Economic burden and disparities in healthcare resource use among adult patients with cardiac arrhythmia

Trends in Racial Disparities in Healthcare Expenditures Among Senior Medicare Fee-for-service Enrollees in 2007-2020

Despite the universal healthcare coverages, racial disparities in healthcare expenditures among senior Medicare beneficiaries exist. A few studies explored how racial disparities in healthcare expenditures changed over past decades and how it affected differently across 4 minoritized races, by type of Medicare and poverty levels. We conducted a longitudinal study of 21 healthcare expenditures from senior Medicare fee-for-service enrollees to determine overall and secular trends in racial disparities in healthcare expenditures between 2007 and 2020, during which the Affordable Care Act (ACA) came into full force and the COVID-19 pandemic had begun. We found important disparities in healthcare expenditures across 4 minoritized races compared to Whites, even after adjusting for possible confounders for such disparities. Disparities between Hispanics/Asians and Whites were much greater than disparities between Blacks and Whites, in all Parts A, B, and D expenditures. This reality has not been sufficiently emphasized in the literature. Importantly, Black-White disparities in total Part B expenditure gradually worsened between 2007 and 2020, and Hispanic-White and Asian-White disparities worsened greatly during that time window. Health planners need to focus on these large disparities and develop methods to shrink them.

Systemic inflammation indicators and risk of incident arrhythmias in 478,524 individuals: evidence from the UK Biobank cohort

BACKGROUND

The role of systemic inflammation in promoting cardiovascular diseases has attracted attention, but its correlation with various arrhythmias remains to be clarified. We aimed to comprehensively assess the association between various indicators of systemic inflammation and atrial fibrillation/flutter (AF), ventricular arrhythmia (VA), and bradyarrhythmia in the UK Biobank cohort.

METHODS

After excluding ineligible participants, a total of 478,524 eligible individuals (46.75% male, aged 40-69 years) were enrolled in the study to assess the association between systemic inflammatory indicators and each type of arrhythmia.

RESULTS

After covariates were fully adjusted, CRP levels were found to have an essentially linear positive correlation with the risk of various arrhythmias; neutrophil count, monocyte count, and NLR showed a non-linear positive correlation; and lymphocyte count, SII, PLR, and LMR showed a U-shaped association. VA showed the strongest association with systemic inflammation indicators, and it was followed sequentially by AF and bradyarrhythmia.

CONCLUSIONS

Multiple systemic inflammatory indicators showed strong associations with the onset of AF, VA, and bradyarrhythmia, of which the latter two have been rarely studied. Active systemic inflammation management might have favorable effects in reducing the arrhythmia burden and further randomized controlled studies are needed.

Guidelines for assessment of cardiac electrophysiology and arrhythmias in small animals

Cardiac arrhythmias are a major cause of morbidity and mortality worldwide. Although recent advances in cell-based models, including human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM), are contributing to our understanding of electrophysiology and arrhythmia mechanisms, preclinical animal studies of cardiovascular disease remain a mainstay. Over the past several decades, animal models of cardiovascular disease have advanced our understanding of pathological remodeling, arrhythmia mechanisms, and drug effects and have led to major improvements in pacing and defibrillation therapies. There exist a variety of methodological approaches for the assessment of cardiac electrophysiology and a plethora of parameters may be assessed with each approach. This guidelines article will provide an overview of the strengths and limitations of several common techniques used to assess electrophysiology and arrhythmia mechanisms at the whole animal, whole heart, and tissue level with a focus on small animal models. We also define key electrophysiological parameters that should be assessed, along with their physiological underpinnings, and the best methods with which to assess these parameters.

Prevalence of pathological arrhythmia in patients triaged to "cardiac arrhythmia" in the emergency department: a preliminary study

Background

Symptoms caused by cardiac arrhythmia are common problems that lead to presentation to the emergency department. However, the prevalence of pathological heart rhythm in patients triaged for cardiac arrhythmia in the emergency department remains up to now unknown.

Methods and results

In this retrospective study, patients triaged for cardiac arrhythmia admitted to the interdisciplinary emergency department of the Ludwig-Maximilians University Hospital in Munich within 1 year were included. Subsequently, cardiac rhythm in the 12-lead electrocardiogram, clinical presentation, admission rate, and diagnosis at discharge was analyzed. A total of 558 out of 39,798 patients were triaged for cardiac arrhythmia. Of these 42.3% of patients showed a pathological heart rhythm on the initial electrocardiogram (66.9% atrial fibrillation, 16.5% atrial flutter, 16.5% others). About 80% presented in emergency severity index III (many resources are needed without critical vitals) conditions. Sixty-two percent of the pathological electrocardiogram group and 60% of the sinus rhythm group of patients were admitted to the hospital, and 34.7% with pathological electrocardiogram underwent invasive investigations (16.8% in the sinus rhythm group). In 43.4% of patients, the diagnosis of cardiac arrhythmia was already known from previous medical contacts.

Conclusion

A total of 1.8% of patients who presented to our interdisciplinary emergency department were triaged for cardiac arrhythmia. With 49.5%, the hospital admission rate was quite high but the patients presented to the emergency department in our cohort were rarely in critical condition. As a high percentage of our cohort had a history of cardiac arrhythmia, better outpatient management is needed for these patients to reduce emergency department visits and save resources.

Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection

The analysis of the electrocardiogram (ECG) is critical for the diagnosis of arrhythmias. Recent advances in information and communications technology (ICT) have led to the development of wearable ECG devices and arrhythmia-detection algorithms. This study aimed to develop an ICT-based clinical trial service platform using a self-reporting technique for real-time arrhythmia detection. To establish a clinical-trial service platform, a mobile application (app), a demilitarized zone (DMZ), an internal network, and Amazon web services virtual private cloud (AWS-VPC) were developed. The ECG data acquired by a wearable device were transmitted to the mobile app, which collected the participants’ self-reported information. The mobile app transmitted raw ECG and self-reported data to the AWS-VPC and DMZ, respectively. In the AWS-VPC, the live-streaming and playback-reviewer services were operational to display the currently and previously acquired ECG data to clinicians through the web client. All the measured data were transmitted to the internal network, in which the arrhythmia-detection algorithm was executed and all the data were saved. The self-reporting technique and arrhythmia-detection algorithm are the key elements of this platform. In particular, subjective information of participants can be easily collected using a self-reporting technique. These features are particularly of critical importance for treating painless, sparsely occurring arrhythmias.

mCardia: A Context-Aware ECG Collection System for Ambulatory Arrhythmia Screening

This article presents the design, technical implementation, and feasibility evaluation of mCardia —a context-aware, mobile electrocardiogram (ECG) collection system for longitudinal arrhythmia screening under free-living conditions. Along with ECG, mCardia also records active and passive contextual data, including patient-reported symptoms and physical activity. This contextual data can provide a more accurate understanding of what happens before, during, and after an arrhythmia event, thereby providing additional information in the diagnosis of arrhythmia. By using a plugin-based architecture for ECG and contextual sensing, mCardia is device-agnostic and can integrate with various wireless ECG devices and supports cross-platform deployment. We deployed the mCardia system in a feasibility study involving 24 patients who used the system over a two-week period. During the study, we observed high patient acceptance and compliance with a satisfactory yield of collected ECG and contextual data. The results demonstrate the high usability and feasibility of mCardia for longitudinal ambulatory monitoring under free-living conditions. The article also reports from two clinical cases, which demonstrate how a cardiologist can utilize the collected contextual data to improve the accuracy of arrhythmia analysis. Finally, the article discusses the lessons learned and the challenges found in the mCardia design and the feasibility study.

Associations of Antidepressants With Atrial Fibrillation and Ventricular Arrhythmias: A Systematic Review and Meta-Analysis

Background

Several published studies have disagreements on whether the use of antidepressants is associated with increased risk of arrhythmias. In this study, we performed this meta-analysis to assess the association of antidepressants with cardiac arrhythmias in patients who require antidepressants.

Methods

The PubMed and Embase databases were systematically searched until December 2021 to find studies that investigated the association between antidepressant use and cardiac arrhythmias. Studies that assessed the effects of any antidepressant on arrhythmias in patients who require antidepressants compared with those who require no antidepressants were included. We used a random-effects model to pool the adjusted risk ratios (RRs) and 95% confidence intervals (CIs). The stability of the results was examined by omitting an individual study at a time.

Results

A total of 3,396 studies were screened and 6 studies with 2,626,746 participants were finally included in this meta-analysis. When compared with no antidepressants, the use of antidepressants was significantly associated with an increased risk of atrial fibrillation (RR = 1.37, 95% CI: 1.16–1.61). However, there was no difference in the risk of ventricular arrhythmias or sudden cardiac death (RR = 1.33, 95% CI: 0.88–2.01) between the two studied groups. In the subgroup analysis, tricyclic antidepressants (RR = 1.12, 95% CI: 0.89–1.41), selective serotonin reuptake inhibitors (RR = 1.46, 95% CI: 0.63–3.38), and selective serotonin reuptake inhibitors (RR = 0.99, 95% CI: 0.97–1.01) did not increase the risk of ventricular arrhythmias and/or sudden cardiac death.

Conclusion

Recently published data suggested that the use of antidepressants did not increase the risk of ventricular arrhythmias or sudden cardiac death. Antidepressants were associated with an increased risk of atrial fibrillation but that still needs further confirmation.

Cannabis use disorder and increased risk of arrhythmia-related hospitalization in young adults

BACKGROUND AND OBJECTIVES

Recent observations indicate that cannabis use can result in cardiovascular complications including arrhythmias. We studied the relationship between cannabis use disorder (CUD) and arrhythmia hospitalization.

METHODS

We conducted a retrospective analysis of the Nationwide Inpatient Sample (2010-2014). Patients (age 15-54) with a primary diagnosis for arrhythmia (N = 570,556) were compared with non-arrhythmia (N = 67,662,082) inpatients for odds ratio (OR) of CUD by the logistic regression model, adjusted for demographics and comorbid risk factors.

RESULTS

The incidence of CUD in arrhythmia inpatients was 2.6%. Among cannabis users, the most prevalent arrhythmia was atrial fibrillation (42%), followed by other arrhythmias (24%) and atrial flutter (8%). Patients with CUD were younger (15-24 years, OR: 4.23), male (OR: 1.70), and African American (OR: 2.70). CUD was associated with higher odds of arrhythmia hospitalization in the young population, 1.28 times in 15-24 years (95% confidence interval [CI]: 1.229-1.346) and 1.52 times in 25-34 years (95% CI: 1.469-1.578).

CONCLUSION AND SCIENTIFIC SIGNIFICANCE

With the growing legalization in the United States, there is an increased use of medicinal/recreational cannabis. This is the first national study to our knowledge that found that CUD is associated with a 47%-52% increased likelihood of arrhythmia hospitalization in the younger population and the risk of association was controlled for potential confounders including other substances. The fact that atrial fibrillation is the most prevalent arrhythmia is of special concern since it can result in stroke and other embolic events. Physicians need to familiarize themselves with cannabis abuse or dependence as a risk factor for arrhythmia.

Non-Invasive Fetal Electrocardiogram Monitoring Techniques: Potential and Future Research Opportunities in Smart Textiles

During the pregnancy, fetal electrocardiogram (FECG) is deployed to analyze fetal heart rate (FHR) of the fetus to indicate the growth and health of the fetus to determine any abnormalities and prevent diseases. The fetal electrocardiogram monitoring can be carried out either invasively by placing the electrodes on the scalp of the fetus, involving the skin penetration and the risk of infection, or non-invasively by recording the fetal heart rate signal from the mother’s abdomen through a placement of electrodes deploying portable, wearable devices. Non-invasive fetal electrocardiogram (NIFECG) is an evolving technology in fetal surveillance because of the comfort to the pregnant women and being achieved remotely, specifically in the unprecedented circumstances such as pandemic or COVID-19. Textiles have been at the heart of human technological progress for thousands of years, with textile developments closely tied to key inventions that have shaped societies. The relatively recent invention of smart textiles is set to push boundaries again and has already opened the potential for garments relevant to medicine, and health monitoring. This paper aims to discuss the different technologies and methods used in non-invasive fetal electrocardiogram (NIFECG) monitoring as well as the potential and future research directions of NIFECG in the smart textiles area.

Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2

The present study addresses the cardiac arrhythmia (CA) classification problem using the deep learning (DL)-based method for electrocardiography (ECG) data analysis. Recently, various DL techniques have been utilized to classify arrhythmias, with one typical approach to developing a one-dimensional (1D) convolutional neural network (CNN) model to handle the ECG signals in the time domain. Although the CA classification in the time domain is very prevalent, current methods' performances are still not robust or satisfactory. This study aims to develop a solution for CA classification in two dimensions by introducing the recurrence plot (RP) combined with an Inception-ResNet-v2 network. The proposed method for nine types of CA classification was tested on the 1st China Physiological Signal Challenge 2018 dataset. During implementation, the optimal leads (lead II and lead aVR) were selected, and then 1D ECG segments were transformed into 2D texture images by the RP approach. These RP-based images as input signals were passed into the Inception-ResNet-v2 for CA classification. In the CPSC, Georgia, and the PTB_XL ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the RP-based method achieved an average F1-score of 0.8521, 0.8529, and 0.8862, respectively. The results suggested the excellent generalization ability of the proposed method. To further assess the performance of the proposed method, we compared the 2D RP-image-based solution with the published 1D ECG-based works on the same dataset. Also, it was compared with two traditional ECG transform into 2D image methods, including the time waveform of the ECG recordings and time-frequency images based on continuous wavelet transform (CWT). The proposed method achieved the highest average F1-score of 0.844, with only two leads of the 12-lead ECG original data, which outperformed other works. Therefore, the promising results indicate that the 2D RP-based method has a high clinical potential for CA classification using fewer lead ECG signals.

Characteristics of telehealth users in NYC for COVID-related care during the coronavirus pandemic

OBJECTIVE

To explore whether racial/ethnic differences in telehealth use existed during the peak pandemic period among NYC patients seeking care for COVID-19 related symptoms.

MATERIALS AND METHODS

This study used data from a large health system in NYC - the epicenter of the US crisis - to describe characteristics of patients seeking COVID-related care via telehealth, ER, or office encounters during the peak pandemic period. Using multinomial logistic regression, we estimated the magnitude of the relationship between patient characteristics and the odds of having a first encounter via telehealth versus ER or office visit, and then used regression parameter estimates to predict patients' probabilities of using different encounter types given their characteristics.

RESULTS

Demographic factors, including race/ethnicity and age, were significantly predictive of telehealth use. As compared to Whites, Blacks had higher adjusted odds of using both the ER versus telehealth (OR: 4.3, 95% CI: 4.0-4.6) and office visits versus telehealth (OR: 1.4, 95% CI: 1.3-1.5). For Hispanics versus Whites, the analogous ORs were 2.5 (95% CI: 2.3-2.7) and 1.2 (95% CI: 1.1-1.3). Compared to any age groups, patients 65+ had significantly higher odds of using either ER or office visits versus telehealth.

CONCLUSIONS

The response to COVID-19 has involved an unprecedented expansion in telehealth. While older Americans and minority populations among others are known to be disadvantaged by the digital divide, few studies have examined disparities in telehealth specifically, and none during COVID-19. Additional research into sociodemographic heterogeneity in telehealth use is needed to prevent potentially further exacerbating health disparities overall.

[Current limits of the long duration rhythmic holter: A real life study]

BACKGROUND

Syncope or stroke remain frequently without any explained diagnosis. Long duration holter ECG is an available tool to diagnose arrhythmias. However, this tool is subject to availability of the recorders.

AIM

Report a single center experience with long duration holter ECG in clinical practice, in the different cardiology and neurology indications, and to assess the different delays until achievement of a diagnosis.

METHODS AND RESULTS

The device (Sorin Spiderflash) was used for 48 patients between January 2018 and June 2018. The holter was applied for a mean duration of 10±4days. The mean age was 55+19 years-old. 20 patients (42%) were explored for a stroke or transient ischemic attack (TIA), 18 (36%) for palpitations, 6 (12%) for syncope and 4 (8%) for evaluation of arrhythmias management. An abnormality has been recorded in 11 (22%) patients and a treatment has been administered in 5 patients (10%). Regarding, the timing of the exam, the mean time between the index event and the indication was 39 days. The mean time between the indication and the availability of the device was 32 days. 16 Days was the mean time for lecture and 23 days was the mean time between the result and the appointment with the cardiologist and neurologist.

CONCLUSION

In this registry, the management of patients by non-invasive long duration holter ECG monitoring may be improved regarding the timing of the exams, their lecture and new appointments with the physicians.

Arrhythmogenic mechanisms of obstructive sleep apnea in heart failure patients

Heart failure (HF) affects 23 million people worldwide and results in 300000 annual deaths. It is associated with many comorbidities, such as obstructive sleep apnea (OSA), and risk factors for both conditions overlap. Eleven percent of HF patients have OSA and 7.7% of OSA patients have left ventricular ejection fraction <50% with arrhythmias being a significant comorbidity in HF and OSA patients. Forty percent of HF patients develop atrial fibrillation (AF) and 30%-50% of deaths from cardiac causes in HF patients are from sudden cardiac death. OSA is prevalent in 32%-49% of patients with AF and there is a dose-dependent relationship between OSA severity and resistance to anti-arrhythmic therapies. HF and OSA lead to various downstream arrhythmogenic mechanisms, including metabolic derangement, remodeling, inflammation, and autonomic imbalance. (1) Metabolic derangement and production of reactive oxidative species increase late Na+ currents, decrease outward K+ currents and downregulate connexin-43 and cell-cell coupling. (2) remodeling also features downregulated K+ currents in addition to decreased Na+/K+ ATPase currents, altered Ca2+ homeostasis, and increased density of If current. (3) Chronic inflammation leads to downregulation of both Nav1.5 channels and K+ channels, altered Ca2+ homeostasis and reduced cellular coupling from alterations of connexin expression. (4) Autonomic imbalance causes arrhythmias by evoking triggered activity through increased Ca2+ transients and reduction of excitation wavefront wavelength. Thus, consideration of these multiple pathophysiological pathways (1-4) will enable the development of novel therapeutic strategies that can be targeted against arrhythmias in the context of complex disease, such as the comorbidities of HF and OSA.

Cardiac arrhythmia detection using deep learning: A review

Due to its simplicity and low cost, analyzing an electrocardiogram (ECG) is the most common technique for detecting cardiac arrhythmia. The massive amount of ECG data collected every day, in home and hospital, may preclude data review by human operators/technicians. Therefore, several methods are proposed for either fully automatic arrhythmia detection or event selection for further verification by human experts. Traditional machine learning approaches have made significant progress in the past years. However, those methods rely on hand-crafted feature extraction, which requires in-depth domain knowledge and preprocessing of the signal (e.g., beat detection). This, plus the high variability in wave morphology among patients and the presence of noise, make it challenging for computerized interpretation to achieve high accuracy. Recent advances in deep learning make it possible to perform automatic high-level feature extraction and classification. Therefore, deep learning approaches have gained interest in arrhythmia detection. In this work, we reviewed the recent advancement of deep learning methods for automatic arrhythmia detection. We summarized existing literature from five aspects: utilized dataset, application, type of input data, model architecture, and performance evaluation. We also reported limitations of reviewed papers and potential future opportunities.

A Novel Wearable Device for Continuous Ambulatory ECG Recording: Proof of Concept and Assessment of Signal Quality

Diagnosis of arrhythmic disorders is challenging because of their short-lasting, intermittent character. Conventional technologies of noninvasive ambulatory rhythm monitoring are limited by modest sensitivity. We present a novel form of wearable electrocardiogram (ECG) sensors providing an alternative tool for long-term rhythm monitoring with the potential of increased sensitivity to detect intermittent or subclinical arrhythmia. The objective was to assess the signal quality and R-R coverage of a wearable ECG sensor system compared to a standard 3-lead Holter. In this phase-1 trial, healthy individuals underwent 24-h simultaneous rhythm monitoring using the OMsignal system together with a 3-lead Holter recording. The OMsignal system consists of a garment (bra or shirt) with integrated sensors recording a single-lead ECG and an acquisition module for data storage and processing. Head-to-head signal quality was assessed regarding adequate P-QRS-T distinction and was performed by three electrophysiologists blinded to the recording technology. The accuracy of signal coverage was assessed using Bland-Altman analysis. Fifteen individuals underwent simultaneous 24-h recording. Signal quality and accuracy of the OMgaments was equivalent to Holter-monitoring (84% vs. 93% electrophysiologists rating, p = 0.06). Signal coverage of R-R intervals showed a very close overlay between the OMsignal system and Holter signals, mean difference in heart rate of 2 ± 5 bpm. The noise level of OMgarments was comparable to Holter recording. OMgarments provide high signal quality for adequate rhythm analysis, representing a promising novel technology for long-term non-invasive ECG monitoring.

Integrated RFA/PSOCT catheter for real-time guidance of cardiac radio-frequency ablation

Radiofrequency ablation (RFA) is an important standard therapy for cardiac arrhythmias, but direct monitoring of tissue treatment is currently lacking. We demonstrate an RFA catheter integrated with polarization sensitive optical coherence tomography (PSOCT) for directly monitoring the RFA process in real time. The integrated RFA/OCT catheter was modified from a standard clinical RFA catheter and includes a miniature forward-viewing cone-scanning OCT probe. The PSOCT system was validated with a quarter-wave plate while the RFA function of the integrated catheter was validated by comparing lesion sizes with those made with an unmodified RFA catheter. Additionally, the integrated catheter guided catheter-tissue apposition and monitored RFA lesion formation in cardiac tissue in real time. The results show that catheter-tissue contact can be characterized by observing the features of the blood and tissue in the acquired OCT images and that RFA lesion formation can be confirmed by monitoring the change in phase retardance in the acquired PSOCT images. This system demonstrates the feasibility of an integrated RFA/OCT catheter to deliver RF energy and image the cardiac wall simultaneously and justifies further research into use of this technology to aid RFA therapy for cardiac arrhythmias.

The effect of inadequate access to healthcare services on emergency room visits. A comparison between physical and mental health conditions

This paper estimates the influence of inadequate access to healthcare services on the rate of Emergency Room (ER) hospital visits in Australia. We take micro-data on different types of healthcare shortfalls from the 2012 Australian Survey of Disability, Aging and Carers, and employ Propensity Score Matching (PSM) techniques to identify their effects on ER visits. We find that shortfalls in access to various medical services increases ER visits for individuals with mental and physical conditions by about the same degree. Conversely, inadequate community care services significantly predict ER visits for individuals with physical conditions, but not for persons with mental conditions. The lack of predictive power for inadequate community care for persons with mental health problems is surprising, as "acopia" is thought to be a significant driver of crises that require emergency treatment. We discuss some of the mechanisms that may underpin this finding and address the policy implications of our results. Lastly a number of robustness checks and diagnostics tests are presented which confirm that our modelling assumptions are not violated and that our results are insensitive to the choice of matching algorithms.

Validation of electromechanical wave imaging in a canine model during pacing and sinus rhythm

BACKGROUND

Accurate determination of regional areas of arrhythmic triggers is of key interest to diagnose arrhythmias and optimize their treatment. Electromechanical wave imaging (EWI) is an ultrasound technique that can image the transient deformation in the myocardium after electrical activation and therefore has the potential to detect and characterize location of triggers of arrhythmias.

OBJECTIVES

The objectives of this study were to investigate the relationship between the electromechanical and the electrical activation of the left ventricular (LV) endocardial surface during epicardial and endocardial pacing and during sinus rhythm as well as to map the distribution of electromechanical delays.

METHODS

In this study, 6 canines were investigated. Two external electrodes were sutured onto the epicardial surface of the LV. A 64-electrode basket catheter was inserted through the apex of the LV. Ultrasound channel data were acquired at 2000 frames/s during epicardial and endocardial pacing and during sinus rhythm. Electromechanical and electrical activation maps were synchronously obtained from the ultrasound data and the basket catheter, respectively.

RESULTS

The mean correlation coefficient between electromechanical and electrical activation was 0.81 for epicardial anterior pacing, 0.79 for epicardial lateral pacing, 0.69 for endocardial pacing, and 0.56 for sinus rhythm.

CONCLUSION

The electromechanical activation sequence determined by EWI follows the electrical activation sequence and more specifically in the case of pacing. This finding is of key interest in the role that EWI can play in the detection of the anatomical source of arrhythmias and the planning of pacing therapies such as cardiovascular resynchronization therapy.