BackgroundThe aim of this study was to investigate the different substrate characteristics of repetitive premature ventricular complexed (PVC) trigger sites by the non-contact mapping (NCM).MethodsThirty-five consecutive patients, including 14 with arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC) and 21 with idiopathic right ventricular outflow tract tachycardia (RVOT VT), were enrolled for electrophysiological study and catheter ablation guided by the NCM. Substrate and electrogram (Eg) characteristics of the earliest activation (EA) and breakout (BO) sites of PVCs were investigated, and these were confirmed by successful PVC elimination.ResultsOverall 35 dominant focal PVCs were identified. PVCs arose from the focal origins with preferential conduction, breakout, and spread to the whole right ventricle. The conduction time and distance from EA to BO site were both longer in the ARVC than the RVOT group. The conduction velocity was similar between the 2 groups. The negative deflection of local unipolar Eg at the EA site (EA slope3,5,10ms values) was steeper in the RVOT, compared to ARVC patients. The PVCs of ARVC occurred in the diseased substrate in the ARVC patients. More radiofrequency applications were required to eliminate the triggers in ARVC patients.Conclusions/InterpretationThe substrate characteristics of PVC trigger may help to differentiate between idiopathic RVOT VT and ARVC. The slowing and slurred QS unipolar electrograms and longer distance from EA to BO in RVOT endocardium suggest that the triggers of ARVC may originate from mid- or sub-epicardial myocardium. More extensive ablation to the trigger site was required in order to create deeper lesions for a successful outcome.
Background:
Concealed left ventricular hypertrophy (LVH) is a prevalent condition that is correlated with a substantial risk of cardiovascular events and mortality, especially in young to middle-aged adults. Early identification of LVH is warranted. In this work, we aimed to develop an artificial intelligence (AI)–enabled model for early detection and risk stratification of LVH using 12-lead ECGs.
Methods:
By deep learning techniques on the ECG recordings from 28 745 patients (20–60 years old), the AI model was developed to detect verified LVH from transthoracic echocardiography and evaluated on an independent cohort. Two hundred twenty-five patients from Japan were externally validated. Cardiologists’ diagnosis of LVH was based on conventional ECG criteria. The area under the curve (AUC), sensitivity, and specificity were applied to evaluate the model performance. A Cox regression model estimated the independent effects of AI-predicted LVH on cardiovascular or all-cause death.
Results:
The AUC of the AI model in diagnosing LVH was 0.89 (sensitivity: 90.3%, specificity: 69.3%), which was significantly better than that of the cardiologists’ diagnosis (AUC, 0.64). In the second independent cohort, the diagnostic performance of the AI model was consistent (AUC, 0.86; sensitivity: 85.4%, specificity: 67.0%). After a follow-up of 6 years, AI-predicted LVH was independently associated with higher cardiovascular or all-cause mortality (hazard ratio, 1.91 [1.04–3.49] and 1.54 [1.20–1.97], respectively). The predictive power of the AI model for mortality was consistently valid among patients of different ages, sexes, and comorbidities, including hypertension, diabetes, stroke, heart failure, and myocardial infarction. Last, we also validated the model in the international independent cohort from Japan (AUC, 0.83).
Conclusions:
The AI model improved the detection of LVH and mortality prediction in the young to middle-aged population and represented an attractive tool for risk stratification. Early identification by the AI model gives every chance for timely treatment to reverse adverse outcomes.
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