Abstract Aims To determine whether triventricular (TriV) pacing is feasible and improves CRT response compared to conventional biventricular (BiV) pacing in patients with left bundle branch block (LBBB) and intermediate QRS prolongation (120–150 ms). Methods and results Between October 2015 and November 2019, 99 patients were recruited from 11 UK centres. Ninety-five patients were randomized 1:1 to receive TriV or BiV pacing systems. The primary endpoint was feasibility of TriV pacing. Secondary endpoints assessed symptomatic and remodelling response to CRT. Baseline characteristics were balanced between groups. In the TriV group, 43/46 (93.5%) patients underwent successful implantation vs. 47/49 (95.9%) in the BiV group. Feasibility of maintaining CRT at 6 months was similar in the TriV vs. BiV group (90.0% vs. 97.7%, P = 0.191). All-cause mortality was similar between TriV vs. BiV groups (4.3% vs. 8.2%, P = 0.678). There were no significant differences in echocardiographic LV volumes or clinical composite scores from baseline to 6-month follow-up between groups. Conclusion Implantation of two LV leads to deliver and maintain TriV pacing at 6 months is feasible without significant complications in the majority of patients. There was no evidence that TriV pacing improves CRT response or provides additional clinical benefit to patients with LBBB and intermediate QRS prolongation and cannot be recommended in this patient group. Clinical trial registration number Clinicaltrials.gov: NCT02529410.

BACKGROUND No periprocedural metric has demonstrated improved cardiac resynchronization therapy (CRT) outcomes in a multicenter setting. OBJECTIVE We sought to determine if left ventricular (LV) lead placement targeted to the coronary sinus (CS) branch generating the best acute hemodynamic response (AHR) results in improved outcomes at 6 months. METHODS In this multicenter randomized controlled trial, patients were randomized to guided CRT or conventional CRT. Patients in the guided arm had LV dP/dtmax measured during biventricular (BIV) pacing. Target CS branches were identified and the final LV lead position was the branch with the best AHR and acceptable threshold values. The primary endpoint was the proportion of patients with a reduction in LV end-systolic volume (LVESV) of >= 15% at 6 months. RESULTS A total of 281 patients were recruited across 12 centers. Mean age was 70.8 +/- 10.9 years and 54% had ischemic etiology. Seventy-three percent of patients in the guided arm demonstrated a reduction in LVESV of >= 15% at 6 months vs 60% in the conventional arm (P = .02). Patients with AHR >= 10% were more likely to demonstrate a reduction of ESV >= 15% (84% of patients with an AHR >= 10% vs 28% with an AHR < 10%; P < 0.001). Procedure duration and fluoroscopy times were longer in the pressure wire-guided arm (104 +/- 39 minutes vs 142 +/- 39 minutes; P <.001 and 20 +/- 16 minutes vs 28 +/- 15 minutes; P = .002). CONCLUSIONS AHR determined by invasively measuring LV dP/ dtmax during BIV pacing predicts reverse remodeling 6 months after CRT. Patients in whom LV dP/dtmax was used to guide LV lead placement demonstrated better rates of reverse remodeling.

Background: Cardiac resynchronization therapy (CRT) produces acute changes in electric resynchronization that can be measured noninvasively with electrocardiographic body surface mapping (ECGi). The relation between baseline acute electrophysiology metrics and their manipulation with CRT and reverse remodeling is unclear. Objective: To test (ECGi) derived parameters of electrical activation as predictors of volumetric response to CRT. Methods: ECGi was performed in 21 patients directly following CRT implant. Activation parameters (left ventricular total activation time [LVtat], global biventricular total activation time [VVtat], global left/right ventricular electrical synchrony [VVsync], and global left ventricular dispersion of activation times [LVdisp]) were measured at baseline and following echocardiographically optimized CRT. Remodeling response (>15% reduction left ventricular end-systolic volume) was assessed 6 months post CRT. Results: Patients were aged 68.9 ± 12.1 years, 81% were male, and 57% were ischemic. Baseline measures of dyssynchrony were more pronounced in left bundle branch block (LBBB) vs non-LBBB. ECGi demonstrated a trend of greater interventricular dyssynchrony between responders and nonresponders that did not reach statistical significance (VVsync: -45.7 ± 22.4 ms vs -25.1 ± 29.3 ms, P = .227). Remaining activation parameters were similar between responders and nonresponders (VVtat 101 ± 22.0 ms vs 98.9 ± 23.4 ms, P = .838; LVtat 86.4 ± 17.1 ms vs 85.1 ± 27.7 ms, P = .904; LVdisp 28.2 ± 6.3 ms vs 27.0 ± 8.7 ms, P = .726). In volumetric responders activation parameters were significantly improved with CRT compared to nonresponders: VV sync (-45.67 ± 22.41 ms vs 2.33±18.87 ms, P = .001), VVtat (101 ± 22.04 ms vs 71 ± 14.01 ms, P = .002), LVtat (86.44 ± 17.15 ms vs 67.67 ± 11.31 ms, P = .006), and LVdisp (28.22 ± 6.3 ms vs 21.56 ± 4.45 ms, P = .008). Conclusion: Baseline ECGi activation times did not predict CRT volumetric response. Volumetric responders exhibited significant improvements in ECGi-derived metrics with CRT. ECGi does not select CRT candidates but may be a useful adjunct to guide left ventricle lead implants and to perform postimplant CRT optimization.

Goal: Noninvasive cardiac electrophysiology (EP) model personalisation has raised interest for instance in the scope of predicting EP cardiac resynchronization therapy (CRT) response. However, the restricted clinical applicability of current methods is due in particular to the limitation to simple situations and the important computational cost. Methods: We propose in this manuscript an approach to tackle these two issues. First, we analyze more complex propagation patterns (multiple onsets and scar tissue) using relevance vector regression and shape dimensionality reduction on a large simulated database. Second, this learning is performed offline on a reference anatomy and transferred onto patient-specific anatomies in order to achieve fast personalized predictions online. Results: We evaluated our method on a dataset composed of 20 dyssynchrony patients with a total of 120 different cardiac cycles. The comparison with a commercially available electrocardiographic imaging (ECGI) method shows a good identification of the cardiac activation pattern. From the cardiac parameters estimated in sinus rhythm, we predicted five different paced patterns for each patient. The comparison with the body surface potential mappings (BSPM) measured during pacing and the ECGI method indicates a good predictive power. Conclusion: We showed that learning offline from a large simulated database on a reference anatomy was able to capture the main cardiac EP characteristics from noninvasive measurements for fast patient-specific predictions. Significance: The fast CRT pacing predictions are a step forward to a noninvasive CRT patient selection and therapy optimisation, to help clinicians in these difficult tasks.

Introduction: The role of implantable cardioverter defibrillators (ICDs) in nonischemic cardiomyopathy is unclear and better risk-stratification is required. We sought to determine if T1 mapping predicts appropriate defibrillator therapy in patients with nonischemic cardiomyopathy. We studied a mixed cohort of ischemic and nonischemic patients to determine whether different cardiac magnetic resonance (CMR) applications (T1 mapping, late gadolinium enhancement, and Grayzone) were selectively predictive of therapies for the different arrhythmic substrates.Methods and results: We undertook a prospective longitudinal study of consecutive patients receiving defibrillators in a tertiary cardiac center. Participants underwent CMR myocardial tissue characterization using T1 mapping and conventional CMR scar assessment before device implantation. QRS duration and fragmentation on the surface electrocardiogram were also assessed. The primary endpoint was appropriate defibrillator therapy. One-hundred thirty patients were followed up for a median of 31 months (IQR +/- 9 months). In nonischemic patients, T1-(native) was the sole predictor of the primary endpoint (hazard ratio [HR] 1.12 per 10 millisecond increment in value [95% confidence interval [CI] 1.04-1.21; P <= 0.01]). In ischemic patients, Grayzone-(2SD-3SD) was the strongest predictor of appropriate therapy (HR 1.34 per 1% left ventricular increment in value [95% CI 1.03-1.76; P = 0.03]). QRS fragmentation correlated well with myocardial scar core (receiver operating characteristic area under the curve [ROCAUC] 0.64; P = 0.02) but poorly with T1-(native) (ROCAUC 0.4) and did not predict appropriate therapy.Conclusions: In the medium-long term, T1-(native) mapping was the only independent predictor of therapy in nonischemic patients, whereas Grayzone was a better predictor in ischemic patients. These findings suggest a potential role for T1-(native) mapping in the selection of patients for ICDs in a nonischemic population.

Goal: We use noninvasive data (body surface potential mapping, BSPM) to personalize the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions. Methods: First, an efficient forward model is proposed, coupling the Mitchell-Schaeffer transmembrane potential model with a current dipole formulation. Then, we estimate the main parameters of the cardiac model: activation onset location and tissue conductivity. A large patient-specific database of simulated BSPM is generated, from which specific features are extracted to train a machine learning algorithm. The activation onset location is computed from a Kernel Ridge Regression and a second regression calibrates the global ventricular conductivity. Results: The evaluation of the results is done both on a benchmark dataset of a patient with premature ventricular contraction (PVC) and on five nonischaemic implanted cardiac resynchonization therapy (CRT) patients with a total of 21 different pacing conditions. Good personalization results were found in terms of the activation onset location for the PVC (mean distance error, MDE = 20.3 mm), for the pacing sites (MDE = 21.7 mm) and for the CRT patients (MDE = 24.6 mm). We tested the predictive power of the personalized model for biventricular pacing and showed that we could predict the new electrical activity patterns with a good accuracy in terms of BSPM signals. Conclusion: We have personalized the cardiac EP model and predicted new patient-specific pacing conditions. Significance: This is an encouraging first step towards a noninvasive preoperative prediction of the response to different pacing conditions to assist clinicians for CRT patient selection and therapy planning.

In the scope of modelling cardiac electrophysiology (EP) for understanding pathologies and predicting the response to therapies, patient-specific model parameters need to be estimated. Although personalisation from non-invasive data (body surface potential mapping, BSPM) has been investigated on simple cases mostly with a single pacing site, there is a need for a method able to handle more complex situations such as sinus rhythm with several onsets. In the scope of estimating cardiac activation maps, we propose a sparse Bayesian kernel-based regression (relevance vector machine, RVM) from a large patient-specific simulated database. RVM additionally provides a confidence on the result and an automatic selection of relevant features. With the use of specific BSPM descriptors and a reduced space for the myocardial geometry, we detail this framework on a real case of simultaneous biventricular pacing where both onsets were precisely localised. The obtained results (mean distance to the two ground truth pacing leads is 18.4 mm) demonstrate the usefulness of this non-linear approach.

Abnormal cardiac motion can indicate different forms of disease, which can manifest at different spatial scales in the myocardium. Many studies have sought to characterise particular motion abnormalities associated with specific diseases, and to utilise motion information to improve diagnoses. However, the importance of spatial scale in the analysis of cardiac deformation has not been extensively investigated. We build on recent work on the analysis of myocardial strains at different spatial scales using a cardiac motion atlas to find the optimal scales for estimating different cardiac biomarkers. We apply a multi-scale strain analysis to a 43 patient cohort of cardiac resynchronisation therapy (CRT) patients using tagged magnetic resonance imaging data for (1) predicting response to CRT, (2) identifying septal flash, (3) estimating QRS duration, and (4) identifying the presence of ischaemia. A repeated, stratified cross-validation is used to demonstrate the importance of spatial scale in our analysis, revealing different optimal spatial scales for the estimation of different biomarkers.

Cardiac Resynchronisation Therapy (CRT) causes changes in cardiac anatomy, electrophysiology and mechanics of the heart after 3-6 months of treatment. Multi-pole pacing (MPP) and multi-vein pacing (MVP) are new technologies that offer the ability to change the location of the pacing site post implant, however, the long term benefits of shifting the left ventricle (LV) pacing site are still uncertain. A personalised biophysical electromechanical model of a patient's heart was developed from MRI, echocardiogram, ECG and pressure catheter recordings, before and after sustained CRT treatment. Simulations of biventricular pacing of the heart were performed for 49 pacing sites across the LV free wall, in the model of the patient prior to-and after sustained pacing. The optimal region for LV pacing was determined by the acute haemodynamic response (AHR). After sustained CRT treatment the heart remodels and the models predict that the optimal region for pacing the LV would expand by 46% after this remodeling. The expansion in the optimal LV pacing region after remodeling predicts that if LV lead location was placed within the optimal region prior to CRT treatment, it will remain within the optimal region after sustained pacing.

Device Optimization for Acute and Chronic CRTBackgroundCardiac anatomy and function adapt in response to chronic cardiac resynchronization therapy (CRT). The effects of these changes on the optimal left ventricle (LV) lead location and timing delay settings have yet to be fully explored.ObjectiveTo predict the effects of chronic CRT on the optimal LV lead location and device timing settings over time.MethodsBiophysical computational cardiac models were generated for 3 patients, immediately post‐implant (ACUTE) and after at least 6 months of CRT (CHRONIC). Optimal LV pacing area and device settings were predicted by pacing the ACUTE and CHRONIC models across the LV epicardium (49 sites each) with a range of 9 pacing settings and simulating the acute hemodynamic response (AHR) of the heart.ResultsThere were statistically significant differences between the distribution of the AHR in the ACUTE and CHRONIC models (P < 0.0005 in all cases). The site delivering the maximal AHR shifted location between the ACUTE and CHRONIC models but provided a negligible improvement (<2%). The majority of the acute optimal LV pacing regions (76–100%) and device settings (76–91%) remained optimal chronically.ConclusionOptimization of the LV pacing location and device settings were important at the time of implant, with a reduced benefit over time, where the majority of the acute optimal LV pacing region and device settings remained optimal with chronic CRT.

We propose a novel technique for the localisation of Left Ventricular (LV) scar based on local motion descriptors. Cardiac MR imaging is employed to construct a spatio-temporal motion atlas where the LV motion of different subjects can be directly compared. Local motion descriptors are derived from the motion atlas and dictionary learning is used for scar classification. Preliminary results on a cohort of 20 patients show a sensitivity and specificity of 80 % and 87 % in a binary classification setting.

We present a framework for combining a cardiac motion atlas with non-motion data. The atlas represents cardiac cycle motion across a number of subjects in a common space based on rich motion descriptors capturing 3D displacement, velocity, strain and strain rate. The non-motion data are derived from a variety of sources such as imaging, electrocardiogram (ECG) and clinical reports. Once in the atlas space, we apply a novel supervised learning approach based on random projections and ensemble learning to learn the relationship between the atlas data and some desired clinical output. We apply our framework to the problem of predicting response to Cardiac Resynchronisation Therapy (CRT). Using a cohort of 34 patients selected for CRT using conventional criteria, results show that the combination of motion and non-motion data enables CRT response to be predicted with 91.2% accuracy (100% sensitivity and 62.5% specificity), which compares favourably with the current state-of-the-art in CRT response prediction.

Cardiac Resynchronisation Therapy (CRT) treats patients with heart failure and electrical dyssynchrony. However, many patients do not respond to therapy. We propose a novel framework for the prospective characterisation of CRT 'super-responders' based on motion analysis of the Left Ventricle (LV). A spatio-temporal motion atlas for the comparison of the LV motions of different subjects is built using cardiac MR imaging. Patients likely to present a super-response to the therapy are identified using a novel ensemble learning classification method based on random projections of the motion data. Preliminary results on a cohort of 23 patients show a sensitivity and specificity of 70% and 85%.

Background Hypertrophic cardiomyopathy (HCM) is associated with significant associated morbidity and mortality. Increased maximal left ventricular wall thickness (LVWT) has been postulated as major risk factor of sudden death; however, relatives with normal LVWT are also at risk. Genetically driven interstitial collagenesis has been proposed as a possible mechanism of diffuse myocardial fibrosis and increased extracellular volume fractions (ECV) has been demonstrated in genotype positive subjects. We investigated whether native T1 can also separate genotype positive subjects in the absence of increase in LVWT and how does it relate to ECV measurements.

Background T1 mapping by cardiovascular magnetic resonance (CMR) provides tissue-dependent relaxation times in line with the underlying myocardial composition. In this study, we examined the value of native T1 in differentiation between acute viral myocarditis and chronic stages of healing. Methods Fifty-three patients with a clinical diagnosis of acute viral myocarditis were compared to fifty-six subjects in clinical convalescence. All subjects underwent assessment of myocardial oedema, function and scar by CMR at 1.5 and 3-Tesla. T1 values were measured in midventricular short-axis slice prior to and after administration of 0.2 mmol/kg of gadobutrol. Results Compared to controls (n = 40), native T1 values were increased in both acute and chronic myocarditis at both field strengths (controls vs. acute vs. chronic myocarditis; 1.5T: native T1 (msec): 940 ± 17 vs. 1070 ± 34 vs. 998 ± 28, p < 0.001; 3T: 1045 ± 18 vs. 1182 ± 66 vs. 1094 ± 15, p < 0.001). Native T1 values were significantly higher in acute vs. chronic myocardium (p < 0.001). Native T1 was identified as the independent discriminator between health and disease, as well as acute and chronic myocarditis (Figure 1). Native T1 showed gradual reduction of values between acute and chronic stage of myocardial inflammation.