Effect of propranolol versus no antiarrhythmic drug on sudden cardiac death, total cardiac death, and total death in patients ≥62 years of age with heart disease, complex ventricular arrhythmias, and left ventricular ejection fraction ≥40%

1994 ◽  
Vol 74 (3) ◽  
pp. 267-270 ◽  
Author(s):  
Wilbert S. Aronow ◽  
Chul Ahn ◽  
Anthony D. Mercando ◽  
Stanley Epstein ◽  
Itzhak Kronzon
Keyword(s):  
Heart Disease ◽  
Sudden Cardiac Death ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Cardiac Death ◽  
Ventricular Arrhythmias ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Total Death

New techniques for risk assessment in sudden cardiac death

ESC CardioMed ◽  
2018 ◽  
pp. 2327-2330
Author(s):  
Juan Fernandez-Armenta ◽  
Antonio Berruezo ◽  
Juan Acosta ◽  
Diego Penela
Keyword(s):  
Risk Assessment ◽  
Sudden Cardiac Death ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Cardiac Death ◽  
Left Ventricular ◽  
Ventricular Ejection ◽  
Left Ventricular Ejection ◽  
Individual Risk ◽  
Ventricular Ejection Fraction

Risk stratification for sudden cardiac death (SCD) is one of the main objectives of clinical arrhythmology. Despite increased knowledge of the fundamental basis and predictors of SCD, the estimation of individual risk remains challenging. To date, symptomatic heart failure and reduced left ventricular ejection fraction are the main variables used to identify patients at high risk of SCD who could potentially benefit from preventive therapies. Beyond left ventricular ejection fraction, new diagnostic tools have been proposed to better stratify patients at risk of SCD. Among them, cardiovascular magnetic resonance imaging, which allows direct visualization of the arrhythmogenic substrate, is considered particularly promising. Genetic testing and serum biomarkers may also have a role in SCD risk assessment.

Abstract 16416: Wide QRS-T Angle on the 12-lead ECG as a Predictor of Sudden Death Beyond the LV Ejection Fraction

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Kelvin C Chua ◽  
Carmen Teodorescu ◽  
Audrey Uy-Evanado ◽  
Kyndaron Reinier ◽  
Kumar Narayanan ◽  
...  
Keyword(s):  
Sudden Cardiac Death ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Cardiac Death ◽  
Left Ventricular ◽  
Ventricular Ejection ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Increased Risk ◽  
Lv Ejection Fraction

Introduction: If we are to improve risk stratification for sudden cardiac death (SCD) we should extend beyond the LV ejection fraction (LVEF). The frontal QRS-T angle has been shown to predict risk of SCD but its value independent of LVEF has not been investigated. Hypothesis: We hypothesize that a wide frontal QRS-T angle predicts SCD independent of LVEF. Methods: Cases of adult sudden cardiac arrest with an available electrocardiogram before the event were identified from a large ongoing population based study of SCD in the Northwest US (population approx. one million). Subjects with a computable frontal QRS-T angle were included. A total of 686 SCD cases (mean age 67.4 years; 95% CI, 52.5 to 82.3 years; 68.2% males; 83.5% whites) met criteria, and were compared to 871 controls with and without coronary artery disease (mean age 66.8 years, 55.3 to 78.3 years; 67.7% males; 90.6% whites) from the same geographical region. Results: The mean frontal QRS-T angle was higher in SCD cases (73.9 degrees; 95% CI, 17.5 to 130.3 degrees, p<0.0001) compared to controls (51.1 degrees; 95% CI 5.0 to 97.2 degrees). Using a cut-off of more than 90 degrees, the frontal QRS-T angle was predictive of SCD, and remained predictive, after adjusting for age, sex, left ventricular ejection fraction (LVEF), prolonged QTc, prolonged QRS duration and baseline comorbidities (OR 1.80; 95% CI, 1.27 to 2.55, p=0.001). On the receiver operating characteristic (ROC) curve, the QRS-T angle demonstrated an area-under-curve (AUC) value of 0.614. Compared to the lowest quartile of QRS-T angle, the highest quartile had nearly a triple increase in the risk of SCD (OR 2.71; 95% CI; 2.03 to 3.60; p<0.0001). Conclusion: A wide QRS-T angle greater than 90 degrees is associated with increased risk of sudden cardiac death independent of left ventricular ejection fraction.

scholarly journals Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China

BMJ Open ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. e023724 ◽  
Author(s):  
Fanqi Meng ◽  
Zhihua Zhang ◽  
Xiaofeng Hou ◽  
Zhiyong Qian ◽  
Yao Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Sudden Cardiac Death ◽  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Study Protocol ◽  
Cardiac Death ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction

IntroductionLeft ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF.Methods and analysisWe will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study.Ethics and disseminationThe study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences.Trial registration numberChiCTR-POC-17011842; Pre-results.

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