Predictor Of Ventricular Tachycardia Research Articles

Direct Comparison of Methods to Differentiate Wide Complex Tachycardias: Novel Automated Algorithms Versus Manual ECG Interpretation Approaches.

Differentiating wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide tachycardia via 12-lead ECG interpretation is a crucial but difficult task. Automated algorithms show promise as alternatives to manual ECG interpretation, but direct comparison of their diagnostic performance has not been undertaken. Two electrophysiologists applied 3 manual WCT differentiation approaches (ie, Brugada, Vereckei aVR, and VT score). Simultaneously, computerized data from paired WCT and baseline ECGs were processed by 5 automated WCT differentiation algorithms (WCT Formula, WCT Formula II, VT Prediction Model, Solo Model, and Paired Model). The diagnostic performance of automated algorithms was compared with manual ECG interpretation approaches. A total of 212 WCTs (111 VT and 101 supraventricular wide tachycardia) from 104 patients were analyzed. WCT Formula demonstrated superior accuracy (85.8%) and specificity (87.1%) compared with Brugada (75.2% and 57.4%, respectively) and Vereckei aVR (65.3% and 36.4%, respectively). WCT Formula II achieved higher accuracy (89.6%) and specificity (85.1%) against Brugada and Vereckei aVR. Performance metrics of the WCT Formula (accuracy 85.8%, sensitivity 84.7%, and specificity 87.1%) and WCT Formula II (accuracy 89.8%, sensitivity 89.6%, and specificity 85.1%) were similar to the VT score (accuracy 84.4%, sensitivity 93.8%, and specificity 74.2%). Paired Model was superior to Brugada in accuracy (89.6% versus 75.2%), specificity (97.0% versus 57.4%), and F1 score (0.89 versus 0.80). Paired Model surpassed Vereckei aVR in accuracy (89.6% versus 65.3%), specificity (97.0% versus 75.2%), and F1 score (0.89 versus 0.74). Paired Model demonstrated similar accuracy (89.6% versus 84.4%), inferior sensitivity (79.3% versus 93.8%), but superior specificity (97.0% versus 74.2%) to the VT score. Solo Model and VT Prediction Model accuracy (82.5% and 77.4%, respectively) was superior to the Vereckei aVR (65.3%) but similar to Brugada (75.2%) and the VT score (84.4%). Automated WCT differentiation algorithms demonstrated favorable diagnostic performance compared with traditional manual ECG interpretation approaches.

Studying the Influence of Finite Element Mesh Size on the Accuracy of Ventricular Tachycardia Simulation.

Ventricular tachycardia (VT) is a life-threatening heart condition commonly seen in patients with myocardial infarction (MI). Although personalized computational modeling has been used to understand VT and its treatment noninvasively, this approach can be computationally intensive and time consuming. Therefore, finding a balance between mesh size and computational efficiency is important. This study aimed to find an optimal mesh resolution that minimizes the need for computational resources while maintaining numerical accuracy and to investigate the effect of mesh resolution variation on the simulation results. We constructed ventricular models from contrast-enhanced magnetic resonance imaging data from six patients with MI. We created seven different models for each patient, with average edge lengths ranging from 315 to 645 µm using commercial software, Mimics. Programmed electrical stimulation was used to assess VT inducibility from 19 sites in each heart model. The simulation results in the slab model with adaptive tetrahedral mesh (same as in the patient-specific model) showed that the absolute and relative differences in conduction velocity (CV) were 6.1 cm/s and 7.8% between average mesh sizes of 142 and 600 µm, respectively. However, the simulation results in the six patient-specific models showed that average mesh sizes with 350 µm yielded over 85% accuracy for clinically relevant VT. Although average mesh sizes of 417 and 478 µm could also achieve approximately 80% accuracy for clinically relevant VT, the percentage of incorrectly predicted VTs increases. When conductivity was modified to match the CV in the model with the finest mesh size, the overall ratio of positively predicted VT increased. The proposed personalized heart model could achieve an optimal balance between simulation time and VT prediction accuracy when discretized with adaptive tetrahedral meshes with an average edge length about 350 µm.

Mitral annular disjunction; how accurate are we? A cardiovascular MRI study defining risk

AimsMitral Annular Disjunction (MAD) refers to embryologic fibrous separation between mitral annular ring and basal left ventricular myocardium. Since its original description, the role of MAD in arrhythmic mitral valve prolapse (MVP) has been the subject of active research. In this study we sought to assess prognostic and imaging characteristics of MVP patients with and without underlying MAD. Methods and resultsPatients with posterior or bi-leaflet MVP were retrospectively identified via a review of all patients referred to our cardiac magnetic resonance (CMR) imaging laboratory from January 2015 to May 2022. MVP patients were further stratified by underlying MAD status. A total of 100 MVP patients undergoing CMR imaging (52 MVP patients with posterior MAD) were retrospectively identified with female comprising 55 % of the cohort. MVP patients with MAD were more likely to have an abnormal basal inferolateral/ papillary muscles LGE (51 % vs 21 %, p < 0.01). Posterior MAD longitudinal disjunction gap in ‘mm’ was a predictor of ventricular tachycardia (VT) [1.29, p = 0.01)]. Using ROC curve analysis, a disjunction gap of ≥ 4 mm was predictive of VT (AUC-0.71, p < 0.01), and incorporation of LGE in ROC model further improved AUC to 0.78 confirmed via Akaike information criterion (p < 0.01). ConclusionAbnormal LGE involving basal inferolateral myocardium and papillary muscles may provide etiologic substrate for arrythmia in MVP patients.

Evaluation of premature ventricular complexes during in-hospital ECG monitoring as a predictor of ventricular tachycardia in an intensive care unit cohort.

In-hospital electrocardiographic (ECG) monitors are typically configured to alarm for premature ventricular complexes (PVCs) due to the potential association of PVCs with ventricular tachycardia (VT). However, no contemporary hospital-based studies have examined the association of PVCs with VT. Hence, the benefit of PVC monitoring in hospitalized patients is largely unknown. This secondary analysis used a large PVC alarm data set to determine whether PVCs identified during continuous ECG monitoring were associated with VT, in-hospital cardiac arrest (IHCA), and/or death in a cohort of adult intensive care unit patients. Six PVC types were examined (i.e., isolated, bigeminy, trigeminy, couplets, R-on-T, and run PVCs) and were compared between patients with and without VT, IHCA, and/or death. Of 445 patients, 48 (10.8%) had VT; 11 (2.5%) had IHCA; and 49 (11%) died. Isolated and run PVC counts were higher in the VT group (p = 0.03 both), but group differences were not seen for the other four PVC types. The regression models showed no significant associations between any of the six PVC types and VT or death, although confidence intervals were wide. Due to thesmall number of cases, we were unable to test for associations between PVCs and IHCA. Our findings suggest that we should question the clinical relevance of activating PVC alarms as a forewarning of VT, and more work should be done with larger sample sizes. A more precise characterization of clinically relevant PVCs that might be associated with VT is warranted.

Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias.

Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning-based workflow for reconstructing personalized computational electrophysiological heart models to guide patient-specific treatment of VT. Contrast-enhanced computed tomography (CE-CT) images with expert ventricular myocardium segmentations were acquired from 111 patients across five cohorts from three different institutions. A deep convolutional neural network (CNN) for segmenting left ventricular myocardium from CE-CT was developed, trained and evaluated. From both CNN-based and expert segmentations in a subset of patients, personalized electrophysiological heart models were reconstructed and rapid pacing was used to induce VTs. CNN-based and expert segmentations were more concordant in the middle myocardium than in the heart's base or apex. Wavefront propagation during pacing was similar between CNN-based and original heart models. Between most sets of heart models, VT inducibility was the same, the number of induced VTs was strongly correlated, and VT circuits co-localized. Our results demonstrate that personalized computational heart models reconstructed from deep learning-based segmentations even with a small training set size can predict similar VT inducibility and circuit locations as those from expertly-derived heart models. Hence, a user-independent, automated framework for simulating arrhythmias in personalized heart models could feasibly be used in clinical settings to aid VT risk stratification and guide VT ablation therapy. KEY POINTS: Personalized electrophysiological heart modelling can aid in patient-specific ventricular tachycardia (VT) risk stratification and VT ablation targeting. Current state-of-the-art, image-based heart models for VT prediction require expert-dependent, manual interactions that may not be accessible across clinical settings. In this study, we develop an automated, deep learning-based workflow for reconstructing personalized heart models capable of simulating arrhythmias and compare its predictions with that of expert-generated heart models. The number and location of VTs was similar between heart models generated from the deep learning-based workflow and expert-generated heart models. These results demonstrate the feasibility of using an automated computational heart modelling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centres.

Abstract 12600: Mitral Annular Disjunction: How Accurate Are We? A Cardiovascular MRI Study Defining Risk

Background: Mitral Annular Disjunction (MAD) refers to embryologic fibrous separation between mitral annular ring and basal left ventricular myocardium. Since its original description, the role of MAD in arrhythmic mitral valve prolapse (MVP) has been subject of active research. In this study we sought to assess prognostic and imaging characteristics of MVP patients with and without underlying MAD. Methods: Patients with posterior or bi-leaflet MVP were retrospectively identified via a review of all patients referred to our cardiac magnetic resonance (CMR) imaging laboratory from January 2015 to May 2022. MVP patients were further stratified by underlying MAD status. CMR characteristics including late gadolinium enhancement (LGE) distribution, clinical characteristics, and data on mitral valve intervention were determined. Results: A total of 100 MVP patients undergoing CMR imaging (52 MVP patients with posterior MAD) were retrospectively identified with female comprising 55% of the cohort. MVP patients with MAD were more likely to have an abnormal basal inferolateral/ papillary muscles LGE (51% vs 21%, p&lt;0.01). Posterior MAD longitudinal disjunction gap in ‘mm’ was a predictor of ventricular tachycardia (VT) [1.23, p=0.03)]. Similarly, posterior MAD as a binary variable was predictor of VT (11, p&lt;0.01), ventricular arrhythmia (composite of VT, and non-sustained VT) (2.4, p=0.03), and major adverse cardiac events (MACE) (composite of VT, syncope, and mitral valve intervention) (2.5, p=0.03). Using ROC curve analysis, a disjunction gap of &gt; 4 mm was predictive of VT (AUC-0.71, p&lt;0.01), and incorporation of LGE in ROC model further improved AUC to 0.78 confirmed via Akaike information criterion (p&lt;0.05), see Figure. Conclusion: Abnormal LGE involving basal inferolateral myocardium and papillary muscles may provide substrate for arrythmia in MVP patients. Further, for the first time, a MAD threshold of &gt; 4 mm is demonstrated to stratify the VT risk.

Abstract 13915: Man vs. Machine: A Direct Comparison of the Diagnostic Efficacy of Novel Automated WCT Differentiation to Traditional Manual ECG Interpretation Approaches

BACKGROUND: Differentiation of wide QRS complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide QRS tachycardia (SWCT) through 12-lead electrocardiogram (ECG) interpretation is one of the most critical yet challenging tasks in clinical practice. Recent research highlights novel automated methods that successfully diagnose WCTs. We sought to directly compare the diagnostic efficacy of these novel automated WCT differentiation methods (i.e., WCT Formula, VT Prediction Model, and WCT Formula II) to traditional ECG interpretation approaches (i.e., Brugada and Vereckei aVR algorithms). METHODS: A collection of paired WCT and baselined ECGs were retrospectively analyzed. Next, an electrophysiologist blindly and prospectively applied the Brugada and Vereckei aVR algorithms. Separately, computerized measurements were used to apply the three automated WCT differentiation methods. The diagnostic performance of each method was then evaluated. RESULTS: 213 WCTs (111 VT and 102 SWCT) from 105 patients were analyzed. The WCT Formula demonstrated superior overall accuracy (84.7% vs. 64.8%), specificity (86.2% vs. 35.3%), positive predictive value (PPV) (85.7% vs. 60.7%), and positive (+) likelihood ratio (LR) (6.06 vs. 1.42) compared to Vereckei aVR algorithm. Similarly, the WCT Formula attained higher specificity (86.2% vs. 61.8%), PPV (85.7% vs. 71.9%), and (+) LR (6.06 vs. 2.36) than the Brugada algorithm. The WCT Formula II demonstrated superior overall accuracy (89.2% vs. 64.8%), specificity (84.3 % vs. 35.3%), PPV (86.7% vs. 60.7%), and positive (+) LR (5.97 vs.1.42) compared to Vereckei aVR algorithm as well as higher specificity (84.3 % vs. 61.8 %), PPV (86.7% vs. 71.9%), and (+) LR (5.97 vs. 2.36) than the Brugada algorithm. The VT prediction model yielded superior specificity (79.4% vs. 35.3%), PPV (79.8 vs. 60.7%) and (+) LR (3.63 vs.1.42) compared to the Vereckei aVR algorithm but did not demonstrate superior diagnostic performance to the Brugada algorithm. CONCLUSIONS: Novel automated WCT differentiation methods achieved similar or superior diagnostic performance metrics compared to traditional ECG interpretation approaches.

Wide complex tachycardia discrimination tool improves physicians' diagnostic accuracy

BackgroundTimely and accurate discrimination of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular WCT (SWCT) is critically important. Previously we developed and validated an automated VT Prediction Model that provides a VT probability estimate using the paired WCT and baseline 12-lead ECGs. Whether this model improves physicians' diagnostic accuracy has not been evaluated. ObjectiveWe sought to determine whether the VT Prediction Model improves physicians' WCT differentiation accuracy. MethodsOver four consecutive days, nine physicians independently interpreted fifty WCT ECGs (25 VTs and 25 SWCTs confirmed by electrophysiological study) as either VT or SWCT. Day 1 used the WCT ECG only, Day 2 used the WCT and baseline ECG, Day 3 used the WCT ECG and the VT Prediction Model's estimation of VT probability, and Day 4 used the WCT ECG, baseline ECG, and the VT Prediction Model's estimation of VT probability. ResultsInclusion of the VT Prediction Model data increased diagnostic accuracy versus the WCT ECG alone (Day 3: 84.2% vs. Day 1: 68.7%, p 0.009) and WCT and baseline ECGs together (Day 3: 84.2% vs. Day 2: 76.4%, p 0.003). There was no further improvement of accuracy with addition of the baseline ECG comparison to the VT Prediction Model (Day 3: 84.2% vs. Day 4: 84.0%, p 0.928). Overall sensitivity (Day 3: 78.2% vs. Day 1: 67.6%, p 0.005) and specificity (Day 3: 90.2% vs. Day 1: 69.8%, p 0.016) for VT were superior after the addition of the VT Prediction Model. ConclusionThe VT Prediction Model improves physician ECG diagnostic accuracy for discriminating WCTs.

Predictors of long-term ventricular tachyarrhythmia recurrence after combined endo-epicardial ablation in patients with structural heart disease

Purpose. To identify predictors of ventricular tachycardia (VT) recurrence after endo-epicardial ablation in patients with structural heart disease.Methods. A prospective observational study included 39 patients with structural heart disease and indications for catheter ablation of ventricular tachyarrhythmia. Endo- and epicardial electroanatomical mapping of the ventricular myocardium and ablation of abnormal electrical activity areas were performed. Clinical, ECG characteristics, and voltage maps of bipolar potentials (limits of scar detection &lt;0.5 mV, normal activity &gt;1.5 mV) and unipolar signals (limits &lt;5.0 and &gt;9.0 mV, respectively) on endo- and epicardial surfaces were evaluated. Intraprocedurally, the procedure was considered effective when no VT was inducible; partially effective - when only clinical VT(s) was/were non-inducible. Scheduled patient visits or remote monitoring were performed at 6, 12 and 24 months, and then annually.Results. The mean age of the patients was 49.5±15.7 years (34 men and 5 women). VT recurrences at 6 months were more often detected in non-ischemic cardiomyopathy patients, in subjects with non-paroxysmal atrial fibrillation (42.9% vs. 7.4%), with a lower VT-QRS amplitude in lead III (0.6 [0.4;1.07] versus 1.28 [0.99; 1.53] mV), and when epicardial “scar” area prevailed over endocardial, P&lt;0.05 for all listed parameters. At 12-months, VT recurrence was more common in patients with partially effective ablation (33.3% vs. 5.0% in patients with effective ablation, P=0.02). The presence of electrical storm at the time of ablation was independently associated with recurrences (HR 4.32; 95% CI: 1.06-17.48; P=0.04).Conclusion. In a heterogeneous group of patients, clinical and electrophysiological factors associated with VT recurrence have been identified at various follow-up periods after endo-epicardial ablation. Electrical storm ablation is an independent predictor of VT recurrence in the long-term, up to 5 years of follow-up.

Atrial fibrillation is associated with an increased risk of ventricular arrhythmias and sudden death in patients with pacemakers and implantable cardioverter defibrillators (ICDs)

Abstract Funding Acknowledgements Type of funding sources: None. Background Atrial fibrillation (AF) has been linked to an increase in the risk of ventricular arrhythmias. Purpose We aimed to investigate whether AF is associated with an increased risk of ventricular tachycardia (VT), ventricular fibrillation (VF) and sudden death (SD) in patients with cardiac implantable electronic devices (CIEDs). Methods All patients hospitalised in France between 2011 and 2020 with a history of pacemakers (PPMs) and implantable cardioverter defibrillator (ICD) were identified from the French National database. Patients with a prior history of VT, VF and SD were excluded. Results A total of 701,195 patients were identified. Of these, 581,781 (90.1%) patients had PPMs and 63,726 (9.9%) had ICDs. In the PPM group, 248046 (42.6%) had AF and 333735 (57.4%) had no AF. After multivariable analysis, predictors for VT, VF and SD included sex, diabetes, heart failure, history of pulmonary oedema, valve disease, dilated cardiomyopathy, coronary artery disease (CAD), AF, vascular disease, intracranial bleeding, smoking, dyslipidaemia, alcohol related disorders, lung disease, chronic kidney disease (CKD), thyroid disorders, inflammatory diseases, anaemia, poor nutrition, cognitive impairment, previous cancer and frailty. The incidence of VT, VF and SD was higher in patients with AF (1.47%/year) compared to those without AF (0.94%/year), with the risk significantly elevated in the former group, hazard ratio (HR) 1.554 (confidence interval (CI) 1.508-1.601). After adjustment for confounders (Figure 1), AF was still associated with a significantly increased risk of VT, VF and SD, HR 1.236 (CI 1.198-1.276) in patients with PPMs. This was further demonstrated through a 1:1 propensity score matched (PSM) analysis (n=200977 in each group) where the risk of incident outcomes was significantly higher in PPM patients with AF, HR 1.230 (1.187-1.274), compared to those without AF. In the ICD group, 20965 (32.9%) had AF and 42761 (67.1%) had no history of AF. Predictors of VT, VF and SD after multivariable analysis included age, sex, diabetes mellitus, heart failure, valve disease, CAD, previous percutaneous coronary intervention, vascular disease, AF, CKD, liver disease and frailty. Incidence of VT, VF and SD was higher in ICD patients with AF (5.30%/ year) compared to those without AF (4.21%/year), with a significantly higher risk, HR 1.261 (CI 1.204-1.320). After adjustment for confounders, this elevated risk was still significant HR 1.167 (1.111-1.226) (Figure 1). 1:1 PSM analysis (n=18349 in each group) demonstrated this further with a significantly elevated risk in ICD patients with AF, compared to ICD patients without AF, HR 1.134 (CI 1.071-1.200). Conclusion Our findings suggest that patients with PPM and ICD with concurrent AF are at a higher risk of VT, VF and sudden death compared to patients with PPM and ICD who do not have AF.

Automatic wide complex tachycardia differentiation using mathematically synthesized vectorcardiogram signals.

BackgroundAutomated 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.MethodsA 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.ResultsThe 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).ConclusionCustom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.

Sustained ventricular tachycardia in acute coronary syndromes the Portuguese experience

Abstract Funding Acknowledgements Type of funding sources: None. OnBehalf Portuguese Registry of Acute Coronary Syndromes Background Acute coronary syndromes (ACS) are frequent and are associated with high levels of comorbidities and complications. Ventricular tachycardia (VT) is one of the most danger and stressful situations in ACS. Objective Evaluate predictors of ventricular tachycardia in ACS. Methods Multicenter retrospective study, based on the Portuguese Registry of ACS between 1/10/2010-4/09/2019. Patients were divided in two groups: A – patients without VT, and B – patients that presented VT on the hospitalization. VT was defined as a register or more of the VT with at least 30 seconds. Logistic regression was performed to assess predictors of VT in ACS patients. Results 25361 in group A (98.6%) and 364 in group B (1.4%). Both groups were similar regarding gender, cardiovascular risk factors, except for dyslipidemia (61.7 vs 51.9%, p &amp;lt; 0.001) and ST-segment elevation myocardial infarction (STEMI) location. Group B was elderly (67 ± 14 vs 70 ± 14, p &amp;lt; 0.001), was admitted directly to the cat lab (10.6 vs 20.4%, p &amp;lt; 0.001), had less time since the onset of symptoms until the admission (383 ± 157 vs 349 ± 121, p = 0.003), but presented higher previous history of heart failure (5.9 vs 10.6%, p &amp;lt; 0.001), peripheral vascular disease (5.5 vs 8.4%, p = 0.015), chronic obstructive pulmonary disease (COPD) (4.4 vs 7.9%, p = 0.001) and dementia (1.7 vs 3.2%, p = 0.038). At admission presented higher levels of STEMI (42 vs 67%, p &amp;lt; 0.001), dyspnea (29 vs 18.1%, p &amp;lt; 0.001), syncope (1.3 vs 6.6%, p &amp;lt; 0.001), cardiac arrest (0.4 vs 4.4%, p &amp;lt; 0.001), Killip-Kimball classification &amp;gt; I (14.8 vs 40.5%, p &amp;lt; 0.001) and atrial fibrillation at admission (AF) (7.1 vs 15.3%, p &amp;lt; 0.001). Ivabradine (3.7 vs 7.6%, p &amp;lt; 0.001), aldosterone receptor antagonists (10.2 vs 24%, p &amp;lt; 0.001), diuretic (28 vs 57.2%, p &amp;lt; 0.001), amiodarone (5.6 vs 53.5%, p &amp;lt; 0.001), digoxin (1.4 vs 4.7%, p &amp;lt; 0.001) were more prevalent used in the admission. Group B exhibited higher multivessel disease (MVD) (51.5 vs 61.5%, p &amp;lt; 0.001), culprit as common coronary trunk (CT) (1.7 vs 4.2%, p = 0.024), hybrid revascularization (0.8 vs 2%, p = 0.032) and left ventricular ejection fraction (LVEF)&amp;lt;50% (38.7 vs 71%, p &amp;lt; 0.001). On the other hand, the used of beta block (81.4 vs 62.3%, p &amp;lt; 0.001), angiotensin-converting-enzyme inhibitor (85.5 vs 74.4%, p &amp;lt; 0.001) and calcium channel blockers (10.1 vs 24%, p &amp;lt; 0.001) since had a protect effect. Regarding reinfarction (0.9 vs 2.5%, p = 0.007), de novo heart failure (15.1 vs 50.3%, p &amp;lt; 0.001), atrioventricular block (2.2 vs 17%, p &amp;lt; 0.001), stroke (1.4 vs 4.9%, p &amp;lt; 0.001) and death (3.4 vs 26.9%, p &amp;lt; 0.001), all were higher in Group B. Logistic regression revealed COPD (odds ratio (OR) 1.9, p = 0.010, confidence interval (CI) 1.17-3.10), STEMI (OR 2.73, p &amp;lt; 0.001, CI 2.00-3.73), AF (OR 2.30, p &amp;lt; 0.001, CI 1.52-3.49), MVD (OR 1.44, p = 0.012, CI 1.08-1.92), CT (OR 2.87, p = 0.003, CI 1.45-5.69) and LVEF &amp;lt; 50% (OR 3.44, p &amp;lt; 0.001, CI 2.52-4.71) as predictors of VT in ACS. Conclusions COPD, STEMI, AF, MVD, CT and LVEF &amp;lt; 50% were predictors of VT in ACS.

Sustained ventricular tachycardia in cardiogenic shock

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Cardiogenic shock (CS) and the presence of sustained ventricular tachycardia (VT) are indicators of worse prognosis in hospitalized patients. In patients severely ill, like patients with CS, the registration of VT can be a stressful situation as well a life threatening condition. Purpose Evaluate the impact of cardiovascular previous history, clinical signs and diagnosis procedures at admission as predictors of VT in CS patients. Methods Single-centre retrospective study, engaging patients hospitalized for CS between 1/01/2014-30/10/2018. 222 patients with CS are included, 19 of them presented VT. Chi-square test, T-student test and Mann-Whitney U test were used to compare categorical and continuous variables. Multiple linear regression analysis was performed to evaluate predictors of new-onset AF in CS patients. Results CS patients without VT and with VT presented similar age, sex, cardiovascular history (namely arterial hypertension, diabetes, dyslipidemia, obesity, smoker status, alcohol intake, previous acute coronary syndrome, history of angina, previous cardiomyopathy), neoplasia history, cardiac arrest during the CS, clinical signs at admission (like heart rate, blood pressure, respiratory rate), blood results (hemoglobin, leucocytes, troponin, creatinine, C-Reactive protein), left ventricular ejection fraction and the culprit lesion. Curiously, history of previous stroke was higher in the group of VT in CS patients with a 6.9% (p = 0.021). Curiously, VT in CS patient had not impact in mortality rates. Multiple logistic regression reveals that previous stroke was a predictor of VT in CS patients (odds ratio 4.337, confident interval 1.363-13.799, p = 0.013). Conclusions History of previous stroke was a predictor of sustained VT in CS patients. The presence of this ventricular arrhythmia can have a hemodynamic impact, however, seems not influenced mortality rates.

Islets of heterogeneous myocardium within the scar in cardiac magnetic resonance predict ventricular tachycardia after myocardial infarction

We assessed findings in cardiac magnetic resonance (CMR) as predictors of ventricular tachycardia (VT) after myocardial infarction (MI), which could allow for more precise identification of patients at risk of sudden cardiac death. Forty-eight patients after prior MI were enrolled and divided into twogroups: with (n = 24) and without (n = 24) VT. VT was confirmed by electrophysiological study and exit site was estimated based on 12-lead electrocardiogram. All patients underwent CMR with late gadolinium enhancement. The examined groups did not differ significantly in clinical and demographical parameters (including LV ejection fraction). There was a significant difference in the infarct age between the VT and non-VT group (15.8 ± 8.4 vs7.1 ± 6.7 years, respectively; P = .002), with the cut-off point at the level of 12 years. In the scar core, islets of heterogeneous myocardium were revealed. They were defined as areas of potentially viable myocardium within or adjacent to the core scar. The number of islets was the strongest independent predictor of VT (odds ratio [OR], 1.42; confidence interval [CI], 1.17-1.73), but total islet size and the largest islet area were also significantly higher in the VT group (OR, 1.04; CI, 1.02-1.07 and OR, 1.16; CI, 1.01-1.27, respectively). Myocardial segments with fibrosis forming 25%-75% of the ventricular wall were associated with a higher incidence of VT (7.5 ± 2.1 vs5.7 ± 2.6; P = .014). Three-dimension CMR reconstruction confirmed good correlation of the location of the islets/channels with VT exit site during electroanatomical mapping in five cases. The identification and quantification of islets of heterogeneous myocardium within the scar might be useful for predicting VT in patients after MI.

The VT Prediction Model: A simplified means to differentiate wide complex tachycardias.

The accurate separation of undifferentiated wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional, manually-applied 12-lead electrocardiogram (ECG) interpretation methods is difficult. We sought to devise a new WCT differentiation method that operates solely on automated measurements routinely provided by computerized ECG interpretation software. In a two-part analysis, we developed and validated a logistic regression model (ie, VT Prediction Model) that utilizes routinely available computerized measurements derived from patients' paired WCT and baseline ECGs. The derivation cohort consisted of 601 paired WCT (273 VT, 328 SWCT) and baseline ECGs from 421 patients. The VT Prediction Model, composed of WCT QRS duration (ms) (P < .0001), QRS duration change (ms) (P < .0001), QRS axis change (°) (P < .0001) and T axis change (°) (P < .0001), yielded effective VT and SWCT differentiation (area under the curve [AUC]: 0.924; confidence interval [CI]: 0.903-0.944) for the derivation cohort. The validation cohort comprised 241 paired WCT (97 VT, 144 SWCT) and baseline ECGs from 177 patients. The VT Prediction Model's implementation on the validation cohort yielded effective WCT differentiation (AUC: 0.900; CI: 0.862-0.939) with overall accuracy, sensitivity, and specificity of 85.0%, 80.4%, and 88.2%, respectively. The VT Prediction Model is an example of how readily available ECG measurements may be used to distinguish VT and SWCT effectively. Further study is needed to develop and refine newer WCT differentiation approaches that utilize computerized measurements provided by ECG interpretation software.

P3800Defibrillation for right ventricular dysfunction - An echocardiographic method for therapy selection

Abstract Introduction Cardiac resynchronization therapy (CRT) is recommended for heart failure (HF) patients with left ventricular ejection fraction (LVEF) &lt;35% and QRS&gt;130 mseg. We aim to identify if baseline transthoracic echocardiographic (TTE) data can predict the need for defibrillation therapies in a primary prevention HF population referred for CRT implant. Methods We analyzed 119 consecutive HF patients in primary prevention referred for CRT implantation between 2004 and 2016. All patients underwent TTE before implantation. During a mean follow-up time of 58.4±33.9 months, all patients were evaluated with device interrogation every 6 months. In order to determine which parameters can predict sustained ventricular tachycardia (VT) or ventricular fibrillation (VF) occurrence, a multivariate analysis was performed including previous medical history, baseline laboratorial, electro and echocardiographic data. Results We included 86.6% (n=106) males, mean age at implant 65.5±9.7 years. A CRT-defibrillator (CRT-D) was implanted in 82 (64.2%) patients and a VT or VF was documented in 16 patients (13.4%). Baseline characteristics are presented in the table. After multivariate analysis, baseline right ventricular dysfunction (defined as TAPSE≤18mm) was an independent predictor for VT or VF occurrence (OR=4.83, 95CI 1.67–6.25, p=0.002). Baseline characteristics VT/VF (n=16) No VT/VF (n=103) P-value Age (years, mean ± SD) 62,4±7,9 66,2±9,5 0,135 Female gender (n, %) 2 (12,5) 14 (15,1) 0,572 Ischemic cardiomyopathy (n, %) 7 (43,8) 21 (22,6) 0,073 NYHA III + IV (n, %) 13 (81,3) 76 (73,8) 0,984 QRS width (mseg, mean ± SD) 168,5±19,5 177,6±22,2 0,142 Atrial fibrillation (n, %) 5 (31,3) 17 (18,3) 0,089 LVEF (%, mean ± SD) 24,6±4,8 26,3±6,3 0,302 TAPSE (mm, mean ± SD) 14,9±4,9 17,9±4,3 0,034 PASP (mmHg, mean ± SD) 48,5±9,4 43,5±14,6 0,419 Mitral regurgitation (moderate or greater, n, %) 5 (31,3) 24 (28,9) 0,077 iVol (mL/m2, mean ± SD) 134,2±36,6 128,7±44,1 0,690 CRT-D implantation (n, %) 16 (100,0) 60 (64,5) 0,002 Conclusion In a real-world HF population, right ventricular dysfunction defined by echocardiography was an independent predictor for VT or VF occurrence. This may help more accurate patient selection for CRT-D implantation.

Markers of ventricular repolarization as an additional non-invasive electrocardiography parameters for predicting ventricular tachycardia/fibrillation in patients with Brugada Syndrome – A systematic review and meta-analysis

BackgroundControversies surrounded the management of asymptomatic Brugada syndrome. Prognostication using electrophysiology study (EPS) is disputable. Non-invasive parameters may be a valuable additional tool for risk stratification. We aim to evaluate the use markers of ventricular repolarization including Tpeak-to-Tend (TpTe), Tpe Dispersion, TpTe/QT ratio, and QTc interval as additional non-invasive electrocardiography parameters for predicting ventricular tachycardia/fibrillation in patients with Brugada syndrome. MethodsWe performed a comprehensive search on TpTe, Tpe Dispersion, TpTe/QT ratio, and QTc interval as a predictor for ventricular tachycardia(VT)/fibrillation(VF)/aborted sudden cardiac death/appropriate ICD shock in patients with Brugada syndromes up until October 2018. ResultsWe included ten studies in the qualitative synthesis and eight studies in meta-analysis. There were a total of 2126 subjects from ten studies. We found that TpTe interval (mean difference 11.97 m s [5.02–18.91]; p < 0.001; I2 80% possibly on ≥80–100 m s and maximum QTc interval (mean difference 11.42 m s [5.90–16.93], p < 0.001; I2 28%) were the most potential ECG parameters to predict VT/VF/AT/SCD. Tpe dispersion and TpTe/QT ratio have a high heterogeneity. Upon sensitivity analysis, there is no single study found to markedly affect heterogeneity of Tpe dispersion and TpTe/QT ratio. Removal of a study reduced maximum QTc interval heterogeneity to 0%. ConclusionsMeasurement of TpTe interval, Tp-e dispersion, TpTe/QT ratio, and QTc interval on ECG emerge as a promising prognostication tool which needs further investigations with a more standardized method, outcome, and cut-off points. As for now, only maximum QTc interval has a reliable result with low heterogeneity sufficiently reliable for prognostication.

Ventricular tachycardia prediction in patients with implantable cardioverter-defibrillators for primary prevention of sudden cardiac death

Clinical data analysis of 83 patients with implantable cardioverter-defibrillators (ICDs) for sudden cardiac death (SCD) primary prevention has been done. We revealed 5 parameters associated with the detection of life-threatening ventricular arrhythmias. These parameters formed the basis for constructing a logistic regression model. The model makes it possible to obtain the probability of occurrence of a specific event depending on the severity of the predictive parameters and the degree of its influence (risk of true ventricular arrhythmias detection). Estimating the potential risk of the life-threatening arrhythmias, individual programming options are required in implantable cardioverter-defibrillators (ICDs) to reduce the amount of unnecessary electrotherapy, as well as more accurate monitoring of the patient's drug therapy.

Utility of 18-Fluorodeoxyglucose positron emission tomography in the prediction of ventricular tachycardia and advanced conduction disease in patients with cardiac sarcoidosis

Utility of 18-Fluorodeoxyglucose positron emission tomography in the prediction of ventricular tachycardia and advanced conduction disease in patients with cardiac sarcoidosis

Classification of ECG signals using cross-recurrence quantification analysis and probabilistic neural network classifier for ventricular tachycardia patients

Ventricular Tachycardia (VT) is one of the leading causes of sudden cardiac death in the world. Prediction of VT is usually diagnosed by using Electrocardiogram (ECG) and requires expeditious treatment which reduces the mortality rate. The cross recurrence plot (CRP) toolbox is used for computing the recurrence rate values for both (healthy and unhealthy) subjects and artificial neural network (ANN) toolbox in Matlab is used for generating the accurate results. Radial basis function neural network (RBFNN) is used for designing the probabilistic neural network classifier for discriminating the normal from abnormal (VT) signals based on the recurrence rate values. This paper illustrates the cross recurrence quantification analysis (CRQA) of ECG signals followed by the decomposition method using discrete wavelet transform (DWT) for the analysis of cardiac disorders with sensitivity, specificity of 98.5% and 97.6% respectively and overall accuracy achieved is 98.7%. This paper is useful in adopting automated approach for detecting the cardiac arrhythmias efficiently.