Premature ventricular contraction increases the risk of heart failure and ventricular tachyarrhythmia

Prognostic implications of premature ventricular contractions and non-sustained ventricular tachycardia in light-chain cardiac amyloidosis

AIMS

Premature ventricular contractions (PVC) and non-sustained ventricular tachycardia (NSVT) are commonly observed in light chain cardiac amyloidosis (AL-CA), but their association with prognosis is still unclear. We aimed to evaluate the prognostic value of PVCs and NSVT in patients with moderate-to-advanced AL-CA.

METHODS AND RESULTS

We retrospectively included patients with AL-CA at modified 2004 Mayo stages II-IIIb between February 2014 and December 2020. Twenty-four-hour Holter recordings were assessed on admission. The outcomes included (i) new onset of adverse ventricular arrhythmia (VA) or sudden cardiac death (SCD) and (ii) cardiac death during follow-up. Of the 143 patients studied (60.41 ± 11.06 years, male 64.34%), 132 (92.31%) had presence of PVC, and 50 (34.97%) had NSVT on Holter. Twelve (8.4%) patients died in hospital and 131 patients were followed up (median 24.4 months), among whom 71 patients had cardiac death, and 15 underwent adverse VA/SCD. NSVT [hazard ratio (HR): 13.57, 95% confidence interval (CI): 3.06-60.18, P < 0.001], log-transformed PVC counts (HR: 1.46, 95%CI: 1.15-1.86, P = 0.002) and PVC burden (HR: 1.43 95%CI:1.14-1.80, P = 0.002) were predictive of new onset of adverse VA/SCD. The highest tertile of PVC counts (HR: 2.33, 95%CI: 1.27-4.28, P = 0.006) and PVC burden (HR: 2.58, 95%CI: 1.42-4.69, P = 0.002), rather than NSVT (HR: 1.16, 95%CI: 0.67-1.98, P = 0.603), was associated with cardiac death. Higher PVC counts/burden provided incremental value on modified 2004 Mayo stage in predicting cardiac death, with C index increasing from 0.681 to 0.712 and 0.717, respectively (P values <0.05).

CONCLUSION

PVC count, burden, and NSVT significantly correlated with adverse VA/SCD during follow-up in patients with AL-CA. Higher PVC counts/burdens added incremental value for predicting cardiac death.

The Influence of Lifestyle Factors on the Occurrence and Severity of Premature Ventricular Contractions: A Comprehensive Review

Premature ventricular contractions (PVC) are a type of ventricular arrhythmias, occurring as a result of formation or reentry of an abnormal impulse in the ventricular myocardium or in the Purkinje system. PVC occurs commonly in healthy individuals and is observed in 1%-4% of the population. Several lifestyle factors like stress levels, caffeine, drugs, alcohol, nicotine, sleep, and physical exercise have been implicated in increasing the risk. Caffeine and drugs precipitate heightened cardiac stimulation, precipitating PVCs. Excessive alcohol and nicotine disturb the electrical pathways resulting in PVCs. Higher rates of PVCs have been associated with obesity. Individuals with insomnia and increased stress levels are also at an increased risk due to an imbalance in the autonomic system. Exercise is known to induce PVCs, including in healthy, asymptomatic individuals. Modification of these factors can decrease PVC risk. This article aims to provide a comprehensive review of the effects of lifestyle factors on PVC.

Mechanisms and Risk Factors for Premature Ventricular Contraction Induced Cardiomyopathy

Frequent premature ventricular contractions (PVCs) can cause a reversible form of cardiomyopathy in patients without structural heart disease. Because of the challenging nature of PVC-induced cardiomyopathy (PVICM), the mechanisms and risk factors for PVICM are still unclear. Based on the evidence from retrospective and observational studies, the risk factors for the development of PVICM, in addition to PVC exposure, include QRS duration, coupling interval and male sex. Based on animal models, abnormal calcium handling and cardiac remodeling may be the crucial mechanism underlying the development of cardiomyopathy. We have summarized the current knowledge on PVICM in this review. Understanding these mechanisms and risk factors is important for the diagnosis and management of this condition, which can lead to heart failure if left untreated.

Ventricular tachyarrhythmia treatment and prevention by subthreshold stimulation with stretchable epicardial multichannel electrode array

The implantable cardioverter-defibrillator (ICD) is an effective method to prevent sudden cardiac death in high-risk patients. However, the transvenous lead is incompatible with large-area electrophysiological mapping and cannot accommodate selective multichannel precision stimulations. Moreover, it involves high-energy shocks, resulting in pain, myocardial damage, and recurrences of ventricular tachyarrhythmia (VTA). We present a method for VTA treatment based on subthreshold electrical stimulations using a stretchable epicardial multichannel electrode array, which does not disturb the normal contraction or electrical propagation of the ventricle. In rabbit models with myocardial infarction, the infarction was detected by mapping intracardiac electrograms with the stretchable epicardial multichannel electrode array. Then, VTAs could be terminated by sequential electrical stimuli from the epicardial multichannel electrode array beginning with low-energy subthreshold stimulations. Last, we used these subthreshold stimulations to prevent the occurrence of additional VTAs. The proposed protocol using the stretchable epicardial multichannel electrode array provides opportunities toward the development of innovative methods for painless ICD therapy.

ECGNet: An Efficient Network for Detecting Premature Ventricular Complexes Based on ECG Images

BACKGROUND

Preoperative prediction of the origin site of premature ventricular complexes (PVCs) is critical for the success of operations. However, current methods are not efficient or accurate enough. In addition, among the proposed strategies, there are few good prediction methods for electrocardiogram (ECG) images combined with deep learning aspects.

METHODS

We propose ECGNet, a new neural network for the classification of 12-lead ECG images. In ECGNet, 609 ECG images from 310 patients who had undergone successful surgery in the Division of Cardiology, the First Affiliated Hospital of Soochow University, are utilized to construct the dataset. We adopt dense blocks, special convolution kernels and divergent paths to improve the performance of ECGNet. In addition, a new loss function is designed to address the sample imbalance situation, whose cause is the uneven distribution of cases themselves, which often occurs in the medical field. We also conduct extensive experiments in terms of network prediction accuracy to compare ECGNet with other networks, such as ResNet and DarkNet.

RESULTS

Our ECGNet achieves extremely high prediction accuracy (91.74%) and efficiency with very small datasets. Our newly proposed loss function can solve the problem of sample imbalance during the training process.

CONCLUSION

The proposed ECGNet can quickly and accurately realize the multiclassification of PVCs after training with little data. Our network has the potential to be helpful to doctors with a preoperative diagnosis of PVCs. We will continue to collect similar cases and perfect our network structure to further improve the accuracy of our network's prediction.

Obstetric and fetal/neonatal outcomes in pregnant women with frequent premature ventricular complexes and structurally normal heart

BACKGROUND

High premature ventricular complex (PVC) burden may increase the risk of left ventricular dysfunction and all-cause mortality. We aimed to evaluate maternal and neonatal outcomes of pregnant women with structurally normal heart having PVC burden ≥1%.

METHODS

This retrospective cohort study used data from Chang Gung Research Database. Pregnancies from January 1, 2005, through June 30, 2020, with documented maternal PVC burden ≥1% by 24-h Holter monitor were identified. Pregnant women with a diagnosis of structural heart disease or arrhythmias other than PVC were excluded. We used propensity score matching (PSM) to balance the covariates between the PVC group and normal control group. The PVC group was classified into low-PVC (<10%) and high-PVC burden subgroups. The maternal and neonatal outcomes were assessed through 6 months after delivery or termination.

RESULTS

After PSM, there were 214, 61, and 46 pregnant women enrolled in the normal control group, low-PVC burden, and high-PVC burden subgroups, respectively. The high-PVC and low-PVC burden subgroups had composite adverse maternal and neonatal events similar to the control group without use of antiarrhythmic drugs (AADs), but a higher proportion of placental abruption was observed in the high-PVC burden subgroup. Maternal age, diabetes, and overweight were significant predictors of composite adverse maternal events, whereas only maternal age was a significant predictor of composite adverse neonatal events.

CONCLUSIONS

High PVC burden was not associated with poor composite adverse maternal and neonatal outcomes with no need of AADs therapy in pregnant women with structurally normal heart.

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach

Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstration of the cyclical contraction and relaxation of human heart muscles. This signal is a primary and noninvasive tool employed to recognize the actual life threat related to the heart. Abnormal ECG heartbeat and arrhythmia are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is one of the most common arrhythmias which begins from the lower chamber of the heart and can cause cardiac arrest, palpitation, and other symptoms affecting all activities of a patient. Nowadays, computer-assisted techniques reduce doctors' burden to assess heart arrhythmia and heart disease automatically. In this study, we propose a PVC recognition based on a deep learning approach using the MIT-BIH arrhythmia database. Firstly, 10 heartbeat and statistical features including three morphological features (RS amplitude, QR amplitude, and QRS width) and seven statistical features are computed for each signal. The extraction process of these features is conducted for 20 s of ECG data that create a feature vector. Next, these features are fed into a convolutional neural network (CNN) to find unique patterns and classify them more effectively. The obtained results prove that our pipeline improves the diagnosis performance more effectively.

Atrial fibrillation is associated with increased risk of lethal ventricular arrhythmias

Atrial fibrillation (AF) is associated with various major adverse cardiac events such as ischemic stroke, heart failure, and increased overall mortality. However, its association with lethal ventricular arrhythmias such as ventricular tachycardia (VT), ventricular flutter (VFL), and ventricular fibrillation (VF) is controversial. We conducted this study to determine whether AF can increase the risk of VT, VFL, and VF. We utilized the Korean National Health Insurance Service database for this nationwide population-based study. This study enrolled people who underwent a nationwide health screen in 2009 for whom clinical follow-up data were available until December 2018. Primary outcome endpoint was the occurrence of VT, VFL, or VF in people who were and were not diagnosed with new-onset AF in 2009. We analyzed a total of 9,751,705 people. In 2009, 12,689 people were diagnosed with new-onset AF (AF group). The incidence (events per 1000 person-years of follow-up) of VT, VFL, and VF was 2.472 and 0.282 in the AF and non-AF groups, respectively. After adjustment for covariates, new-onset AF was associated with 4.6-fold increased risk (p < 0.001) of VT, VFL, and VF over 10 years of follow-up. The risk of VT, VFL, and VF was even higher if identification of AF was based on intensified criteria (≥ 2 outpatient records or ≥ 1 inpatient record; hazard ratio = 5.221; p < 0.001). In conclusion, the incidence of VT, VFL, and VF was significantly increased in people with new-onset AF. The potential risk of suffering lethal ventricular arrhythmia in people with AF should be considered in clinical practice.