Electrical Substrates Driving Response to Cardiac Resynchronization Therapy

Computational Modelling Enabling In Silico Trials for Cardiac Physiologic Pacing

Conduction system pacing (CSP) has the potential to achieve physiological-paced activation by pacing the ventricular conduction system. Before CSP is adopted in standard clinical practice, large, randomised, and multi-centre trials are required to investigate CSP safety and efficacy compared to standard biventricular pacing (BVP). Furthermore, there are unanswered questions about pacing thresholds required to achieve optimal pacing delivery while preventing device battery draining, and about which patient groups are more likely to benefit from CSP rather than BVP. In silico studies have been increasingly used to investigate mechanisms underlying changes in cardiac function in response to pathologies and treatment. In the context of CSP, they have been used to improve our understanding of conduction system capture to optimise CSP delivery and battery life, and noninvasively compare different pacing methods on different patient groups. In this review, we discuss the in silico studies published to date investigating different aspects of CSP delivery.

Current Role of Electrocardiographic Imaging in Patient Selection for Cardiac Resynchronization Therapy

Cardiac resynchronization therapy (CRT) is a recognized therapy for heart failure with altered ejection fraction and abnormal left ventricular activation time. Since the introduction of the therapy, a 30% rate of non-responders is observed and unchanged. The 12-lead ECG remains the only recommended tool for patient selection to CRT. The 12-lead ECG is, however, limited in its inability to provide a precise pattern of regional electrical activity. Electrocardiographic imaging (ECGi) provides a non-invasive detailed mapping of cardiac activation and therefore appears as a promising tool for CRT candidates. The non-invasive ventricular activation maps acquired by ECGi have been primarily explored for the diagnosis and guidance of therapy in patients with atrial or ventricular tachyarrhythmia. However, the accuracy of the system in this field is lacking and needs further improvement before considering a clinical application. On the other hand, its use for patient selection for CRT is encouraging. In this review, we introduce the technical considerations and we describe how ECGi can precisely characterize ventricular activation, especially in patients with left bundle branch block, thus identifying the electrical substrate responsive to CRT.

A systematic review of cardiac in-silico clinical trials

Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012-1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In 75% of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in 19% of ISCTs. The specific software used was not reported in 14% of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with 28% of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only 19% of the studies. In 97% of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.

What Body Surface Mapping Has Taught Us About Ventricular Conduction Disease Implications for Cardiac Resynchronization Therapy and His Bundle Pacing

The degree and pattern of conduction disease seem determinant when assessing potential cardiac resynchronization therapy (CRT) candidates. In the present review, the authors discuss the available noninvasive techniques that can be used to acquire ventricular activation time maps. They describe what body surface mapping has taught us about left bundle branch block, right bundle branch block, intraventricular conduction delay, and right ventricular pacing and discuss the ability of derived parameters of electrical dyssynchrony to predict long-term clinical response to CRT or His bundle pacing.

Intracardiac conduction time as a predictor of cardiac resynchronization therapy response: Results of the BIO|SELECT pilot study

Background

Quadripolar left ventricular (LV) leads are capable of sensing and pacing the left ventricle from 4 different electrodes, which may potentially improve patient response to cardiac resynchronization therapy (CRT).

Objective

We measured 3 different time intervals: right ventricular (RV)-sensed to LV-sensed during intrinsic rhythm (RVs-LVs), RV-paced to LV-sensed (RVp-LVs), and LV-paced to LV-sensed (LVp-LVs, between distal [LV1] and proximal pole on a quadripolar LV lead), and assessed their association with CRT response in terms of LV end-systolic volume (LVESV) and a composite benefit index (CBI) comprising LVESV, LV ejection fraction (LVEF), brain natriuretic peptide level, and NYHA class.

Methods

A CRT-defibrillator system with quadripolar LV lead was implanted in 196 patients (mean age 69 years, mean LVEF 30%, left bundle-branch block [LBBB] 58%). Conduction intervals were measured before hospital discharge. At baseline and 7-month follow-up, echocardiographic and other components of CBI were determined.

Results

The mean RVs-LV1s, RVp-LV1s, and LVp-LVs delays were 68 ± 38 ms, 132 ± 34 ms, and 99 ± 31 ms, respectively. From baseline to 7 months, LVESV decreased by 17.3% ± 28.6%. The RVs-LV1s interval correlated stronger with CBI (R² = 0.12, P < .00001) than with LVESV change (R² = 0.05, P = .006). In contrast, RVp-LV1s did not correlate and LVp-LVs correlated only weakly with CRT response. The subgroup of patients (44%) with LBBB and RVs-LV1s above the lower quartile (≥34 ms) showed the greatest response to CRT.

Conclusion

The RVs-LVs interval during intrinsic rhythm is relevant for CRT success, whereas RVp-LVs and LVp-LVs intervals did not predict CRT response.

Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data

Background: Up to 30–50% of chronic heart failure patients who underwent cardiac resynchronization therapy (CRT) do not respond to the treatment. Therefore, patient stratification for CRT and optimization of CRT device settings remain a challenge.

Objective: The main goal of our study is to develop a predictive model of CRT outcome using a combination of clinical data recorded in patients before CRT and simulations of the response to biventricular (BiV) pacing in personalized computational models of the cardiac electrophysiology.

Materials and Methods: Retrospective data from 57 patients who underwent CRT device implantation was utilized. Positive response to CRT was defined by a 10% increase in the left ventricular ejection fraction in a year after implantation. For each patient, an anatomical model of the heart and torso was reconstructed from MRI and CT images and tailored to ECG recorded in the participant. The models were used to compute ventricular activation time, ECG duration and electrical dyssynchrony indices during intrinsic rhythm and BiV pacing from the sites of implanted leads. For building a predictive model of CRT response, we used clinical data recorded before CRT device implantation together with model-derived biomarkers of ventricular excitation in the left bundle branch block mode of activation and under BiV stimulation. Several Machine Learning (ML) classifiers and feature selection algorithms were tested on the hybrid dataset, and the quality of predictors was assessed using the area under receiver operating curve (ROC AUC). The classifiers on the hybrid data were compared with ML models built on clinical data only.

Results: The best ML classifier utilizing a hybrid set of clinical and model-driven data demonstrated ROC AUC of 0.82, an accuracy of 0.82, sensitivity of 0.85, and specificity of 0.78, improving quality over that of ML predictors built on clinical data from much larger datasets by more than 0.1. Distance from the LV pacing site to the post-infarction zone and ventricular activation characteristics under BiV pacing were shown as the most relevant model-driven features for CRT response classification.

Conclusion: Our results suggest that combination of clinical and model-driven data increases the accuracy of classification models for CRT outcomes.

Comparison of electrical dyssynchrony parameters between electrocardiographic imaging and a simulated ECG belt

AIMS

Electrocardiographic imaging (ECGi) and the ECG belt are body surface potential mapping systems which can assess electrical dyssynchrony in patients undergoing cardiac resynchronization therapy (CRT). ECGi-derived dyssynchrony metrics are calculated from reconstructed epicardial potentials based on body surface potentials combined with a thoracic CT scan, while the ECG belt relies on body surface potentials alone. The relationship between dyssynchrony metrics from these two systems is unknown. In this study we aim to compare intra-ventricular and inter-ventricular dyssynchrony metrics between ECGi and the ECG belt.

METHODS

Seventeen patients underwent ECGi after CRT. A subsample of 40 body surface potentials was used to simulate the ECG belt. ECGi dyssynchrony metrics, calculated from reconstructed epicardial potentials, and ECG belt dyssynchrony metrics, calculated from the sampled body surface potentials were compared.

RESULTS

There was a strong positive correlation between ECGi left ventricular activation time (LVAT) and ECG belt left thorax activation time (LTAT) (R = 0.88 ; P < 0.001) and between ECGi standard deviation of activation times (SDAT) and ECG belt-SDAT (R = 0.76; P < 0.001) during intrinsic rhythm. The correlation for both pairs was also strong during biventricular pacing. Ventricular electrical uncoupling, a well validated ECGi inter-ventricular dyssynchrony metric, correlated strongly with ECG belt-SDAT during intrinsic rhythm (R = 0.76; P < 0.001) but not biventricular pacing (R = 0.29; P = 0.26). Cranial or caudal displacement of the simulated ECG belt did not affect LTAT or SDAT.

CONCLUSION

ECGi- and ECG belt-derived intra-ventricular and inter-ventricular dyssynchrony metrics were strongly correlated. The ECG belt may offer comparable dyssynchrony assessment to ECGi, with associated practical and cost advantages.

Adaptive Cardiac Resynchronization Therapy Effect on Electrical Dyssynchrony (aCRT-ELSYNC): A randomized controlled trial

BACKGROUND

Adaptive cardiac resynchronization therapy (aCRT) is known to have clinical benefits over conventional CRT, but the mechanisms are unclear.

OBJECTIVE

Compare effects of aCRT and conventional CRT on electrical dyssynchrony.

METHODS

A prospective, double-blind, 1:1 parallel-group assignment randomized controlled trial in patients receiving CRT for routine clinical indications. Participants underwent cardiac computed tomography and 128-electrode body surface mapping. The primary outcome was change in electrical dyssynchrony measured on the epicardial surface using noninvasive electrocardiographic imaging before and 6 months post-CRT. Ventricular electrical uncoupling (VEU) was calculated as the difference between the mean left ventricular (LV) and right ventricular (RV) activation times. An electrical dyssynchrony index (EDI) was computed as the standard deviation of local epicardial activation times.

RESULTS

We randomized 27 participants (aged 64 ± 12 years; 34% female; 53% ischemic cardiomyopathy; LV ejection fraction 28% ± 8%; QRS duration 155 ± 21 ms; typical left bundle branch block [LBBB] in 13%) to conventional CRT (n = 15) vs aCRT (n = 12). In atypical LBBB (n = 11; 41%) with S waves in V5-V6, conduction block occurred in the anterior RV, as opposed to the interventricular groove in strict LBBB. As compared to baseline, VEU reduced post-CRT in the aCRT (median reduction 18.9 [interquartile range 4.3-29.2 ms; P = .034]), but not in the conventional CRT (21.4 [-30.0 to 49.9 ms; P = .525]) group. There were no differences in the degree of change in VEU and EDI indices between treatment groups.

CONCLUSION

The effect of aCRT and conventional CRT on electrical dyssynchrony is largely similar, but only aCRT harmoniously reduced interventricular dyssynchrony by reducing RV uncoupling.

Cardiac Inflammation Impedes Response to Cardiac Resynchronization Therapy in Patients With Idiopathic Dilated Cardiomyopathy

BACKGROUND

Cardiac resynchronization therapy (CRT) is an established therapy in patients with dilated cardiomyopathy (DCM) and conduction disorders. Still, one-third of the patients with DCM do not respond to CRT. This study aims to depict the underlying cardiac pathophysiological processes of nonresponse to CRT in patients with DCM using endomyocardial biopsies.

METHODS

Within the Maastricht and Innsbruck registries of patients with DCM, 99 patients underwent endomyocardial biopsies before CRT implantation, with histological quantification of fibrosis and inflammation, where inflammation was defined as >14 infiltrating cells/mm². Echocardiographic left ventricular end-systolic volume reduction ≥15% after 6 months was defined as response to CRT. RNA was isolated from cardiac biopsies of a representative subset of responders and nonresponders.

RESULTS

Sixty-seven patients responded (68%), whereas 32 (32%) did not respond to CRT. Cardiac inflammation before implantation was negatively associated with response to CRT (25% of responders, 47% of nonresponders; odds ratio 0.3 [0.12-0.76]; P=0.01). Endomyocardial biopsies fibrosis did not relate to CRT response. Cardiac inflammation improved the robustness of prediction beyond well-known clinical predictors of CRT response (likelihood ratio test P<0.001). Cardiac transcriptomic profiling of endomyocardial biopsies reveals a strong proinflammatory and profibrotic signature in the hearts of nonresponders compared with responders. In particular, COL1A1, COL1A2, COL3A1, COL5A1, POSTN, CTGF, LOX, TGFβ1, PDGFRA, TNC, BGN, and TSP2 were significantly higher expressed in the hearts of nonresponders.

CONCLUSIONS

Cardiac inflammation along with a transcriptomic profile of high expression of combined proinflammatory and profibrotic genes are associated with a poor response to CRT in patients with DCM.

Electrocardiographic optimization techniques in resynchronization therapy

Cardiac resynchronization therapy (CRT) is a cornerstone of therapy for patients with heart failure, reduced left ventricular (LV) ejection fraction, and a wide QRS complex. However, not all patients respond to CRT: 30% of CRT implanted patients are currently considered clinical non-responders and up to 40% do not achieve LV reverse remodelling. In order to achieve the best CRT response, appropriate patient selection, device implantation, and programming are important factors. Optimization of CRT pacing intervals may improve results, increasing the number of responders, and the magnitude of the response. Echocardiography is considered the reference method for atrioventricular and interventricular (VV) intervals optimization but it is time-consuming, complex and it has a large interobserver and intraobserver variability. Previous studies have linked QRS shortening to clinical response, echocardiographic improvement and favourable prognosis. In this review, we describe the electrocardiographic optimization methods available: 12-lead electrocardiogram; fusion-optimized intervals (FOI); intracardiac electrogram-based algorithms; and electrocardiographic imaging. Fusion-optimized intervals is an electrocardiographic method of optimizing CRT based on QRS duration that combines fusion with intrinsic conduction. The FOI method is feasible and fast, further reduces QRS duration, can be performed during implant, improves acute haemodynamic response, and achieves greater LV remodelling compared with nominal programming of CRT.

Effects of Heart Rate and Ventricular Wall Thickness on Non-invasive Mapping: An in silico Study

Background: Non-invasive cardiac mapping—also known as Electrocardiographic imaging (ECGi)—is a novel, painless and relatively economic method to map the electrical activation and repolarization patterns of the heart, providing a valuable tool for early identification and diagnosis of conduction abnormalities and arrhythmias. Moreover, the ability to obtain information on cardiac electrical activity non-invasively using ECGi provides the potential for a priori information to guide invasive surgical procedures, improving success rates, and reducing procedure time.

Previous studies have shown the influence of clinical variables, such as heart rate, heart size, endocardial wall, and body composition on surface electrocardiogram (ECG) measurements. The influence of clinical variables on the ECG variability has provided information on cardiovascular control and its abnormalities in various pathologies. However, the effects of such clinical variables on the Body Surface Potential (BSP) and ECGi maps have yet to be systematically investigated.

Methods: In this study we investigated the effects of heart size, intracardiac thickness, and heart rate on BSP and ECGi maps using a previously-developed 3D electrophysiologically-detailed ventricles-torso model. The inverse solution was solved using the three different Tikhonov regularization methods.

Results: Through comparison of multiple measures of error/accuracy on the ECGi reconstructions, our results showed that using different heart geometries to solve the forward and inverse problems produced a larger estimated focal excitation location. An increase of ~2 mm in the Euclidean distance error was observed for an increase in the heart size. However, the estimation of the location of focal activity was still able to be obtained. Similarly, a Euclidean distance increase was observed when the order of regularization was reduced.

For the case of activation maps reconstructed at the same ectopic focus location but different heart rates, an increase in the errors and Euclidean distance was observed when the heart rate was increased.

Conclusions: Non-invasive cardiac mapping can still provide useful information about cardiac activation patterns for the cases when a different geometry is used for the inverse problem compared to the one used for the forward solution; rapid pacing rates can induce order-dependent errors in the accuracy of reconstruction.

Non-invasive cardiac mapping for non-response in cardiac resynchronization therapy

Cardiac resynchronization therapy (CRT) is an effective intervention in selected patients with moderate-to-severe heart failure with reduced ejection fraction and abnormal left ventricular activation time. The non-response rate of approximately 30% has remained nearly unchanged since this therapy was introduced 25 years ago. While intracardiac mapping is widely used for diagnosis and guidance of therapy in patients with tachyarrhythmia, its application in characterization of the electrical substrate to elucidate the mechanisms involved in CRT response remain anecdotal. In the present review, we describe the traditional determinants of CRT response before presenting novel non-invasive techniques used for CRT optimization. We discuss efforts to identify the target electrical substrate to guide the deployment of pacing electrodes during the operative procedure. Non-invasive body surface mapping technologies such as ECG imaging or ECG belt enables prediction of acute and chronic CRT response. While electrical dyssynchrony parameters provide high predictive accuracy for CRT response when obtained during intrinsic conduction, their predictive value is less when acquired during CRT or LV-pacing. Key messages Classic predictors of CRT response are female gender, NYHA class ≤ III, left ventricular ejection fraction ≥25%, QRS duration ≥150 ms and estimated glomerular filtration rate ≥60 mL/min. ECG-imaging is a comprehensive non-invasive mapping system which allows to express the amount of electrical asynchrony of a CRT candidate. Non-invasive body surface mapping technologies enables excellent prediction of acute and chronic CRT response before implantation. When performed during CRT or LV-pacing, the added value of these mapping systems remains unclear.

Benefits of Multisite/Multipoint Pacing to Improve Cardiac Resynchronization Therapy Response

This article provides a general overview of the underlying mechanisms that support pacing from more discrete points and/or a wider vector (multisite and multipoint pacing) to improve left ventricular resynchronization. We performed a critical overview of the current literature and to identify some remaining knowledge gaps to spur further research. It was not our goal to provide a systematic review with a comprehensive bibliography, but rather to focus on selected publications that, in our opinion, have either expertly reviewed a specific aspect of cardiac resynchronization therapy or have been landmark studies in the field.

Computational models in cardiology

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.

The Left and Right Ventricles Respond Differently to Variation of Pacing Delays in Cardiac Resynchronization Therapy: A Combined Experimental- Computational Approach

Introduction: Timing of atrial, right (RV), and left ventricular (LV) stimulation in cardiac resynchronization therapy (CRT) is known to affect electrical activation and pump function of the LV. In this study, we used computer simulations, with input from animal experiments, to investigate the effect of varying pacing delays on both LV and RV electrical dyssynchrony and contractile function.

Methods: A pacing protocol was performed in dogs with atrioventricular block (N = 6), using 100 different combinations of atrial (A)-LV and A-RV pacing delays. Regional LV and RV electrical activation times were measured using 112 electrodes and LV and RV pressures were measured with catheter-tip micromanometers. Contractile response to a pacing delay was defined as relative change of the maximum rate of LV and RV pressure rise (dP/dt_max) compared to RV pacing with an A-RV delay of 125 ms. The pacing protocol was simulated in the CircAdapt model of cardiovascular system dynamics, using the experimentally acquired electrical mapping data as input.

Results: Ventricular electrical activation changed with changes in the amount of LV or RV pre-excitation. The resulting changes in dP/dt_max differed markedly between the LV and RV. Pacing the LV 10–50 ms before the RV led to the largest increases in LV dP/dt_max. In contrast, RV dP/dt_max was highest with RV pre-excitation and decreased up to 33% with LV pre-excitation. These opposite patterns of changes in RV and LV dP/dt_max were reproduced by the simulations. The simulations extended these observations by showing that changes in steady-state biventricular cardiac output differed from changes in both LV and RV dP/dt_max. The model allowed to explain the discrepant changes in dP/dt_max and cardiac output by coupling between atria and ventricles as well as between the ventricles.

Conclusion: The LV and the RV respond in a opposite manner to variation in the amount of LV or RV pre-excitation. Computer simulations capture LV and RV behavior during pacing delay variation and may be used in the design of new CRT optimization studies.