12 resultados para Arritmia ventricular compleja

em Deakin Research Online - Australia


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The isolated Langendorff-mode perfused heart has become a valuable experimental model, used extensively to examine cardiac function, pathophysiology and pharmacology. For the clinical cardiologist an ECG is often a simple practicality, however in experimental circumstances, particularly with ex vivo murine hearts it is not always possible to obtain an ECG due to experimental recording constraints. However, the mechanical record of ventricular contractile function can be highly informative in relation to electrical state. It is difficult though to achieve consistency in these evaluations of arrhythmia as a validated common reference framework is lacking. In 1988, a group of investigators developed the ‘Lambeth Conventions’—a standardised reference for the definition and classification of arrhythmias in animal experimental models of ischaemia, infarction and reperfusion in vivo. Now, two decades later it is timely to revisit the Lambeth Conventions, and to update the guidelines in the context of the marked increase in murine heart study in experimental cardiac pathophysiology. Here we describe an adjunct to the Lambeth Conventions for the reporting of ventricular arrhythmias post-ischaemia in ex vivo mouse hearts when ECG recordings are not employed. Of seven discrete and identifiable patterns of mechanical dysrhythmia observed in reperfusion, five could be classified using conventional ECG terminology: ventricular premature beat, bigeminy, trigeminy, ventricular tachycardia and ventricular fibrillation. Two additional rhythm variations detected from the pressure record are described (potentiated contraction and alternans).

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This is the protocol for a review and there is no abstract. The objectives are as follows:

To assess the effects of nurse-led titration of ACEIs, beta-adrenergic blocking agents and ARBs in patients with left ventricular systolic dysfunction in terms of safety and patient outcomes.

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Ventricular repolarization dynamics is an important predictor of the outcome in cardiovascular diseases. Mathematical modeling of the heart rate variability (RR interval variability) and ventricular repolarization variability (QT interval variability) is one of the popular methods to understand the dynamics of ventricular repolarization. Although ECG derived respiration (EDR) was previously suggested as a surrogate of respiration, but the effect of respiratory movement on ventricular repolarization dynamics was not studied. In this study, the importance of considering the effect of respiration and the validity of using EDR as a surrogate of respiration for linear parametric modeling of ventricular repolarization variability is studied in two cases with different physiological and psychological conditions. In the first case study, we used 20 young and 20 old healthy subjects’ ECG and respiration data from Fantasia database at Physionet to analyze a bivariate QT–RR and a trivariate QT–RR–RESP or QT–RR–EDR model structure to study the aging effect on cardiac repolarization variability. In the second study, we used 16 healthy subjects’ data from drivedb (stress detection for automobile drivers) database at Physionet to do the same analysis for different psychological condition (i.e., in stressed and no stress condition). The results of our study showed that model having respiratory information (QT–RR–RESP and QT–RR–EDR) gave significantly better fit value (p < 0.05) than that of found from the QT–RR model. EDR showed statistically similar (p > 0.05) performance as that of respiration as an exogenous model input in describing repolarization variability irrespective of age and different mental conditions. Another finding of our study is that both respiration and EDR-based models can significantly (p < 0.05) differentiate the ventricular repolarization dynamics between healthy subjects of different age groups and with different psychological conditions, whereas models without respiration or EDR cannot distinguish between the groups. These results established the importance of using respiration and the validity of using EDR as a surrogate of respiration in the absence of respiration signal recording in linear parametric modeling of ventricular repolarization variability in healthy subjects.

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This study will examine the effects of combined aerobic and resistance training on left ventricular remodeling in diabetic patients with diastolic dysfunction. This is the first randomized controlled trial to look for effects of combined strength training and aerobic exercise on myocardial function as well as other clinical, functional, or psychological parameters in diabetic patients with isolated diastolic dysfunction, and will provide important insights into the potential management strategies for heart failure with preserved ejection fraction.

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Classification of electrocardiogram (ECG) data stream is essential to diagnosis of critical heart conditions. It is vital to accurately detect abnormality in the ECG in order to prevent possible beginning of life-threatening cardiac symptoms. In this paper, we focus on identifying premature ventricular contraction (PVC) which is one of the most common heart rhythm abnormalities. We use "Replacing" strategy to check the effects of each individual heartbeat on the variation of principal directions. Based on this idea, an online PVC detection method is proposed to classify the new arriving PVC beats in the real-time and online manner. The proposed approach is tested on the MIT-BIH arrhythmia database (MIT-BIH-AR). The PVC detection accuracy was 98.77%, with the sensitivity and positive predictivity of 96.12% and 86.48%, respectively. These results are an improvement on previous reported results for PVC detection. In addition, our proposed method is effective in terms of computation time. The average execution time of our proposed method was 3.83 s for a 30 min ECG recording. It shows the capability of the classifier to detect abnormal PVCs in online manner.

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Short telomeres are associated with increased risk of cardiovascular disease. Here we studied cardiomyocyte telomere length at key ages during the ontogeny of cardiac hypertrophy and failure in the hypertrophic heart rat (HHR), and compared these with the normal heart rat (NHR) control strain. Key ages corresponded with the pathophysiological sequence beginning with fewer cardiomyocytes (2-days), leading to left ventricular hypertrophy (LVH) (13-weeks) and subsequently progression to heart failure (38-weeks). We measured telomere length, tissue activity of telomerase, mRNA levels of telomerase reverse transcriptase (Tert) and telomerase RNA component (Terc), and expression of the telomeric regulator microRNA miR-34a. Cardiac telomere length was longer in the HHR compared to the control strain at 2-days and 38-weeks, but shorter at 13-weeks. Neonatal HHR had higher cardiac telomerase activity and expression of Tert and miR-34a. Telomerase activity was not different at 13- or 38-weeks. Tert mRNA and Terc RNA were over-expressed at 38-weeks, while miR-34a was over-expressed at 13-weeks but down-regulated at 38-weeks. Circulating leukocytes were strongly correlated with cardiac telomere length in the HHR only. The longer neonatal telomeres in HHR are likely to reflect fewer foetal and early postnatal cardiomyocyte cell divisions and explain the reduced total cardiomyocyte complement that predisposes to later hypertrophy and failure. Although shorter telomeres were a feature of cardiac hypertrophy at 13-weeks, they were not present at the progression to heart failure at 38-weeks.

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Ventricular repolarization(VR) characteristics is affected by ageing alongside several other factors like Heart rate(HR),respiration, modulation of autonomic nervous system, different drug effects, genetical factors affecting the cardiac ion channel characteristics, gender etc. Therefore, total VR variability (i.e. QT interval variability in surface ECG) consists of two components: one dependent on HR variability (HRV) and another independent of HRV. Analysis of QT interval variability (QTV) is crucial for both healthy and pathological conditions as increase in VR variability measured by QTV increases cardiac repolarization instability, which might lead to arrhythmogenesis. Analyzing the effect of ageing using a widely used measure of QTV (i.e. QTVI) is reported inconsistently in Healthy subjects whereas the same for Long QT Syndrome (LQTS) subjects is not widely reported. In this study, we propose a novel time domain measure from beat-tobeat QT-RR distribution to analyze how ageing affects VR in both Healthy and a group of genotyped LQTS1 subjects. A total of 139 Healthy subjects and 134 LQTS1 subjects of three different age groups (i.e. Young: age 20-35, Middle-aged: 40-55 and Old: age<;60) were analyzed for this study. The proposed measure is also compared with other existing widely used measures of QTV like SDQT and QTVI in differentiating different age groups. The proposed measure stands out to be more discriminatory than other existing variability measures of QT interval.

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In this study, a linear parametric modeling technique was applied to model ventricular repolarization (VR) dynamics. Three features were selected from the surface ECG recordings to investigate the changes in VR dynamics in healthy and cardiac autonomic neuropathy (CAN) participants with diabetes including heart rate variability (calculated from RR intervals), repolarization variability (calculated from QT intervals), and respiration [calculated by ECG-derived respiration (EDR)]. Surface ECGs were recorded in a supine resting position from 80 age-matched participants (40 with no cardiac autonomic neuropathy (NCAN) and 40 with CAN). In the CAN group, 25 participants had early/subclinical CAN (ECAN) and 15 participants were identified with definite/clinical CAN (DCAN). Detecting subclinical CAN is crucial for designing an effective treatment plan to prevent further cardiovascular complications. For CAN diagnosis, VR dynamics was analyzed using linear parametric autoregressive bivariate (ARXAR) and trivariate (ARXXAR) models, which were estimated using 250 beats of derived QT, RR, and EDR time series extracted from the first 5 min of the recorded ECG signal. Results showed that the EDR-based models gave a significantly higher fitting value (p < 0.0001) than models without EDR, which indicates that QT-RR dynamics is better explained by respiratory-information-based models. Moreover, the QT-RR-EDR model fitting values gradually decreased from the NCAN group to ECAN and DCAN groups, which indicate a decoupling of QT from RR and the respiration signal with the increase in severity of CAN. In this study, only the EDR-based model significantly distinguished ECAN and DCAN groups from the NCAN group (p < 0.05) with large effect sizes (Cohen's d > 0.75) showing the effectiveness of this modeling technique in detecting subclinical CAN. In conclusion, the EDR-based trivariate QT-RR-EDR model was found to be better in detecting the presence and severity of CAN than the bivariate QT-RR model. This finding also establishes the importance of adding respiratory information for analyzing the gradual deterioration of normal VR dynamics in pathological conditions, such as diabetic CAN.