Electrophysiological and anatomical factors determine arrhythmic risk in acute myocardial ischaemia and its modulation by sodium current availability

Cardiac arrhythmia following acute myocardial infarction: a retrospective analysis of 27,648 hospitalized patients in a tertiary heart hospital

Arrhythmia frequently complicates acute myocardial infarction (AMI) and contributes to high morbidity and mortality. We aimed to investigate the prevalence, risk factors, and impact of cardiac arrhythmias in AMI patients at a tertiary heart hospital. This retrospective observational study included AMI patients who were admitted between January 1991 and May 2022. Patients' data were analyzed and compared according to the absence or presence of cardiac arrhythmias post-AMI. We hypothesized that arrhythmias are associated with higher mortality following AMI. During the study, 27,648 patients were hospitalized with AMI, of whom 2118 (7.7%) developed arrhythmia. Patients who developed arrhythmia had a higher average age compared to those without arrhythmia (57.2 vs. 54.8 years, p=0.001), and a larger proportion were male compared to female patients (85.2% vs. 14.8%, p=0.001). Atrial fibrillation was observed in 383 patients (18.1%). Ventricular tachycardia was found in 461 (21.8%), and ventricular fibrillation occurred in 526 patients (24.8%). Complete heart block was developed in 286 (13.5%) patients, 1st-degree atrioventricular (AV) block in 36 (1.7%), 2nd-degree AV block in 138 (6.5%), left bundle branch block in 81 (3.8%), and right bundle branch block in 118 (5.6%). The rate of β-blocker use has increased in the arrhythmias group at discharge compared to the on-admission rate (55.7% vs. 32.5%). However, it remained sub-optimal. Arrhythmias were associated with longer hospital stays and five times higher hospital mortality than the non-arrhythmia group. Multivariable logistic regression analysis indicated that arrhythmia was associated with increased mortality risk three times following AMI (adjusted odds ratio 3.01; 95% confidence interval 2.42-3.75, p=0.001). Almost one-tenth of patients hospitalized with AMI in Qatar developed arrhythmia with variable outcomes; however, the in-hospital mortality remained high. Addressing the risk factors and optimizing the prevention and treatment of AMI and arrhythmias is crucial to improving clinical outcomes. This study may underestimate the incidence of arrhythmias post-AMI as it did not report all types.

Ventricular tachycardia substrate mapping: What's been done and what needs to be done

Substrate mapping is an important component of electrophysiological (EP) study for the treatment of reentrant ventricular tachycardia (VT). It is used to detect characteristics of the electrical circuit and, in particular, the location and properties of the central common pathway, aka the isthmus, where multiple circuit loops can coincide. Typically, reentrant circuits are single or double loop, but as the common pathway size increases, 4-loop patterns may emerge, consisting of 2 parallel isthmuses or a single isthmus with 4 loops. Arrhythmogenic substrate contains a mixture of scar, calcification, and fibrofatty regions blended with viable ventricular myocytes, which can slow conduction. It is identified in the EP laboratory in part by the presence of low-amplitude electrograms and a zone of uniform slow conduction resulting from a sparsity of remaining viable myocytes and molecular-level remodeling. The electrograms recorded near isthmus boundaries frequently exhibit an abnormal morphology, such as fractionation and late or split deflections, due to the separation of muscle fiber bundles by fibroadipose tissue or calcification, and due to other conduction impediments such as source-sink mismatch, wherein topographic changes to the viable myocardial structure occur. Substrate mapping facilitates the identification of arrhythmogenic regions during sinus rhythm, whereas inducible VT with periods of ongoing reentry, when recordable, can be used for further assessment. Substrate modeling augments substrate mapping by seeking to predict electrogram morphology and mapped features and properties to be encountered during EP study based on an accurate depiction of arrhythmogenic tissue. Herein, we elaborate on the details of VT substrate mapping and modeling to the present time.

Mechanisms and Implications of Electrical Heterogeneity in Cardiac Function in Ischemic Heart Disease

A healthy heart shows intrinsic electrical heterogeneities that play a significant role in cardiac activation and repolarization. However, cardiac diseases may perturb the baseline electrical properties of the healthy cardiac tissue, leading to increased arrhythmic risk and compromised cardiac functions. Moreover, biological variability among patients produces a wide range of clinical symptoms, which complicates the treatment and diagnosis of cardiac diseases. Ischemic heart disease is usually caused by a partial or complete blockage of a coronary artery. The onset of the disease begins with myocardial ischemia, which can develop into myocardial infarction if it persists for an extended period. The progressive regional tissue remodeling leads to increased electrical heterogeneities, with adverse consequences on arrhythmic risk, cardiac mechanics, and mortality. This review aims to summarize the key role of electrical heterogeneities in the heart on cardiac function and diseases. Ischemic heart disease has been chosen as an example to show how adverse electrical remodeling at different stages may lead to variable manifestations in patients. For this, we have reviewed the dynamic electrophysiological and structural remodeling from the onset of acute myocardial ischemia and reperfusion to acute and chronic stages post-myocardial infarction. The arrhythmic mechanisms, patient phenotypes, risk stratification at different stages, and patient management strategies are also discussed. Finally, we provide a brief review on how computational approaches incorporate human electrophysiological heterogeneity to facilitate basic and translational research.

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Risk stratification and predictive value of serum sodium fluctuation for adverse prognosis in acute coronary syndrome patients

BACKGROUND

Serum sodium fluctuation (SF) as an indicator of the extent of changes in serum sodium is associated with increased mortality in hospitalized patients. However, there is no consensus on diagnostic criteria for SF, and its impact on the outcome of patients with acute coronary syndrome (ACS) remains uncertain. We defined SF and assessed its association with adverse prognosis in hospitalized ACS patients.

METHODS

Patients diagnosed with ACS were consecutively recruited. The serum SF rate (SFR) was defined as the ratio of the difference between the highest and lowest serum sodium levels during hospitalization to the initial serum sodium level on admission. The Cox proportional hazards model was performed to evaluate the association between SFR and mortality. The dose-response relationships of SFR with mortality was characterized by restricted cubic splines (RCS) model. The predictive performance of SF for mortality was assessed by the area under the receiver operating characteristic curves (AUCs).

RESULTS

The study retrospectively enrolled 1856 ACS patients, of which 36 (1.94%) patients dead within 1 year. Multivariate Cox analysis showed that SFR was independently associated with higher risk of 1-year mortality (HR = 1.17, 95% CI: 1.111-1.244, P < 0.001). RCS analysis showed the optimal threshold for SFR was 5%, and the 1-year cumulative mortality was higher in the abnormal SF group (SFR ≥ 5%) compared with the normal SF group (SFR < 5%, P < 0.01). The AUCs of SF for predicting mortality within 1 month, 6 months, and 1 year were 0.842 (95% CI: 0.781-0.904), 0.830 (95% CI:0.736-0.926), 0.703 (95% CI:0.595--0.811), respectively. Even in patients with normal baseline serum sodium, abnormal SF group demonstrated a significantly higher 1-year mortality compared to normal SF group (HR = 4.955, 95% CI: 1.919-12.795).

CONCLUSION

The SFR during hospitalization is an adequate predictor of adverse outcomes in ACS patients, independent of serum sodium level at admission. Additional research is warranted to ascertain whether interventions targeting SF confer measurable clinical benefits.

Modelling the interaction between stem cells derived cardiomyocytes patches and host myocardium to aid non-arrhythmic engineered heart tissue design

Application of epicardial patches constructed from human-induced pluripotent stem cell- derived cardiomyocytes (hiPSC-CMs) has been proposed as a long-term therapy to treat scarred hearts post myocardial infarction (MI). Understanding electrical interaction between engineered heart tissue patches (EHT) and host myocardium represents a key step toward a successful patch engraftment. EHT retain different electrical properties with respect to the host heart tissue due to the hiPSC-CMs immature phenotype, which may lead to increased arrhythmia risk. We developed a modelling framework to examine the influence of patch design on electrical activation at the engraftment site. We performed an in silico investigation of different patch design approaches to restore pre-MI activation properties and evaluated the associated arrhythmic risk. We developed an in silico cardiac electrophysiology model of a transmural cross section of host myocardium. The model featured an infarct region, an epicardial patch spanning the infarct region and a bath region. The patch is modelled as a layer of hiPSC-CM, combined with a layer of conductive polymer (CP). Tissue and patch geometrical dimensions and conductivities were incorporated through 10 modifiable model parameters. We validated our model against 4 independent experimental studies and showed that it can qualitatively reproduce their findings. We performed a global sensitivity analysis (GSA) to isolate the most important parameters, showing that the stimulus propagation is mainly governed by the scar depth, radius and conductivity when the scar is not transmural, and by the EHT patch conductivity when the scar is transmural. We assessed the relevance of small animal studies to humans by comparing simulations of rat, rabbit and human myocardium. We found that stimulus propagation paths and GSA sensitivity indices are consistent across species. We explored which EHT design variables have the potential to restore physiological propagation. Simulations predict that increasing EHT conductivity from 0.28 to 1-1.1 S/m recovered physiological activation in rat, rabbit and human. Finally, we assessed arrhythmia risk related to increasing EHT conductivity and tested increasing the EHT Na+ channel density as an alternative strategy to match healthy activation. Our results revealed a greater arrhythmia risk linked to increased EHT conductivity compared to increased Na+ channel density. We demonstrated that our modeling framework could capture the interaction between host and EHT patches observed in in vitro experiments. We showed that large (patch and tissue dimensions) and small (cardiac myocyte electrophysiology) scale differences between small animals and humans do not alter EHT patch effect on infarcted tissue. Our model revealed that only when the scar is transmural do EHT properties impact activation times and isolated the EHT conductivity as the main parameter influencing propagation. We predicted that restoring physiological activation by tuning EHT conductivity is possible but may promote arrhythmic behavior. Finally, our model suggests that acting on hiPSC-CMs low action potential upstroke velocity and lack of IK1 may restore pre-MI activation while not promoting arrhythmia.

Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients

Background: Remodeling due to myocardial infarction (MI) significantly increases patient arrhythmic risk. Simulations using patient-specific models have shown promise in predicting personalized risk for arrhythmia. However, these are computationally- and time- intensive, hindering translation to clinical practice. Classical machine learning (ML) algorithms (such as K-nearest neighbors, Gaussian support vector machines, and decision trees) as well as neural network techniques, shown to increase prediction accuracy, can be used to predict occurrence of arrhythmia as predicted by simulations based solely on infarct and ventricular geometry. We present an initial combined image-based patient-specific in silico and machine learning methodology to assess risk for dangerous arrhythmia in post-infarct patients. Furthermore, we aim to demonstrate that simulation-supported data augmentation improves prediction models, combining patient data, computational simulation, and advanced statistical modeling, improving overall accuracy for arrhythmia risk assessment.

Methods: MRI-based computational models were constructed from 30 patients 5 days post-MI (the “baseline” population). In order to assess the utility biophysical model-supported data augmentation for improving arrhythmia prediction, we augmented the virtual baseline patient population. Each patient ventricular and ischemic geometry in the baseline population was used to create a subfamily of geometric models, resulting in an expanded set of patient models (the “augmented” population). Arrhythmia induction was attempted via programmed stimulation at 17 sites for each virtual patient corresponding to AHA LV segments and simulation outcome, “arrhythmia,” or “no-arrhythmia,” were used as ground truth for subsequent statistical prediction (machine learning, ML) models. For each patient geometric model, we measured and used choice data features: the myocardial volume and ischemic volume, as well as the segment-specific myocardial volume and ischemia percentage, as input to ML algorithms. For classical ML techniques (ML), we trained k-nearest neighbors, support vector machine, logistic regression, xgboost, and decision tree models to predict the simulation outcome from these geometric features alone. To explore neural network ML techniques, we trained both a three - and a four-hidden layer multilayer perceptron feed forward neural networks (NN), again predicting simulation outcomes from these geometric features alone. ML and NN models were trained on 70% of randomly selected segments and the remaining 30% was used for validation for both baseline and augmented populations.

Results: Stimulation in the baseline population (30 patient models) resulted in reentry in 21.8% of sites tested; in the augmented population (129 total patient models) reentry occurred in 13.0% of sites tested. ML and NN models ranged in mean accuracy from 0.83 to 0.86 for the baseline population, improving to 0.88 to 0.89 in all cases.

Conclusion: Machine learning techniques, combined with patient-specific, image-based computational simulations, can provide key clinical insights with high accuracy rapidly and efficiently. In the case of sparse or missing patient data, simulation-supported data augmentation can be employed to further improve predictive results for patient benefit. This work paves the way for using data-driven simulations for prediction of dangerous arrhythmia in MI patients.

Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications

Ventricular arrhythmias are the primary cause of sudden cardiac death and one of the leading causes of mortality worldwide. Whole-heart computational modeling offers a unique approach for studying ventricular arrhythmias, offering vast potential for developing both a mechanistic understanding of ventricular arrhythmias and clinical applications for treatment. In this review, the fundamentals of whole-heart ventricular modeling and current methods of personalizing models using clinical data are presented. From this foundation, the authors summarize recent advances in whole-heart ventricular arrhythmia modeling. Efforts in gaining mechanistic insights into ventricular arrhythmias are discussed, in addition to other applications of models such as the assessment of novel therapeutics. The review emphasizes the unique benefits of computational modeling that allow for insights that are not obtainable by contemporary experimental or clinical means. Additionally, the clinical impact of modeling is explored, demonstrating how patient care is influenced by the information gained from ventricular arrhythmia models. The authors conclude with future perspectives about the direction of whole-heart ventricular arrhythmia modeling, outlining how advances in neural network methodologies hold the potential to reduce computational expense and permit for efficient whole-heart modeling.

Inference of ventricular activation properties from non-invasive electrocardiography

The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients’ cardiac function to inform therapeutic and diagnostic decision-making. Both electrocardiography and imaging are used for the clinical diagnosis of cardiac disease. The integration of multi-modal datasets through advanced computational methods could enable the development of the cardiac ‘digital twin’, a comprehensive virtual tool that mechanistically reveals a patient's heart condition from clinical data and simulates treatment outcomes. The adoption of cardiac digital twins requires the non-invasive efficient personalisation of the electrophysiological properties in cardiac models. This study develops new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography, cardiac magnetic resonance (CMR) imaging and modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical CMR imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac ventricular myocardial-mass volumes ranging from 74 cm³ to 171 cm³ and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties in sinus rhythm from non-invasive epicardial activation time maps and ECG recordings, achieving higher accuracy for the endocardial speed and sheet (transmural) speed than for the fibre or sheet-normal directed speeds.

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Estimation of the ventricular speeds and earliest activation sites from ECG and CMR.

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Evaluation with twenty virtual subjects shows the effect of anatomical variability.

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Bayesian-inspired simultaneous estimation of continuous and discrete parameters.

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Efficient dynamic time warping-based comparison of electrocardiograms (ECG).

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Changing fibre and sheet-normal speed does not affect healthy activation sequence.