Search

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]

Ventricular dysfunction is common among patients with repaired cyanotic congenital heart disease. To date, no pharmacologic intervention has been demonstrated to be beneficial in this setting. To begin addressing this knowledge gap, we conducted a single-center prospective, randomized, open-label pilot study to investigate the effects of eplerenone on serologic markers of collagen turnover and inflammation, 6-minute walk distance, and quality of life in patients with tetralogy of Fallot (TOF) or transposition of the great arteries with a systemic right ventricle (transposition of the great arteries [TGA]). Patients were randomized to a 3-month drug-free period at the beginning of the treatment period or at the end. All patients received 12 months of eplerenone therapy during the treatment period. Twenty-six patients were enrolled in the trial; 17 completed the study protocol: 8 with TOF and 9 with TGV. Eplerenone had no effect on serum levels of procollagen 1 N-terminal peptide (PINP), procollagen 3 N-terminal peptide (PIIINP), or galectin-3 (G3). Similarly, eplerenone had no effect on 6-minute walk distance or quality of life. In conclusion, PINP and PIIINP levels are as high as or higher in patients with TOF and TGA than in patients with normal cardiac anatomy and heart failure, whereas G3 levels are lower. Eplerenone is well tolerated by adults born with congenital heart disease.