Benchmarking ventricular arrhythmias in the mouse—revisiting the ‘Lambeth Conventions’ 20 years on

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).

Nurse-led titration of angiotensin converting enzyme inhibitors, beta-adrenergic blocking agents and angiotensin receptor blockers for patients with left ventricular systolic dysfunction

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.

Effect of ECG-derived respiration (EDR) on modeling ventricular repolarization dynamics in different physiological and psychological conditions

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.

Effect of exercise training on left ventricular remodeling in diabetic patients with diastolic dysfunction: rationale and design

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.

Effective and efficient detection of premature ventricular contractions based on variation of principal directions

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.