Key result
Machine learning analysis of resting ECG alternans identifies appropriate ICD therapy recipients with ~98% sensitivity.
Why the study?
LVEF lacks sensitivity and specificity as a risk index for primary prevention ICD implantation, motivating the search for additional risk indexes such as electrocardiographic alternans.
Does electrocardiographic alternans (ECGA) analysis improve risk stratification for appropriate ICD therapy in heart failure patients with primary prevention ICDs?
Observational (n=122)
No
Does electrocardiographic alternans (ECGA) analysis improve risk stratification for appropriate ICD therapy in heart failure patients with primary prevention ICDs?
Answers are grounded in this paper and cite their sources.
Resting electrocardiographic alternans analyzed via machine learning can accurately identify heart failure patients at high risk for arrhythmias, potentially improving patient selection for primary prevention ICDs beyond LVEF alone.
The current Guidelines recommend implantable cardioverter defibrillator (ICD) for primary prevention of sudden cardiac death (SCD) when left ventricular ejection fraction (LVEF) is reduced. Nevertheless, LVEF lacks sensitivity and specificity as a risk index, meaning that additional risk indexes are needed. Electrocardiographic alternans (ECGA) is the every-other-beat morphology oscillation in either ECG wave: P-wave/QRS-complex/T-wave alternans (PWA/QRSA/TWA, respectively). This study aims to investigate ECGA as an additional criterion to decide for ICD implantation for primary prevention of SCD. ECGs were acquired during a bicycle-ergometer test in a heart-failure population having ICDs for primary prevention. During follow-up, patients were classified into cases, if device therapy was administered, and controls, if no device therapy occurred. Resting and exercise ECGs were analyzed using the enhanced adaptive matched filter method (EAMFM) to identify ECGA. Unlike the exercise condition, the resting condition showed a statistically significant difference in PWA and QRSA between cases and controls. Thus, to classify them, rest-related ECGA features were used to feed a support vector machine (SVM), validated by a leave-one-out cross-validation algorithm. SVM yielded a sensitivity, specificity, and F1 score of 98.49%, 83.33%, and 95.61%, respectively. These results suggest that EAMFM-derived ECGA may act as a further useful feature to stratify the arrhythmia risk, overcoming the insufficient sensitivity and specificity of LVEF only. Thus, the main contribution of this study is the proposal of an additional ECGA-based criterion for identifying patients who may benefit from primary prevention ICD implantation paving the way for a conceivable revision of the current Guidelines.
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Marcantoni et al. (2025) conducted an observational in Heart failure with reduced ejection fraction (n=122). Rest-related electrocardiographic alternans (ECGA) vs. Absence of significant ECGA was evaluated on Classification of patients requiring appropriate ICD therapy (cases) versus those who did not (controls) using a support vector machine (Sensitivity 98.49%, Specificity 83.33%). Rest-related electrocardiographic alternans features analyzed via a support vector machine classified heart failure patients who appropriately received ICD therapy with 98.49% sensitivity and 83.33% specificity.
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