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Aims Among patients with structural heart disease with ventricular tachycardia (VT) refractory to medical therapy and catheter ablation, cardiac stereotactic body radiotherapy (SBRT) is a paradigm-changing treatment option. This study aims to assess the efficacy of cardiac SBRT in refractory VT by comparing the rates of VT episodes, anti-tachycardia pacing (ATP) therapies, and implantable cardioverter-defibrillator (ICD) shocks post-SBRT with pre-SBRT. Methods and results We performed a comprehensive literature search and included all clinical studies reporting outcomes on cardiac SBRT for VT. Treatment efficacy was evaluated as random-effects pooled rate-ratios of VT episodes, ATP therapies and ICD shocks post-SBRT (after 6-week blanking) and pre-SBRT, with patients serving as their own controls. Post-SBRT overall survival was assessed using Kaplan–Meier method. We included 23 studies published 2017–24 reporting on 225 patients who received cardiac SBRT, with median follow-up 5.8–28 months. There was significant heterogeneity among the studies for all three efficacy endpoints (P < 0.00001). The random-effects pooled rate-ratios of VT episodes, ATP therapies and ICD shocks post- vs. pre-SBRT were 0.10 (95% CI 0.06, 0.16), 0.09 (0.05, 0.15), and 0.09 (0.05, 0.17), respectively (all P < 0.00001). The most common reported complications included pericardial (8.0%, including 0.9% late oesophagogastro-pericardial fistula) and pulmonary (5.8%). There was no change in left ventricular ejection fraction post-SBRT (P = 0.3) but some studies reported an increase in mitral regurgitation. The combined 3-, 12-, and 24-month overall patient survival was 0.86 (0.80, 0.90), 0.72 (0.65, 0.78), and 0.57 (0.47, 0.67), respectively. Conclusion Among patients with refractory VT in context of structural heart disease, VT burden and ICD shocks are dramatically reduced following cardiac SBRT. The overall mortality in this population with heart failure and refractory VT receiving palliative cardiac SBRT remains high. [ABSTRACT FROM AUTHOR]

Introduction: Biventricular pacing (BiVp) improves outcomes in systolic heart failure patients with electrical dyssynchrony. BiVp is delivered from epicardial left ventricular (LV) and endocardial right ventricular (RV) electrodes. Acute electrical activation changes with different LV‐RV stimulation offsets can help guide individually optimized BiVp programming. We sought to study the BiVp ventricular activation with different LV‐RV offsets and compare with 12‐lead ECG. Methods: In five patients with BiVp (63 ± 17‐year‐old, 80% male, LV ejection fraction 27 ± 6%), we evaluated acute ventricular epicardial activation, varying LV‐RV offsets in 20 ms increments from −40 to 80 ms, using electrocardiographic imaging (ECGI) to obtain absolute ventricular electrical uncoupling (VEUabs, absolute difference in average LV and average RV activation time) and total activation time (TAT). For each patient, we calculated the correlation between ECGI and corresponding ECG (3D‐QRS‐area and QRS duration) with different LV‐RV offsets. Results: The LV‐RV offset to attain minimum VEUabs in individual patients ranged 20–60 ms. In all patients, a larger LV‐RV offset was required to achieve minimum VEUabs (36 ± 17 ms) or 3D‐QRS‐area (40 ± 14 ms) than that for minimum TAT (−4 ± 9 ms) or QRS duration (−8 ± 11 ms). In individual patients, 3D‐QRS‐area correlated with VEUabs (r 0.65 ± 0.24) and QRS duration correlated with TAT (r 0.95 ± 0.02). Minimum VEUabs and minimum 3D‐QRS‐area were obtained by LV‐RV offset within 20 ms of each other in all five patients. Conclusions: LV‐RV electrical uncoupling, as assessed by ECGI, can be minimized by optimizing LV‐RV stimulation offset. 3D‐QRS‐area is a surrogate to identify LV‐RV offset that minimizes LV‐RV uncoupling. [ABSTRACT FROM AUTHOR]

Background: Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs. Objective: Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG. Methods: We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model). Results: Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording. Conclusion: Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs. [ABSTRACT FROM AUTHOR]

Background: Automated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification. Methods: A derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one "all‐inclusive" model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance. Results: The VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X‐lead QRS amplitude change, Y‐lead QRS amplitude change, and Z‐lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95). Conclusion: Custom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically. [ABSTRACT FROM AUTHOR]