Multipole analysis of heart rate variability as a predictor of imminent ventricular arrhythmias in ICD patients

Predicting Malignant Ventricular Arrhythmias Using Real-Time Remote Monitoring

BACKGROUND

Although implantable cardioverter-defibrillator (ICD) therapies are associated with increased morbidity and mortality, the prediction of malignant ventricular arrhythmias has remained elusive.

OBJECTIVES

The purpose of this study was to evaluate whether daily remote-monitoring data may predict appropriate ICD therapies for ventricular tachycardia or ventricular fibrillation.

METHODS

This was a post hoc analysis of IMPACT (Randomized trial of atrial arrhythmia monitoring to guide anticoagulation in patients with implanted defibrillator and cardiac resynchronization devices), a multicenter, randomized, controlled trial of 2,718 patients evaluating atrial tachyarrhythmias and anticoagulation for patients with heart failure and ICD or cardiac resynchronization therapy with defibrillator devices. All device therapies were adjudicated as either appropriate (to treat ventricular tachycardia or ventricular fibrillation) or inappropriate (all others). Remote monitoring data in the 30 days before device therapy were utilized to develop separate multivariable logistic regression and neural network models to predict appropriate device therapies.

RESULTS

A total of 59,807 device transmissions were available for 2,413 patients (age 64 ± 11 years, 26% women, 64% ICD). Appropriate device therapies (141 shocks, 10 antitachycardia pacing) were delivered to 151 patients. Logistic regression identified shock lead impedance and ventricular ectopy as significantly associated with increased risk of appropriate device therapy (sensitivity 39%, specificity 91%, AUC: 0.72). Neural network modeling yielded significantly better (P < 0.01 for comparison) predictive performance (sensitivity 54%, specificity 96%, AUC: 0.90), and also identified patterns of change in atrial lead impedance, mean heart rate, and patient activity as predictors of appropriate therapies.

CONCLUSIONS

Daily remote monitoring data may be utilized to predict malignant ventricular arrhythmias in the 30 days before device therapies. Neural networks complement and enhance conventional approaches to risk stratification.

ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree

Spontaneous prediction of malignant ventricular arrhythmia (MVA) is useful to avoid delay in rescue operations. Recently, researchers have developed several algorithms to predict MVA using various features derived from electrocardiogram (ECG). However, there are several unresolved issues regarding MVA prediction such as the effect of number of ECG features on a prediction remaining unclear, possibility that an alert for occurring MVA may arrive very late and uncertainty in the performance of the algorithm predicting MVA minutes before onset. To overcome the aforementioned problems, this research conducts an in-depth study on the number and types of ECG features that are implemented in a decision tree classifier. In addition, this research also investigates an algorithm's execution time before the occurrence of MVA to minimize delays in warnings for MVA. Lastly, this research aims to study both the sensitivity and specificity of an algorithm to reveal the performance of MVA prediction algorithms from time to time. To strengthen the results of analysis, several classifiers such as support vector machine and naive Bayes are also examined for the purpose of comparison study. There are three phases required to achieve the objectives. The first phase is literature review on existing relevant studies. The second phase deals with design and development of four modules for predicting MVA. Rigorous experiments are performed in the feature selection and classification modules. The results show that eight ECG features with decision tree classifier achieved good prediction performance in terms of execution time and sensitivity. In addition, the results show that the highest percentage for sensitivity and specificity is 95% and 90% respectively, in the fourth 5-minute interval (15.1 minutes-20 minutes) that preceded the onset of an arrhythmia event. Such results imply that the fourth 5-minute interval would be the best time to perform prediction.

Alterations in heart rate variability are associated with abnormal myocardial perfusion

BACKGROUND

Abnormalities in the autonomic nervous system may occur in ischemic heart disease, but the mechanisms by which they are linked are not fully defined. The risk of cardiac events is increased during morning hours. Studying the contributions of autonomic mechanisms may yield insights into risk stratification and treatment. We hypothesize that autonomic dysfunction, measured by decreased heart rate variability (HRV), associates with abnormal stress myocardial perfusion imaging (MPI).

METHODS

We performed a cross-sectional study of the association between abnormal myocardial stress perfusion with HRV using 276 middle-aged veteran twins without known ischemic heart disease. The primary independent variable was cardiac autonomic regulation measured with 24-hour electrocardiogram (ECG) monitoring, using linear and non-linear (multipole density, or Dyx) HRV metrics. The primary outcome was abnormal perfusion (>5% affected myocardium) during adenosine stress on [13N]-ammonia myocardial perfusion imaging with positron emission tomography.

RESULTS

The mean (SD) age was 53 (3) years and 55 (20%) had abnormal perfusion. HRV (by Dyx) was reduced during morning hours in subjects with abnormal perfusion. At 7 AM, each standard deviation (SD) decrease in Dyx was associated a 4.8 (95% CI, 1.5 - 15.8) odds ratio (OR) for abnormal MPI. With Dyx < 2.0, the 7 AM OR for abnormal MPI was 11.8 (95% CI, 1.2 - 111.4).

CONCLUSIONS

Autonomic dysfunction, measured by non-linear HRV in the morning hours, was associated with an increased OR of abnormal MPI. These results suggest a potentially important role of ECG-based biomarkers in risk stratification for individuals with suspected ischemic heart disease.

Prediction of Sudden Cardiac Death in Implantable Cardioverter Defibrillators: A Review and Comparative Study of Heart Rate Variability Features

Over the last four decades, implantable cardioverter defibrillators (ICDs) have been widely deployed to reduce sudden cardiac death (SCD) risk in patients with a history of life-threatening arrhythmia. By continuous monitoring of the heart rate, ICDs can use decision algorithms to distinguish normal cardiac sinus rhythm or supra-ventricular tachycardia from abnormal cardiac rhythms like ventricular tachycardia and ventricular fibrillation and deliver appropriate therapy such as an electrical stimulus. Despite the success of ICDs, more research is still needed, particularly in decision-making algorithms. Because of low specificity in practical devices, patients with ICDs still receive inappropriate shocks, which may lead to inadvertent mortality and reduction of quality of life. At the same time, higher sensitivity can lead to the use of newer tiered therapies. The purpose of this study is to review the literature on common signal features used in detection algorithms for abnormal cardiac sinus rhythm, as well as reviewing datasets used for algorithm development in previous studies. More than 50 different features to address heart rate changes before SCD have been reviewed and general methodology on this area proposed based on variety of studies on ICDs functionality. A comparative study on the prediction performance of these features, using a common database, is also presented. By combining these features with a support vector machine classifier, achieved results have compared well with other studies.

ECG Parameters for Malignant Ventricular Arrhythmias: A Comprehensive Review

Many studies showed electrocardiogram (ECG) parameters are useful for predicting fatal ventricular arrhythmias (VAs). However, the studies have several shortcomings. Firstly, all studies lack of effective way to present behavior of various ECG parameters prior to the occurrence of the VAs. Secondly, they also lack of discussion on how to consider the parameters as abnormal. Thirdly, the reports do not include approaches to increase the detection accuracy for the abnormal patterns. The purpose of this study is to address the aforementioned issues. It identifies ten ECG parameters from various sources and then presents a review based on the identified parameters. From the review, it has been found that the increased risk of VAs can be represented by presence and certain abnormal range of the parameters. The variation of parameters range could be influenced by either gender or age. This study also has discovered the facts that averaging, outliers elimination and morphology detection algorithms can contribute to the detection accuracy.

Heart rate variability as a predictor of cardiac dysrhythmias during weaning from mechanical ventilation

BACKGROUND

Weaning from mechanical ventilation to spontaneous breathing is associated with changes in the hemodynamic and autonomic nervous systems that are reflected by heart rate variability. Although cardiac dysrhythmias are an important manifestation of hemodynamic alterations, the impact of heart rate variability on the occurrence of dysrhythmias during weaning has not been specifically studied.

OBJECTIVES

To describe differences in heart rate variability spectral power and occurrence of cardiac dysrhythmias at baseline and during the initial trial of weaning from mechanical ventilation and to evaluate the impact of heart rate variability during weaning on occurrence of dysrhythmias.

METHOD

Continuous 3-lead electrocardiographic recordings were collected from 35 patients receiving mechanical ventilation for 24 hours at baseline and during the initial weaning trial. Heart rate variability was evaluated by using spectral power analysis.

RESULTS

Low-frequency power increased (P = .04) and high-frequency and very-low-frequency power did not change during weaning. The mean number of supraventricular ectopic beats per hour during weaning was higher than the mean at baseline (P < .001); the mean of ventricular ectopic beats did not change. Low-frequency power was a predictor of ventricular and supraventricular ectopic beats during weaning (P < .001). High-frequency power was predictive of ventricular and supraventricular (P = .02) ectopic beats during weaning. Very-low-frequency power was predictive of ventricular ectopic beats (P < .001) only.

CONCLUSION

Heart rate variability power spectra during weaning were predictive of dysrhythmias.

Potential effects of intrinsic heart pacemaker cell mechanisms on dysrhythmic cardiac action potential firing

The heart's regular electrical activity is initiated by specialized cardiac pacemaker cells residing in the sinoatrial node. The rate and rhythm of spontaneous action potential firing of sinoatrial node cells are regulated by stochastic mechanisms that determine the level of coupling of chemical to electrical clocks within cardiac pacemaker cells. This coupled-clock system is modulated by autonomic signaling from the brain via neurotransmitter release from the vagus and sympathetic nerves. Abnormalities in brain-heart clock connections or in any molecular clock activity within pacemaker cells lead to abnormalities in the beating rate and rhythm of the pacemaker tissue that initiates the cardiac impulse. Dysfunction of pacemaker tissue can lead to tachy-brady heart rate alternation or exit block that leads to long atrial pauses and increases susceptibility to other cardiac arrhythmia. Here we review evidence for the idea that disturbances in the intrinsic components of pacemaker cells may be implemented in arrhythmia induction in the heart.