appears to be linked to the relatively low temperature sensitivity of ventricular transmural conduction, a conclusion supported by the anti-arrhythmic effect of heptanol at 31°C.

Project description:Aims: Ventricular arrhythmia triggers sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM), yet electrophysiological biomarkers are not used for risk stratification. Our aim was to identify distinct HCM phenotypes based on ECG computational analysis, and characterize differences in clinical risk factors and anatomical differences using cardiac magnetic resonance (CMR) imaging. Methods: High-fidelity 12-lead Holter ECGs from 85 HCM patients and 38 healthy volunteers were analyzed using mathematical modeling and computational clustering to identify phenotypic subgroups. Clinical features and the extent and distribution of hypertrophy assessed by CMR were evaluated in the subgroups. Results: QRS morphology alone was crucial to identify three HCM phenotypes with very distinct QRS patterns. Group 1 (n = 44) showed normal QRS morphology, Group 2 (n = 19) showed short R and deep S waves in V4, and Group 3 (n = 22) exhibited short R and long S waves in V4-6, and left QRS axis deviation. However, no differences in arrhythmic risk or distribution of hypertrophy were observed between these groups. Including T wave biomarkers in the clustering, four HCM phenotypes were identified: Group 1A (n = 20), with primary repolarization abnormalities showing normal QRS yet inverted T waves, Group 1B (n = 24), with normal QRS morphology and upright T waves, and Group 2 and Group 3 remaining as before, with upright T waves. Group 1A patients, with normal QRS and inverted T wave, showed increased HCM Risk-SCD scores (1A: 4.0%, 1B: 1.8%, 2: 2.1%, 3: 2.5%, p = 0.0001), and a predominance of coexisting septal and apical hypertrophy (p < 0.0001). HCM patients in Groups 2 and 3 exhibited predominantly septal hypertrophy (85 and 90%, respectively). Conclusion: HCM patients were classified in four subgroups with distinct ECG features. Patients with primary T wave inversion not secondary to QRS abnormalities had increased HCM Risk-SCD scores and coexisting septal and apical hypertrophy, suggesting that primary T wave inversion may increase SCD risk in HCM, rather than T wave inversion secondary to depolarization abnormalities. Computational ECG phenotyping provides insight into the underlying processes captured by the ECG and has the potential to be a novel and independent factor for risk stratification.

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Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis.

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