DeSimone, Christopher V.

Background: Non-ischemic cardiomyopathies can be primary (p-NICM) which involves mainly the heart muscle or caused by systemic disease with heart muscle involvement as it is seen in systemic infiltrative diseases (i-NICM). There is limited data on the safety and in-hospital outcomes of ventricular tachycardia (VT) ablation in i-NICM. Methods: We identified patients who underwent VT ablation between 2018-2021 using the National Inpatient Sample Database. i-NICM include amyloidosis, hemochromatosis, or sarcoidosis. In-hospital complications include mortality, acute kidney injury (AKI), acute heart failure (AHF), bleeding, vasopressor use, blood transfusion and cardiac tamponade. Multivariate logistic regression analyses were performed. Results : There were 7,420 VT ablations, 7,235 had P-NICM and 185 had i-NICM. A multivariable analysis did not reveal any difference in mortality (adjusted odds ratio [aOR]: 0.81; 95% confidence interval 0.32-1.98; p=0.62), AHF aOR: 0.88 (0.41-1.81; p=0.69, AKI aOR:1.17 (0.83-1.65); p=0.36, blood transfusion aOR: 0.63 (0.25-1.59); p=0.33, vasopressor aOR: 0.86( 0.51-1.49); p=0.61 or pericardial effusion/ tamponade aOR: 1.16 (0.67-2.1); p=0.58 Figure Conclusion: Patients with i-NICM had similar in-hospital complications compared to P-NICM further supporting the safety of VT ablation in managing recurrent VT in patients with i-NICM.

Abstract Background Accurate automated wide QRS complex tachycardia (WCT) discrimination between ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using novel calculations derived from computerized electrocardiogram (ECG) data from paired WCT and baseline ECGs. Purpose Our aim was to develop and trial novel WCT discrimination approaches for WCT patients with and without a corresponding baseline ECG. Central to this analysis was the creation and use of a novel parameter (i.e., percent monophasic time-voltage area [PMonoTVA] [%]) that may be derived from computerized ECG measurements present on the WCT ECG alone. Methods In a two-part study, we derived and tested WCT differentiation models comprised of novel and previously established parameters formulated from computerized data of paired WCT and baseline ECGs. In Part 1, novel and established parameters generated from WCT and baseline ECG data were used to derive, validate, and compare five different binary classification models: (i) logistic regression [LR], (ii) artificial neural network [ANN], (iii) Random Forests [RF], (iv) support vector machine [SVM], and (v) ensemble learning (EL). In Part 2, two unique LR models were derived, validated, and compared using parameters generated from computerized data of the (i) WCT ECG alone (i.e., Solo Model) and (ii) paired WCT and baseline ECGs (i.e., Paired Model). Results In Part 1, among 103 patients with VT or SWCT diagnoses established from corroborating electrophysiology studies or intra-cardiac device recordings, favorable diagnostic performance was achieved by all modeling technique subtypes: LR (area under the receiver operating characteristic curve [AUC] 0.95), ANN (AUC 0.91), RF (AUC 0.97), SVM (AUC 0.98), and EL (AUC 0.97). In Part 2, among 235 patients with a VT or SWCT diagnosis established with (Gold Standard cohort) or without (Non-Gold Standard cohort) a corroborating electrophysiology procedure or intra-cardiac device recording, favorable diagnostic performance was achieved by the Solo Model (AUC 0.86) and Paired Model (AUC 0.95) (Table). Conclusion Accurate WCT discrimination may be accomplished using novel parameters derived from computerized data of the WCT ECG alone and paired WCT and baseline ECGs. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health