Anticoagulation in patients with heart failure and normal sinus rhythm

2009 ◽  
Vol 66 (2) ◽  
pp. 134-141 ◽  
Author(s):  
Toni L. Ripley ◽  
Edith Nutescu
Keyword(s):  
Heart Failure ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Patients With Heart Failure

Acute Effects of Biventricular Pacing in Heart Failure Patients with a Normal Ejection Fraction and Mechanical Dyssynchrony

Cardiology ◽  
2015 ◽  
Vol 130 (2) ◽  
pp. 112-119 ◽  
Author(s):  
Yi-Chih Wang ◽  
Chih-Chieh Yu ◽  
Fu-Chun Chiu ◽  
Vincent Splett ◽  
Ruth Klepfer ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Biventricular Pacing ◽  
Mechanical Dyssynchrony ◽  
Left Ventricular ◽  
Acute Effects ◽  
Heart Failure Patients ◽  
Normal Sinus

Objectives: We tested the acute effects of resynchronization in heart failure patients with a normal (>50%) left ventricular (LV) ejection fraction (HFNEF) and mechanical dyssynchrony. Methods: Twenty-four HFNEF patients (72 ± 6 years, 5 male) with mechanical dyssynchrony (standard deviation of electromechanical time delay among 12 LV segments >35 ms) were studied with temporary pacing catheters in the right atrium, LV, and right ventricle (RV), and high-fidelity catheters for pressure recording. Using selected atrioventricular (AV) intervals of 60, 90, 120, 150, and 180 ms to optimize transmitral flow during simultaneous biventricular pacing, the RV-LV (VV) interval was then evaluated at RV30, RV15, 0, LV15, LV30, and LV45 (RV or LV indicates which ventricle was paced first, the number indicates by how many ms). Results: During simultaneous pacing, longer AV intervals were associated with improved LV pressure-derivative minimums and increased aortic pressures (p < 0.05 vs. normal sinus rhythm). In the VV interval from RV30 to LV45, there was a graded increase in the aortic velocity time integral and a decrease in dyssynchrony during simultaneous or LV-first pacing (p < 0.05 vs. normal sinus rhythm). Conclusions: For HFNEF patients with mechanical dyssynchrony, acute simultaneous biventricular or LV-first pacing with longer AV intervals reduced mechanical dyssynchrony and improved diastolic and systolic hemodynamics.

Nonlinear Characterization of Heart Rate Variability in Normal Sinus Rhythm, Atrial Fibrillation and Congestive Heart Failure

2016 ◽  
Author(s):  
Syed Hassan Zaidi ◽  
Imran Akhtar ◽  
Syed Imran Majeed ◽  
Tahir Zaidi ◽  
Muhammad Saif Ullah Khalid
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Sinus Rhythm ◽  
Time Series Data ◽  
Normal Sinus Rhythm ◽  
Series Data ◽  
Normal Sinus

This paper highlights the application of methods and techniques from nonlinear analysis to illustrate their far superior capability in revealing complex cardiac dynamics under various physiological and pathological states. The purpose is to augment conventional (time and frequency based) heart rate variability analysis, and to extract significant prognostic and clinically relevant information for risk stratification and improved diagnosis. In this work, several nonlinear indices are estimated for RR intervals based time series data acquired for Healthy Sinus Rhythm (HSR) and Congestive Heart Failure (CHF), as the two groups represent different cases of Normal Sinus Rhythm (NSR). In addition to this, nonlinear algorithms are also applied to investigate the internal dynamics of Atrial Fibrillation (AFib). Application of nonlinear tools in normal and diseased cardiovascular states manifest their strong ability to support clinical decision support systems and highlights the internal complex properties of physiological time series data such as complexity, irregularity, determinism and recurrence trends in cardiovascular regulation mechanisms.

scholarly journals ECG Classification using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 2492-2494
Keyword(s):  
Heart Failure ◽  
Cardiovascular Disease ◽  
Congestive Heart Failure ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Original Data ◽  
Ecg Signals ◽  
Normal Sinus ◽  
Electrocardiogram Signals

Recently, the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a important part in the clinical diagnosis of cardiovascular disease. Convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features using wavelet .The classification of ARR (Arrhythmia), CHF (Congestive Heart Failure), and NSR (Normal Sinus Rhythm) signals. The experimental results contains on ARR signals from the MIT-BIH arrhythmia,CHF signals from the BIDMC Congestive Heart Failure and NSR signals from the MIT-BIH Normal Sinus Rhythm Databases show that the proposed method achieves a promising classification accuracy of 90.63%, significantly outperforming several typical ECG classification methods.

scholarly journals Bucindolol Decreases Atrial Fibrillation Burden in Patients with Heart Failure and the ADRB1 Arg389Arg Genotype

2021 ◽  
Author(s):  
Jonathan P. Piccini ◽  
Christopher Dufton ◽  
Ian A. Carroll ◽  
Jeff S. Healey ◽  
William T. Abraham ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Left Ventricular ◽  
Rhythm Control ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Normal Sinus

Background - Bucindolol is a genetically targeted β-blocker/mild vasodilator with the unique pharmacologic properties of sympatholysis and ADRB1 Arg389 receptor inverse agonism. In the GENETIC-AF trial conducted in a genetically defined heart failure (HF) population at high risk for recurrent atrial fibrillation (AF), similar results were observed for bucindolol and metoprolol succinate for the primary endpoint of time to first atrial fibrillation (AF) event; however, AF burden and other rhythm control measures were not analyzed. Methods - The prevalence of ECGs in normal sinus rhythm, AF interventions for rhythm control (cardioversion, ablation and antiarrhythmic drugs), and biomarkers were evaluated in the overall population entering efficacy follow-up (N=257). AF burden was evaluated for 24 weeks in the device substudy (N=67). Results - In 257 patients with HF the mean age was 65.6 ± 10.0 years, 18% were female, mean left ventricular ejection fraction (LVEF) was 36%, and 51% had persistent AF. Cumulative 24-week AF burden was 24.4% (95% CI: 18.5, 30.2) for bucindolol and 36.7% (95% CI: 30.0, 43.5) for metoprolol (33% reduction, p < 0.001). Daily AF burden at the end of follow-up was 15.1% (95% CI: 3.2, 27.0) for bucindolol and 34.7% (95% CI: 17.9, 51.2) for metoprolol (55% reduction, p < 0.001). For the metoprolol and bucindolol respective groups the prevalence of ECGs in normal sinus rhythm was 4.20 and 3.03 events per patient (39% increase in the bucindolol group, p < 0.001), while the rate of AF interventions was 0.56 and 0.82 events per patient (32% reduction for bucindolol, p = 0.011). Reductions in plasma norepinephrine (p = 0.038) and NT-proBNP (p = 0.009) were also observed with bucindolol compared to metoprolol. Conclusions - Compared with metoprolol, bucindolol reduced AF burden, improved maintenance of sinus rhythm, and lowered the need for additional rhythm control interventions in patients with HF and the ADRB1 Arg389Arg genotype.

Robust correlation dimension estimator for heart rate variability

2020 ◽  
Vol 15 (16) ◽  
pp. 62-68
Author(s):  
A.V. Martynenko ◽  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Congestive Heart Failure ◽  
Sinus Rhythm ◽  
Correlation Dimension ◽  
Normal Sinus Rhythm ◽  
Robust Estimator ◽  
Normal Sinus ◽  
Significant Difference ◽  
Limited Length

Introduction. Non-linear methods of analysis have found widespread use in the Heart Rate Variability (HRV) technology, when the long-term HRV records are available. Using one of the effective nonlinear methods of analysis of HRV correlation dimension D2 for the standard 5-min HRV records is suppressed by unsatisfactory accuracy of available methods in case of short records (usually, doctors have about 500 RRs during standard 5-min HRV record), as well as complexity and ambiguity of choosing additional parameters for known methods of calculating D2. The purpose of the work. Building a robust estimator for calculating correlation dimension D2 with high accuracy for limited se-ries of RR-intervals observed in a standard 5-minute HRV record, i. e. with N  500. As well as demonstrating the capabilities of the D2 formula on a well known attractors (Lorenz, Duffing, Hennon and etc.) and in applications for Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF) and Atrial Fibrillation (AF). Materials and Methods. We used MIT-BIH long-term HRV records for normal sinus rhythm, congestive heart failure and atrial fibrillation. In order to analyze the accuracy of new robust estimator for D2, we used the known theoretical values for some famous attractors (Lorenz, Duffing, Hennon and etc.) and the most popular Grassberger-Procaccia (G-P) algorithm for D2. The results of the study. We have shown the effectiveness of the developed D2 formula for time series of limited length (N = 500–1000) by some famous attractors (Lorenz, Duffing, Hennon and etc.) and with the most popular Grassberger-Procaccia (G-P) algorithm for D2. It was demonstrated statistically significant difference of D2 for normal sinus rhythm and congestive heart failure by standard 5 min HRV segments from MIT-BIH database. The promised technology for early prediction of atrial fibrillation episodes by current D2 algorithm was shown for standard 5 min HRV segments from MIT-BIH Atrial Fibrillation database. Conclusion. Robust correlation dimension D2 estimator suggested in the article allows for time series of limited length (N ≈ 500) to calculate D2 value that differs at mean from a precise one by 5 ± 4%, as demonstrated for various well known attractors (Lorenz, Duffing, Hennon and etc.). We have shown on the standard 5-min segments from MIT-BIH database of HRV records: - the statistically significant difference of D2 for cases of normal sinus rhythm and congestive heart failure; - D2 drop significantly for the about 30 min. before of AF and D2 growth drastically under AF there was shown for HRV records with Atrial Fibrillation (AF) episodes. The suggested robust correlation dimension D2 estimator is perfect suitable for real time HRV monitoring as accurate, fast and non-consuming for computing resources. Key words: Hearth rate variability; Correlation dimension; Congestive heart failure; Atrial fibrillation.

Racial Variations in the Incidence and Outcomes of Stroke in Chronic Heart Failure Patients with Normal Sinus Rhythm

2008 ◽  
Vol 14 (6) ◽  
pp. S84
Author(s):  
Syed A. Agha ◽  
Grigorios Giamouzis ◽  
Marjan Mujib ◽  
James O. Ekundayo ◽  
Javed Butler ◽  
...  
Keyword(s):  
Heart Failure ◽  
Chronic Heart Failure ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Heart Failure Patients ◽  
Normal Sinus ◽  
Racial Variations
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