Predicting postinfarct ventricular tachycardia by integrating cardiac MRI and advanced computational reentrant pathway analysis.

BACKGROUND: Implantable cardiac defibrillator (ICD) implantation can protect against sudden cardiac death after myocardial infarction. However, improved risk stratification for device requirement is still needed. OBJECTIVE: The purpose of this study was to improve assessment of postinfarct ventricular electropathology and prediction of appropriate ICD therapy by combining late gadolinium enhancement (LGE) and advanced computational modeling. METHODS: ADAS 3D LV (ADAS LV Medical, Barcelona, Spain) and custom-made software were used to generate 3-dimensional patient-specific ventricular models in a prospective cohort of patients with a myocardial infarction (N = 40) having undergone LGE imaging before ICD implantation. Corridor metrics and 3-dimensional surface features were computed from LGE images. The Virtual Induction and Treatment of Arrhythmias (VITA) framework was applied to patient-specific models to comprehensively probe the vulnerability of the scar substrate to sustaining reentrant circuits. Imaging and VITA metrics, related to the numbers of induced ventricular tachycardias and their corresponding round trip times (RTTs), were compared with ICD therapy during follow-up. RESULTS: Patients with an event (n = 17) had a larger interface between healthy myocardium and scar and higher VITA metrics. Cox regression analysis demonstrated a significant independent association with an event: interface (hazard ratio [HR] 2.79; 95% confidence interval [CI] 1.44-5.44; P < .01), unique ventricular tachycardias (HR 1.67; 95% CI 1.04-2.68; P = .03), mean RTT (HR 2.14; 95% CI 1.11-4.12; P = .02), and maximum RTT (HR 2.13; 95% CI 1.19-3.81; P = .01). CONCLUSION: A detailed quantitative analysis of LGE-based scar maps, combined with advanced computational modeling, can accurately predict ICD therapy and could facilitate the early identification of high-risk patients in addition to left ventricular ejection fraction.

Guided implantation of a leadless left ventricular endocardial electrode and acoustic transmitter using computed tomography anatomy, dynamic perfusion and mechanics, and predicted activation pattern.

BACKGROUND: The WiSE-CRT System (EBR systems, Sunnyvale, CA) permits leadless left ventricular pacing. Currently, no intraprocedural guidance is used to target optimal electrode placement while simultaneously guiding acoustic transmitter placement in close proximity to the electrode to ensure adequate power delivery. OBJECTIVE: The purpose of this study was to assess the use of computed tomography (CT) anatomy, dynamic perfusion and mechanics, and predicted activation pattern to identify both the optimal electrode and transmitter locations. METHODS: A novel CT protocol was developed using preprocedural imaging and simulation to identify target segments (TSs) for electrode implantation, with late electrical and mechanical activation, with ≥5 mm wall thickness without perfusion defects. Modeling of the acoustic intensity from different transmitter implantation sites to the TSs was used to identify the optimal transmitter location. During implantation, TSs were overlaid on fluoroscopy to guide optimal electrode location that were evaluated by acute hemodynamic response (AHR) by measuring the maximal rate of left ventricular pressure rise with biventricular pacing. RESULTS: Ten patients underwent the implantation procedure. The transmitter could be implanted within the recommended site on the basis of preprocedural analysis in all patients. CT identified a mean of 4.8 ± 3.5 segments per patient with wall thickness < 5 mm. During electrode implantation, biventricular pacing within TSs resulted in a significant improvement in AHR vs non-TSs (25.5% ± 8.8% vs 12.9% ± 8.6%; P < .001). Pacing in CT-identified scar resulted in either failure to capture or minimal AHR improvement. The electrode was targeted to the TSs in all patients and was implanted in the TSs in 80%. CONCLUSION: Preprocedural imaging and modeling data with intraprocedural guidance can successfully guide WiSE-CRT electrode and transmitter implantation to allow optimal AHR and adequate power delivery.

Evaluation of photometric stereo and elastomeric sensor imaging for the non-destructive 3D analysis of questioned documents - A pilot study.

Photometric Stereo and Elastomeric Sensor Imaging were assessed for measuring the 3-dimensional (3D) morphology of questioned document samples. Photometric stereo is shown to be a powerful non-contact technique for revealing micron level detail of the samples examined. Elastomeric Sensor Imaging is shown to complement photometric stereo by yielding equivalent results. Additionally, this technique allows quantification of the morphological depth information. The techniques were applied to 2 different types of questioned document sample. Firstly, written signatures were examined. Both techniques were able to reveal characteristic features that could be used to infer stroke direction and ink line application sequence. Secondly toner/ink intersections were examined. Both techniques allowed visualisation of 3D features which were used to infer the sequence of application.

Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images.

Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance metrics. When considering their role in a clinical decision support setting, AI classification models should ideally avoid confident wrong predictions and maximise the confidence of correct predictions. Models that do this are said to be well calibrated with regard to confidence. However, relatively little attention has been paid to how to improve calibration when training these models, i.e. to make the training strategy uncertainty-aware. In this work we: (i) evaluate three novel uncertainty-aware training strategies with regard to a range of accuracy and calibration performance measures, comparing against two state-of-the-art approaches, (ii) quantify the data (aleatoric) and model (epistemic) uncertainty of all models and (iii) evaluate the impact of using a model calibration measure for model selection in uncertainty-aware training, in contrast to the normal accuracy-based measures. We perform our analysis using two different clinical applications: cardiac resynchronisation therapy (CRT) response prediction and coronary artery disease (CAD) diagnosis from cardiac magnetic resonance (CMR) images. The best-performing model in terms of both classification accuracy and the most common calibration measure, expected calibration error (ECE) was the Confidence Weight method, a novel approach that weights the loss of samples to explicitly penalise confident incorrect predictions. The method reduced the ECE by 17% for CRT response prediction and by 22% for CAD diagnosis when compared to a baseline classifier in which no uncertainty-aware strategy was included. In both applications, as well as reducing the ECE there was a slight increase in accuracy from 69% to 70% and 70% to 72% for CRT response prediction and CAD diagnosis respectively. However, our analysis showed a lack of consistency in terms of optimal models when using different calibration measures. This indicates the need for careful consideration of performance metrics when training and selecting models for complex high risk applications in healthcare.

The role of guidance in delivering cardiac resynchronization therapy: A systematic review and network meta-analysis.

Background: Positioning the left ventricular lead at the optimal myocardial segment has been proposed to improve cardiac resynchronization therapy (CRT) response. Objectives: We performed a systematic review and network meta-analysis evaluating echocardiographic and clinical response delivered with different guidance modalities compared to conventional fluoroscopic positioning. Methods: Randomized trials with ≥6 months follow-up comparing any combination of imaging, electrical, hemodynamic, or fluoroscopic guidance were included. Imaging modalities were split whether one modality was used: cardiac magnetic resonance (CMR), speckle-tracking echocardiography (STE), single-photon emission computed tomography, cardiac computed tomography (CT), or a combination of these, defined as "multimodality imaging." Results: Twelve studies were included (n = 1864). Pair-wise meta-analysis resulted in significant odds of reduction in left ventricular end-systolic volume (LVESV) >15% (odds ratio [OR] 1.50, 95% confidence interval [CI] 1.05-2.13, P = .025) and absolute reduction in LVESV (standardized mean difference [SMD] -0.25, 95% CI -0.43 to -0.08, P = .005) with guidance. CMR (OR 55.3, 95% CI 4.7-656.9, P = .002), electrical (OR 17.0, 95% CI 2.9-100, P = .002), multimodality imaging (OR 4.47, 95% CI 1.36-14.7, P = .014), and hemodynamic guidance (OR 1.29-28.0, P = .02) were significant in reducing LVESV >15%. Only STE demonstrated a significant reduction in absolute LVESV (SMD -0.38, 95% CI -0.68 to -0.09, P = .011]. CMR had the highest probability of improving clinical response (OR 17.9, 95% CI 5.14-62.5, P < .001). Conclusion: Overall, guidance improves CRT outcomes. STE and multimodality imaging provided the most reliable evidence of efficacy. Wide CIs observed for results of CMR guidance suggest more powered studies are required before a clear ranking is possible.

Convergent ablation for persistent atrial fibrillation: outcomes from a single-centre real-world experience.

OBJECTIVES: Atrial fibrillation (AF) is common and can cause significant morbidity and detriment to quality of life. Success rates for conventional catheter ablation are suboptimal in persistent AF (PsAF), especially when longstanding. Convergent hybrid ablation combines endoscopic surgical epicardial and endocardial catheter ablation. It offers promise in treating PsAF. We aimed to evaluate outcomes at our centre following convergent ablation. METHODS: We conducted an observational study of patients undergoing ablation from 2012 to 2019 at a London cardiac centre. Sixty-seven patients underwent convergent ablation entailing epicardial ablation, mostly via sub-xiphoid access, followed by endocardial left atrial catheter ablation. Baseline and follow-up data were obtained retrospectively from clinical records. Primary outcome was freedom from AF on/off anti-arrhythmic drugs after 12-month follow-up. Secondary outcomes included freedom from AF over the entire follow-up, freedom from anti-arrhythmic drugs, freedom from atrial arrhythmias, symptom status, repeat ablation and complications. RESULTS: At baseline, 80.6% had PsAF >1 year (80.6%), 49.3% had body mass index >30 kg/m2 at baseline and 19.4% had left ventricular ejection fraction of 40% or less. The median follow-up was 2.3 (1.4-3.7) years. Freedom from AF recurrence was 81.3% at 1 year and 61.5% over overall follow-up. Eleven patients (16.4%) required redo AF ablation. Prolonged AF duration was associated with increased recurrence at 12 months and duration >5 years with a shorter time to recurrence on Kaplan-Meier analysis, but this and other factors did not significantly impact the AF recurrence during the overall follow-up period. CONCLUSIONS: Convergent ablation had good 1-year and overall success rates for treating PsAF. Our results in a diverse, real-world population support the potential of convergent ablation in patients with challenging to treat PsAF.

Multi-lead pacing for cardiac resynchronization therapy in heart failure: a meta-analysis of randomized controlled trials.

Aims: Multi-lead pacing is a potential therapy to improve response to cardiac resynchronization therapy (CRT) by providing rapid activation of the myocardium from multiple sites. Here, we perform a meta-analysis of randomized controlled trials to assess the efficacy of multi-lead pacing. Methods and results: A literature search was performed which identified 251 unique records. After screening, 6 studies were found to meet inclusion criteria, with 415 patients included in the meta-analysis. Four studies performed multi-lead pacing with two left ventricular (LV) leads and one right ventricular (RV) lead. One study used two RV leads and one LV lead, and one study used both configurations. There was no difference between multi-lead pacing and conventional CRT in LV end-systolic volume [mean difference (MD) -0.54 mL, P = 0.93] or LV ejection fraction (MD 1.42%, P = 0.40). There was a borderline significant improvement in Minnesota Living With Heart Failure Questionnaire score for multi-lead pacing vs. conventional CRT (MD -4.46, P = 0.05), but the difference was not significant when only patients receiving LV-only multi-lead pacing were included (MD -3.59, P = 0.25). There was also no difference between groups for 6-min walk test (MD 15.06 m, P = 0.38) or New York Heart Association class at follow-up [odds ratio (OR) 1.49, P = 0.24]. There was no difference in mortality between groups (OR 1.11, P = 0.77). Conclusion: This meta-analysis does not support the use of multi-lead pacing for CRT delivery. However, significant variation between studies was noted, and therefore a benefit for multi-lead pacing in select patients cannot be excluded, and further investigation may be warranted.

Effect of scar and pacing location on repolarization in a porcine myocardial infarction model.

Background: The effect of chronic ischemic scar on repolarization is unclear, with conflicting results from human and animal studies. An improved understanding of electrical remodeling within scar and border zone tissue may enhance substrate-guided ablation techniques for treatment of ventricular tachycardia. Computational modeling studies have suggested increased dispersion of repolarization during epicardial, but not endocardial, left ventricular pacing, in close proximity to scar. However, the effect of endocardial pacing near scar in vivo is unknown. Objective: The purpose of this study was to investigate the effect of scar and pacing location on local repolarization in a porcine myocardial infarction model. Methods: Six model pigs underwent late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging followed by electroanatomic mapping of the left ventricular endocardium. LGE-CMR images were registered to the anatomic shell and scar defined by LGE. Activation recovery intervals (ARIs), a surrogate for action potential duration, and local ARI gradients were calculated from unipolar electrograms within areas of late gadolinium enhancement (aLGE) and healthy myocardium. Results: There was no significant difference between aLGE and healthy myocardium in mean ARI (304.20 ± 19.44 ms vs 300.59 ± 19.22 ms; P = .43), ARI heterogeneity (23.32 ± 11.43 ms vs 24.85 ± 12.99 ms; P = .54), or ARI gradients (6.18 ± 2.09 vs 5.66 ± 2.32 ms/mm; P = .39). Endocardial pacing distance from scar did not affect ARI gradients. Conclusion: Our findings suggest that changes in ARI are not an intrinsic property of surviving myocytes within scar, and endocardial pacing close to scar does not affect local repolarization.

Machine learning-derived major adverse event prediction of patients undergoing transvenous lead extraction: Using the ESC EHRA EORP European lead extraction ConTRolled ELECTRa registry.

BACKGROUND: Transvenous lead extraction (TLE) remains a high-risk procedure. OBJECTIVE: The purpose of this study was to develop a machine learning (ML)-based risk stratification system to predict the risk of major adverse events (MAEs) after TLE. A MAE was defined as procedure-related major complication and procedure-related death. METHODS: We designed and evaluated an ML-based risk stratification system trained using the European Lead Extraction ConTRolled (ELECTRa) registry to predict the risk of MAEs in 3555 patients undergoing TLE and tested this on an independent registry of 1171 patients. ML models were developed, including a self-normalizing neural network (SNN), stepwise logistic regression model ("stepwise model"), support vector machines, and random forest model. These were compared with the ELECTRa Registry Outcome Score (EROS) for MAEs. RESULTS: There were 53 MAEs (1.7%) in the training cohort and 24 (2.4%) in the test cohort. Thirty-two clinically important features were used to train the models. ML techniques were similar to EROS by balanced accuracy (stepwise model: 0.74 vs EROS: 0.70) and superior by area under the curve (support vector machines: 0.764 vs EROS: 0.677). The SNN provided a finite risk for MAE and accurately identified MAE in 14 of 169 "high (>80%) risk" patients (8.3%) and no MAEs in all 198 "low (<20%) risk" patients (100%). CONCLUSION: ML models incrementally improved risk prediction for identifying those at risk of MAEs. The SNN has the additional advantage of providing a personalized finite risk assessment for patients. This may aid patient decision making and allow better preoperative risk assessment and resource allocation.

A multimodal deep learning model for cardiac resynchronisation therapy response prediction.

We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the 'nnU-Net' segmentation model to extract segmentations of the heart over the full cardiac cycle from the two modalities. Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities. At test time, this framework can be used with 2D echocardiography data only, whilst taking advantage of the implicit relationship between CMR and echocardiography features learnt from the model. We evaluate our pipeline on a cohort of 50 CRT patients for whom paired echocardiography/CMR data were available, and results show that the proposed multimodal classifier results in a statistically significant improvement in accuracy compared to the baseline approach that uses only 2D echocardiography data. The combination of multimodal data enables CRT response to be predicted with 77.38% accuracy (83.33% sensitivity and 71.43% specificity), which is comparable with the current state-of-the-art in machine learning-based CRT response prediction. Our work represents the first multimodal deep learning approach for CRT response prediction.

Standard care vs. TRIVEntricular pacing in Heart Failure (STRIVE HF): a prospective multicentre randomized controlled trial of triventricular pacing vs. conventional biventricular pacing in patients with heart failure and intermediate QRS left bundle branch block.

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.

Technical feasibility of leadless left bundle branch area pacing for cardiac resynchronization: a case series.

BACKGROUND: Left bundle branch area pacing (LBBAP) is a novel form of conduction system pacing which can reverse left bundle branch block and deliver cardiac resynchronization therapy (CRT). The WiSE-CRT system delivers leadless endocardial pacing with symptomatic and left ventricular (LV) remodelling improvements following intervention. We report the technical feasibility of delivering leadless LBBAP using the WiSE-CRT system. CASE SUMMARY: In Case 1, a 57-year-old male with ischaemic cardiomyopathy and complete heart block underwent implantation of the WiSE-CRT system, using a retrograde transaortic approach, after failed conventional CRT. Temporary left bundle stimulation from the LV septum achieved superior electrical resynchronization and equivalent haemodynamic response compared to endocardial pacing at the lateral LV wall. In Case 2, an 82-year-old gentleman with tachyarrhythmia-induced cardiomyopathy underwent WiSE-CRT implantation via a trans-septal inter-atrial approach, with the endocardial electrode successfully deployed in the LV septum. DISCUSSION: Here we report the first case of deployment of the WiSE-CRT endocardial electrode in the LV septum and demonstrate the technical feasibility of leadless LBBAP. Entirely leadless CRT is an attractive option for patients with venous access issues or recurrent lead complications and has previously been successful using the WiSE-CRT system and a leadless pacemaker in the right ventricle. Further studies are required to assess long-term efficacy and safety of leadless LBBAP.

Non-invasive simulated electrical and measured mechanical indices predict response to cardiac resynchronization therapy.

BACKGROUND: Cardiac Resynchronization Therapy (CRT) in dyssynchronous heart failure patients is ineffective in 20-30% of cases. Sub-optimal left ventricular (LV) pacing location can lead to non-response, thus there is interest in LV lead location optimization. Invasive acute haemodynamic response (AHR) measurements have been used to optimize the LV pacing location during CRT implantation. In this manuscript, we aim to predict the optimal lead location (AHR>10%) with non-invasive computed tomography (CT) based measures of cardiac anatomical and mechanical properties, and simulated electrical activation times. METHODS: Non-invasive measurements from CT images and ECG were acquired from 34 patients indicated for CRT upgrade. The LV lead was implanted and AHR was measured at different pacing sites. Computer models of the ventricles were used to simulate the electrical activation of the heart, track the mechanical motion throughout the cardiac cycle and measure the wall thickness of the LV on a patient specific basis. RESULTS: We tested the ability of electrical, mechanical and anatomical indices to predict the optimal LV location. Electrical (RV-LV delay) and mechanical (time to peak contraction) indices were correlated with an improved AHR, while wall thickness was not predictive. A logistic regression model combining RV-LV delay and time to peak contraction was able to predict positive response with 70 ± 11% accuracy and AUROC curve of 0.73. CONCLUSION: Non-invasive electrical and mechanical indices can predict optimal epicardial lead location. Prospective analysis of these indices could allow clinicians to test the AHR at fewer pacing sites and reduce time, costs and risks to patients.

The physiological effects of cardiac resynchronization therapy on aortic and pulmonary flow and dynamic and static components of systemic impedance.

BACKGROUND: Patients who improve following cardiac resynchronization therapy (CRT) have left ventricular (LV) remodeling and improved cardiac output (CO). Effects on the systemic circulation are unknown. OBJECTIVE: To explore the effects of CRT on aortic and pulmonary blood flow and systemic afterload. METHODS: At CRT implant patients underwent a noninvasive assessment of central hemodynamics, including wave intensity analysis (n = 28). This was repeated at 6 months after CRT. A subsample (n = 11) underwent an invasive electrophysiological and hemodynamic assessment immediately following CRT. CRT response was defined as reduction in LV end-systolic volume ≥15% at 6 months. RESULTS: In CRT responders (75% of those in the noninvasive arm), there was a significant increase in CO (from 3 ± 2 L/min to 4 ± 2 L/min, P = .002) and LV dP/dtmax (from 846 ± 162 mm Hg/s to 958 ± 194 mm Hg/s, P = .001), immediately after CRT in those in the invasive arm. They demonstrated a significant increase in aortic forward compression wave (FCW) both acutely and at follow-up. The relative change in LV dP/dtmax strongly correlated with changes in the aortic FCW (R s 0.733, P = .025). CRT responders displayed a significant reduction in afterload, and a decrease in systemic vascular resistance and pulse wave velocity acutely; there was a significant decrease in acute pulmonary afterload measured by the pulmonary FCW and forward expansion wave. CONCLUSION: Improved cardiac function following CRT is attributable to a combination of changes in the cardiac and cardiovascular system. The relative importance of these 2 mechanisms may then be important for optimizing CRT.

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.

Multipoint pacing for cardiac resynchronisation therapy in patients with heart failure: A systematic review and meta-analysis.

INTRODUCTION: Multipoint pacing (MPP) has been proposed as an effective way to improve cardiac resynchronisation therapy (CRT) response. We performed a systematic review and meta-analysis evaluating the efficacy of CRT delivered via MPP compared to conventional CRT. METHODS: A literature search was performed from inception to January 2021 for studies in Medline, Embase and Cochrane databases, comparing MPP to conventional CRT with a minimum of 6 months follow-up. Randomised and nonrandomised studies were assessed for relevant efficacy data including echocardiographic (left ventricular end systolic volume [LVESV] and ejection fraction) or functional changes (New York Heart Association [NYHA] class/Clinical Composite Score). Subgroup analyses were performed by study design and programming type. RESULTS: A total of 7 studies with a total of 1390 patients were included in the final analysis. Overall, MPP demonstrated greater echocardiographic improvement than conventional CRT in nonrandomised studies (odds ratio [OR]: 5.33, 95% confidence interval [CI]: [3.05-9.33], p < .001), however, was not significant in randomised studies (OR: 1.86, 95% CI: [0.91-3.79], p = .086). There was no significant difference in LVESV reduction >15% (OR: 1.96, 95% CI: [0.69-5.55], p = .20) or improvement by ≥1 NYHA class (OR: 2.49, 95% CI: [0.74-8.42], p = .141) when comparing MPP to conventional CRT. In a sub analysis, MPP programmed by widest anatomical separation (MPP-AS) signalled greater efficacy, however, only 120 patients were included in this analysis. CONCLUSION: Overall MPP was more efficacious in nonrandomised studies, and not superior when assessed in randomised studies. There was considerable heterogeneity in study design making overall interpretation of results challenging. Widespread MPP programming in all CRT patients is currently not justified. Further large, randomised studies with patient-specific programming may clarify its effectiveness.

Leadless left ventricular endocardial pacing for CRT upgrades in previously failed and high-risk patients in comparison with coronary sinus CRT upgrades.

AIMS: Cardiac resynchronization therapy (CRT) upgrades may be less likely to improve following intervention. Leadless left ventricular (LV) endocardial pacing has been used for patients with previously failed CRT or high-risk upgrades. We compared procedural and long-term outcomes in patients undergoing coronary sinus (CS) CRT upgrades with high-risk and previously failed CRT upgrades undergoing LV endocardial upgrades. METHOD AND RESULTS: Prospective consecutive CS upgrades between 2015 and 2019 were compared with those undergoing WiSE-CRT implantation. Cardiac resynchronization therapy response at 6 months was defined as improvement in clinical composite score (CCS) and a reduction in LV end-systolic volume (LVESV) ≥15%. A total of 225 patients were analysed; 121 CS and 104 endocardial upgrades. Patients receiving WiSE-CRT tended to have more comorbidities and were more likely to have previous cardiac surgery (30.9% vs. 16.5%; P = 0.012), hypertension (59.2% vs. 34.7%; P < 0.001), chronic obstructive airways disease (19.4% vs. 9.9%; P = 0.046), and chronic kidney disease (46.4% vs. 21.5%; P < 0.01) but similar LV ejection fraction (30.0 ± 8.3% vs. 29.5 ± 8.6%; P = 0.678). WiSE-CRT upgrades were successful in 97.1% with procedure-related mortality in 1.9%. Coronary sinus upgrades were successful in 97.5% of cases with a 2.5% rate of CS dissection and 5.6% lead malfunction/displacement. At 6 months, 91 WiSE-CRT upgrades and 107 CS upgrades had similar improvements in CCS (76.3% vs. 68.5%; P = 0.210) and reduction in LVESV ≥15% (54.2% vs. 56.3%; P = 0.835). CONCLUSION: Despite prior failed upgrades and high-risk patients with more comorbidities, WiSE-CRT upgrades had high rates of procedural success and similar improvements in CCS and LV remodelling with CS upgrades.

Automated Left Ventricle Ischemic Scar Detection in CT Using Deep Neural Networks.

Objectives: The aim of this study is to develop a scar detection method for routine computed tomography angiography (CTA) imaging using deep convolutional neural networks (CNN), which relies solely on anatomical information as input and is compatible with existing clinical workflows. Background: Identifying cardiac patients with scar tissue is important for assisting diagnosis and guiding interventions. Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is the gold standard for scar imaging; however, there are common instances where it is contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is faster than Cardiovascular magnetic resonance imaging but is unable to reliably image scar. Methods: A dataset of LGE MRI (200 patients, 83 with scar) was used to train and validate a CNN to detect ischemic scar slices using segmentation masks as input to the network. MRIs were segmented to produce 3D left ventricle meshes, which were sampled at points along the short axis to extract anatomical masks, with scar labels from LGE as ground truth. The trained CNN was tested with an independent CTA dataset (25 patients, with ground truth established with paired LGE MRI). Automated segmentation was performed to provide the same input format of anatomical masks for the network. The CNN was compared against manual reading of the CTA dataset by 3 experts. Results: Note that 84.7% cross-validated accuracy (AUC: 0.896) for detecting scar slices in the left ventricle on the MRI data was achieved. The trained network was tested against the CTA-derived data, with no further training, where it achieved an 88.3% accuracy (AUC: 0.901). The automated pipeline outperformed the manual reading by clinicians. Conclusion: Automatic ischemic scar detection can be performed from a routine cardiac CTA, without any scar-specific imaging or contrast agents. This requires only a single acquisition in the cardiac cycle. In a clinical setting, with near zero additional cost, scar presence could be detected to triage images, reduce reading times, and guide clinical decision-making.