Characteristics and Prevalence of Premature Ventricular Complex: A Telemedicine Study

Can the efficacy of a medical treatment be predicted based on the type of idiopathic premature ventricular contraction?

BACKGROUND

Premature ventricular contractions (PVCs) are common arrhythmias with diverse clinical implications. This retrospective study aimed to evaluate the efficacy of medical treatments using various clinical, imaging, and electrocardiographic parameters in patients with idiopathic PVCs.

METHODS

A total of 1051 patients with idiopathic PVCs were retrospectively analyzed. Patients were categorized into three groups based on treatment response: beta-blocker (BB) responders (479 patients), calcium-channel blocker (CCB) responders (335 patients), and class 1c antiarrhythmic (AA) responders (237 patients). Clinical, imaging, and electrocardiographic data were collected and analyzed to assess the factors influencing treatment response.

RESULTS

Age, left ventricular ejection fraction (LVEF), PVC QRS duration, CI variability, and multiple PVC morphologies were identified as significant factors affecting treatment response. Older age and lower LVEF were associated with better response to BB treatment, whereas CCB responders showed narrower QRS complexes. BB responders also exhibited higher CI variability, possibly linked to automaticity mechanisms. Moreover, the BB responder group had a higher frequency of multiple PVC morphologies.

CONCLUSION

These findings emphasize the importance of tailored treatment approaches based on individual patient characteristics.

Classification of Premature Ventricular Contractions in Athletes During Routine Preparticipation Exams

BACKGROUND

Large-scale data on incidental premature ventricular contraction (PVC) prevalence and morphologies have been lacking, leaving many providers without guidance on further cardiac testing for patients with incidental PVCs on ECG. Athletes offer an intriguing cohort to understand the clinical significance, prevalence, and common morphologies of incidental PVCs because they often undergo ECG screening during preparticipation exams.

METHODS

Digital ECGs were obtained from 10 728 screened athletes aged 14 to 35 years during mass screenings in schools and professional sports teams between 2014 and 2021. A retrospective analysis of ECGs with PVCs was performed using the simultaneous display of frontal (limb) and horizontal (precordial) plane leads. PVCs were coded for morphology and categorized as benign or nonbenign using recommended criteria.

RESULTS

Twenty-six athletes (0.24%) were found to have at least 1 PVC. Among these, 50% were female, 65% were White, 8% were Asian, 4% were Hispanic, and 23% were Black. Nineteen of the 26 (73%) ECGs had PVCs with a left bundle branch block pattern compared with 7 (27%) with a right bundle branch block pattern. Twenty-four ECGs (96%) had PVCs with benign patterns, including 18 with right ventricular outflow tract, 5 with left anterior fascicle, and 2 with left posterior fascicle morphology.

CONCLUSIONS

There is a low prevalence of PVCs on routine ECG screening of young athletes, and most PVCs are of benign morphology in this population. This study highlights the value of using digital ECG recorders with simultaneous lead display to guide decision-making about further cardiac testing and referrals in young athletes with PVCs. Using our results and review of the literature, we propose methods and algorithms of PVC evaluation on screening ECGs to help guide many providers with risk stratification and decision-making about further cardiac testing and electrophysiology referrals in young athletes with PVCs.

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.

Risk of atrial fibrillation among patients with premature ventricular complexes: a systematic review and meta-analysis of cohort studies

INTRODUCTION

Atrial fibrillation (AF) is more likely found in patients with premature ventricular complexes (PVCs). Nonetheless, the outcomes of previous investigations remain inconclusive. To evaluate the link between PVCs and the risk of AF, we did a systematic review and meta-analysis.

EVIDENCE ACQUISITION

Potentially eligible studies were found by searching for published publications indexed in the MEDLINE and EMBASE databases from inception to April 13, 2021, looking for studies that assessed the risk of AF in patients with PVCs vs. those who did not have PVCs. Dersimonian and Laird's random-effect, generic inverse variance technique was used to calculate the pooled risk ratio (RR) and 95% confidence interval (CI).

EVIDENCE SYNTHESIS

The meta-analysis includes 6 cohort studies (1 prospective and 5 retrospective cohort studies) with a total of 9,662,088 individuals. We found that patients with PVCs have a significantly higher risk of AF than individuals without PVCs with the pooled RR of 1.90 (95% CI: 1.51-2.39, I²=83%).

CONCLUSIONS

PVCs are significantly related with a 1.90-fold higher incidence of AF, according to the present systematic review and meta-analysis. Nonetheless, further research is needed to determine how this connection should be treated in clinical practice if it is causal.

Age and gender differences of basic electrocardiographic values and abnormalities in the general adult population; Tehran Cohort Study

BACKGROUND

Although several studies are available regarding baseline Electrocardiographic (ECG) parameters and major and minor ECG abnormalities, there is considerable controversy regarding their age and gender differences in the literature.

METHODS

Data from 7630 adults aged ≥ 35 from the Tehran Cohort Study registered between March 2016 and March 2019 were collected. Basic ECG parameters values and abnormalities related to arrhythmia, defined according to the American Heart Association definitions, were analyzed and compared between genders and four distinct age groups. The odds ratio of having any major ECG abnormality between men and women, stratified by age, was calculated.

RESULTS

The average age was 53.6 (± 12.66), and women made up 54.2% (n = 4132) of subjects. The average heart rate (HR) was higher among women(p < 0.0001), while the average values of QRS duration, P wave duration, and RR intervals were higher among men(p < 0.0001). Major ECG abnormalities were observed in 2.9% of the study population (right bundle branch block, left bundle branch block, and Atrial Fibrillation were the most common) and were more prevalent among men compared to women but without statistical significance (3.1% vs. 2.7% p = 0.188). Moreover, minor abnormalities were observed in 25.9% of the study population and again were more prevalent among men (36.4% vs. 17% p < 0.001). The prevalence of major ECG abnormalities was significantly higher in participants older than 65.

CONCLUSION

Major and minor ECG abnormalities were roughly more prevalent in male subjects. In both genders, the odds of having major ECG abnormalities surge with an increase in age.

Prevalence and characteristics of atrial fibrillation in Makassar city population: A telemedicine study

OBJECTIVE

Atrial fibrillation (AF) is one of the most commonly occurring arrhythmias and a major modifiable risk factor of stroke, especially in women. The incidence of AF in Indonesia is not well-characterized yet. This is a community-based study to determine the prevalence and characteristics of AF in the Makassar city population.

METHOD

Standard ECG recording showing atrial fibrillation obtained between January 1, 2014 and September 31, 2018 from Telemedicine Study Center in Hasanuddin University Hospital were collected in form of portable document format (pdf) and were analyzed. ECG with incomplete interpretation and/or epidemiological data were excluded. ECG interpretation and analysis were performed by the first author as an electrophysiologist (MA). Epidemiological data, heart rate, P wave amplitude, QRS axis, QRS complex duration and configuration, QRS rate (ventricular response), corrected QT interval according to Bazzett's formula, presence of QRS complex abnormalities and ST-T changes were analyzed. Data analysis were performed using SPSS 20.0 for Windows.

RESULT

A total of 19.718 ECG data were obtained, taken from the Makassar Telemedicine study data center at Hasanuddin University Hospital which cover all Public Health Center and a private clinic in Makassar city. From this population, AF was found in 189 (0.96%) ECGs. 98 (51.9%) are males' and 91 (48.1%) of which are females'. AF is increasingly prevalent with increasing ages. There was a significant difference on the QRS axis between male and female with a p value of <0.001. The duration of the QRS complex between men and women was significantly different (p=0.038). QTc value was also found to be significantly different between male and female (p=0.001). AF was accompanied by PVC in 9 males and 2 females.

CONCLUSION

The prevalence of AF in the Makassar population is 0.96%, more common in men and elderly.

Telemedicine for detecting Brugada Syndrome in eastern Indonesia: A multi-center prospective observational study

BACKGROUND

The incidence of Brugada syndrome has been reported to occur mostly in Asian countries. However, key countries such as Indonesia, the largest-populated Southeast Asian country, have yet to report any existing data regarding the incidence of Brugada syndrome among its population. Detecting these patients has been challenging, especially in primary healthcare settings, which generally have limited resources. Telemedicine may represent an ideal solution for initial diagnosis to determine if a patient may have this condition.

METHODS

We collected and analyzed numerous 12-lead electrocardiograms (ECG) of patients who visited various healthcare centers in Makassar for routine medical check-up between June 2017-April 2018. Electrocardiograms from these centers were sent to the Cardiac Center at Dr. Wahidin Sudirohusodo Hospital in Makassar via telemedicine.

RESULTS

During the period, we successfully obtained 9558 ECGs. While none of the patients were initially suspected of Brugada Syndrome, we found 102 (1.07%) among them to have a Brugada ECG pattern (BrEP). BrEP was more commonly found in males compared to females (67.6% vs. 32.4% of the cases found). There were significant differences in the number of confirmed cases among the types of BrEP for male and female patients. The number of confirmed cases of BrEP in male and female patients were significantly different (p < 0.05), where the number of cases for male vs. female was 8 vs. 4 for type 1, 17 vs. 1 for type 2, and 44 vs. 28 for type 3.

CONCLUSION

Brugada syndrome is a disease that is at grave risk of being frequently underdiagnosed. Our study indicates that telemedicine can become an appropriate tool that can assist physicians in detecting suspected patients. Future efforts should also be directed at studying the possible use of telemedicine for detecting other similarly rare conditions.

Association of premature ventricular complexes and risk of ischemic stroke: A systematic review and meta-analysis

Recent studies have suggested that patients with premature ventricular complexes (PVCs) may have a higher risk of ischemic stroke. However, the data are limited and inconclusive. We conducted a systematic review and meta‐analysis to investigate the association between PVCs and the risk of ischemic stroke. A comprehensive literature review was conducted by searching for published articles indexed in MEDLINE and EMBASE databases from inception through September 25, 2020, to identify studies that compared the risk of ischemic stroke between patients with PVCs and individuals without PVCs. Pooled risk ratio (RR) and 95% confidence interval (CI) were calculated using a random‐effect, generic inverse variance method of Dersimonian and Laird. A total of four observational studies (2 prospective and 2 retrospective cohort studies) with 42 677 participants met the eligibility criteria and were included in the meta‐analysis. We found that patients with PVCs have a significantly higher risk of ischemic stroke than individuals without PVCs with the pooled RR of 1.31 (95% CI, 1.07–1.60, I² = 43%). From our systematic review and meta‐analysis, we found that PVCs are associated with a higher risk of ischemic stroke. Whether this association is causal and how it should be addressed in clinical practice require further investigations.

Park Algorithm as Predictor of Premature Ventricular Contraction Origin in Three-Dimensional Mapping Electrophysiological Studies

PURPOSE

In the past few years, premature ventricular contraction (PVC) has attracted immense attention, both in patients with or without structural heart disease. Despite the technological advancement, no guiding tools are currently available to assist in the prediction of origin of PVC using a 12-lead electrocardiogram (ECG) before electrophysiology and ablation procedures. Park and co-workers compiled the existing algorithms for the morphology of ECG from the literature and generated a single algorithm based on specific features of ECG for the prediction of PVC origin. The Park algorithm is limited to idiopathic PVC and has not been evaluated clinically. In the present study, the Park algorithm was used to predict PVC origin in patients with or without structural heart disease and compared with the gold standard examination based on three-dimensional electrophysiological mapping studies.

PATIENTS AND METHODS

A cross-sectional study employing ECG data and electrophysiology study (EPS) reports from patients' medical records at Integrated Heart Center Wahidin Sudirohusodo Hospital, Makassar, Indonesia was conducted. The study was performed from April 2018 to June 2019 with a total of 31 samples; however, four samples were excluded during the EPS.

RESULTS

In the present study, the incidence of structural heart disease was 45.2%. The suitability of the Park algorithm for electrophysiological evaluation was 85.2%, both in the case of PVC with and/or without structural heart disease. The prediction of the origin of PVC in the right or left heart using the Park algorithm showed a sensitivity of 95%, specificity of 100%, positive predictive value of 100%, negative predictive value of 87.5%, and accuracy of 96%.

CONCLUSION

The findings of the study suggest significant accuracy of the Park algorithm in the prediction of location of origin of PVC. High sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the Park algorithm highlight its suitability to be used for determining the location of PVC origin in the right or left heart.

Recognition of Premature Ventricular Contraction Beat from 12Lead ECG Based on A Novel Detection Function of QRS Onset

Premature ventricular contraction (PVC) is associated to the risk of ventricular dysfunction and cardiovascular events. Its diagnosis depends on a long-time monitoring, and computational tools for PVC recognition can provide significant assistance to specialists. For this purpose, we present an automatic algorithm for the recognition PVC beat based on long-term 12-lead ECG.A total of 249 patients with PVC were included in this study. Initially, a novel QRS onset detection function was used to automatically extract QRS complexes from massive original ECG data. Then, non-personalized but shared QRS-width features of 12-lead QRS complexes were extracted and fed to a binary classifier based on SVM. In order to verify the model, 17, 512 normal beats and 17, 690 PVC beats extracted from 35 patients were used for training, and another 215 normal beats and 291 PVC beats selected randomly from the remaining 214 patients were used for testing.As a result, the achieved accuracy, sensitivity, specificity in training data and testing data are 98.9%, 98.3%, 99.5% and 97.2%, 97.7%, 96.7%, respectively. The high accuracy of PVC recognition makes it promising to be an efficient technique being used in clinical settings to automatically analyze huge ECG data so as to replace the tedious manual interpretation.

Localization of origins of premature ventricular contraction in the whole ventricle based on machine learning and automatic beat recognition from 12-lead ECG

OBJECTIVE

The localization of origins of premature ventricular contraction (PVC) is the key factor for the success of ablation of ventricular arrhythmias. Existing methods rely heavily on manual extraction of PVC beats, which limits their application to the automatic PVC recognition from long-term data recorded by ECG monitors before and during operation. In addition, research identifying PVC sources in the whole ventricle have not been reported. The purpose of this study was to validate the feasibility of localization of origins of PVC in the whole ventricle and to explore an automatic algorithm for recognition of PVC beats based on long-term 12-lead ECG.

APPROACH

This study included 249 patients with spontaneous PVCs or pacing-induced PVCs. A novel algorithm was used to automatically extract PVC beats from a massive amount of original ECG data, which was collected by different acquisition devices. After clustering and labelling, 374 sample groups, each containing dozens to hundreds of PVC beats, formed the entire dataset of 11 categories corresponding to 11 regions of PVC origins in the whole ventricle. To choose the best classification model for the current task, four machine learning methods, support vector machine (SVM), random forest (RF), gradient-boosting decision tree (GBDT) and Gaussian naïve Bayes (GNB), were compared by randomly selecting 70% of the entire dataset (sample groups = 257) for training and the remaining 30% (sample groups = 117) for testing. The average performance of each model was estimated by the bootstrap method using 1000 resampling trials.

MAIN RESULTS

For PVC beat recognition, the achieved testing accuracy, sensitivity and specificity is 97.6%, 98.3% and 96.7%, respectively. For localization purpose, the achieved testing accuracy varies slightly from 70.7% to 74.1% among four classifiers, and when neighboring regions were combined, the testing rank accuracy is improved to a range of 91.5% to 93.2%.

SIGNIFICANCE

The proposed algorithm can automatically recognize PVC beats and map them to one of the 11 regions in the whole ventricle. Owing to the high accuracy of PVC beat recognition and the capability to target the potential PVC origins in multi regions, it is expected to be a predominant technique being used in clinical settings to automatically analyze huge ECG data before and during operation so as to replace the tedious manual identification.