scholarly journals Prediction of Ventricular Arrhythmias in Patients at Risk of Sudden Cardiac Death

10.5772/20670 ◽  
2011 ◽  
Author(s):  
K.H. Haugaa ◽  
J.P. Amlie ◽  
T. Edvardse
Keyword(s):  
At Risk ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Ventricular Arrhythmias ◽  
Patients At Risk

Abstract 15714: Prediction of Sudden Cardiac Death With Automated High-Throughput Analysis of T-Wave Heterogeneity in Standard 12-Lead Electrocardiogram

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Tuomas Kenttä ◽  
Bruce D Nearing ◽  
Kimmo Porthan ◽  
Jani T Tikkanen ◽  
Matti Viitasalo ◽  
...  
Keyword(s):  
At Risk ◽  
Relative Risk ◽  
Sudden Cardiac Death ◽  
General Population ◽  
Cardiac Death ◽  
Left Ventricular ◽  
Adjusted Relative Risk ◽  
T Wave ◽  
Increased Risk ◽  
Patients At Risk

Introduction: Noninvasive identification of patients at risk for sudden cardiac death (SCD) remains a major clinical challenge. Abnormal ventricular repolarization is associated with increased risk of lethal ventricular arrhythmias and SCD. Hypothesis: We investigated the hypothesis that spatial repolarization heterogeneity can identify patients at risk for SCD in general population. Methods: Spatial R-, J- and T-wave heterogeneities (RWH, JWH and TWH, respectively) were automatically analyzed with second central moment technique from standard digital 12-lead ECGs in 5618 adults (46% men; age 50.9±12.5 yrs.) who took part in Health 2000 Study, an epidemiological survey representative of the entire Finnish adult population. During average follow-up of 7.7±1.4 years, a total of 72 SCDs occurred. Thresholds of RWH, JWH and TWH were based on optimal cutoff points from ROC curves. Results: Increased RWH, JWH and TWH (Fig.1) in left precordial leads (V4-V6) were univariately associated with SCD (P<0.001, each). When adjusted with clinical risk markers (age, gender, BMI, systolic blood pressure, cholesterol, heart rate, left ventricular hypertrophy, QRS duration, arterial hypertension, diabetes, coronary heart disease and previous myocardial infarction) JWH and TWH remained as independent predictors of SCD. Increased TWH (≥102μV) was associated with a 1.9-fold adjusted relative risk (95% confidence interval [CI]: 1.2 - 3.1; P=0.011) and increased JWH (≥123μV) with a 2.0-fold adjusted relative risk for SCD (95% CI: 1.2 - 3.3; P=0.004). When both TWH and JWH were above threshold, the adjusted relative risk for SCD was 3.2-fold (95% CI: 1.7 - 6.2; P<0.001). When all heterogeneity measures (RWH, JWH and TWH) were above threshold, the risk for SCD was 3.7-fold (95% CI: 1.6 - 8.6; P=0.003). Conclusions: Automated measurement of spatial J- and T-wave heterogeneity enables analysis of high patient volumes and is able to stratify SCD risk in general population.

The pharmacologic approach to patients at risk of sudden cardiac death

2006 ◽  
pp. 57-74
Author(s):  
Giuseppe Boriani ◽  
Cinzia Valzania ◽  
Mauro Biffi ◽  
Cristian Martignani ◽  
Igor Diemberger ◽  
...  
Keyword(s):  
At Risk ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Patients At Risk

Ventricular Arrhythmias: Antiadrenergic Therapy for the Patient with Coronary Artery Disease

2005 ◽  
Vol 10 (4_suppl) ◽  
pp. S23-S31 ◽  
Author(s):  
Stefan H. Hohnloser
Keyword(s):  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Ventricular Arrhythmias ◽  
Clinical Evidence ◽  
Synergistic Effects ◽  
Implantable Cardioverter Defibrillators ◽  
Patients At Risk ◽  
Adrenergic Blockers ◽  
Artery Disease ◽  
Cardioverter Defibrillators

Patients who have had a recent myocardial infarction (MI) are at high risk of ventricular arrhythmias that often cause sudden cardiac death. It is believed that sympathetic overactivity in the peri-infarction period may alter the electrophysiology and structure of the myocardium, thus placing these patients at risk of developing rhythm disturbances. A number of pharmacologic and nonpharmacologic therapies have been shown to reduce the risk of post-MI mortality, including sudden cardiac death. β-Adrenergic blockers are recommended for all post-MI patients without contraindications because of overwhelming clinical evidence of their benefit in reducing mortality in this patient population. Recent clinical trials of implantable cardioverter defibrillators have provided compelling support that they are effective in both the primary and secondary prevention of sudden cardiac death. In addition, several studies have shown that combination therapy with β-blockers and implantable cardioverter defibrillators have synergistic effects that optimize the benefits of both therapies.

scholarly journals Identifying Mitral Valve Prolapse at Risk for Ventricular Arrhythmias and Myocardial Fibrosis from 12-lead Electrocardiograms using Deep Learning

2021 ◽  
Author(s):  
Geoffrey H Tison ◽  
Sean Abreau ◽  
Lisa Lim ◽  
Valentina Crudo ◽  
Joshua Barrios ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Cardiac Arrest ◽  
Sudden Cardiac Death ◽  
Mitral Valve ◽  
Mitral Regurgitation ◽  
Myocardial Fibrosis ◽  
Mitral Valve Prolapse ◽  
Cardiac Death ◽  
Ventricular Arrhythmias

Background: Mitral valve prolapse (MVP) is a common valvulopathy, with a subset of MVP patients developing sudden cardiac death or cardiac arrest. Complex ventricular ectopy (ComVE) represents a marker of arrhythmic risk that is associated with myocardial fibrosis and increased mortality in MVP. We hypothesize that an ECG-based machine-learning model can identify MVP with ComVE and/or myocardial fibrosis on cardiac magnetic resonance (CMR) imaging. Methods: A deep convolutional neural network (CNN) was trained to detect ComVE using 6,916 12-lead ECGs from 569 MVP patients evaluated at the University of California San Francisco (UCSF) between 2012 and 2020. A separate CNN was also trained to detect late gadolinium enhancement (LGE) using 87 ECGs from MVP patients with contrast CMR. Results: The prevalence of ComVE was 160/569 or 28% (20 patients or 3% had cardiac arrest or sudden cardiac death). The area under the curve (AUC) of the CNN to detect ComVE was 0.81 (95% CI, 0.78-0.84). AUC remained high even after excluding patients with moderate-severe mitral regurgitation (MR) [0.80 (95% CI, 0.77-0.83)], or with bileaflet MVP [0.81 (95% CI, 0.76-0.85)]. The top ECG segments able to discriminate ComVE vs no ComVE were related to ventricular depolarization and repolarization (early-mid ST and QRS fromV1, V3, and III). LGE in the papillary muscles or basal inferolateral wall was present in 21 (24%) of 87 patients with available CMR. The AUC for detection of LGE was 0.75 (95% CI, 0.68-0.82). Conclusions: Standard 12-lead ECGs analyzed with machine learning can detect MVP at risk for ventricular arrhythmias and fibrosis and can identify novel ECG correlates of arrhythmic risk regardless of leaflet involvement or mitral regurgitation severity. ECG-based CNNs may help select those MVP patients requiring closer follow-up and/or a CMR. 

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