Estimated prevalence of undiagnosed atrial fibrillation in the United States

Delayed diagnosis and missed opportunities in the early detection of atrial fibrillation: a cross-sectional study

INTRODUCTION

We analysed the frequency of atrial fibrillation (AF) delayed diagnosis and the factors associated with it in newly diagnosed patients.

METHODS

This was a descriptive, cross-sectional, multicentre study. Data were collected from newly diagnosed patients with AF through medical records review and interviews during cardiology, internal medicine, primary care and emergency department consultations in Spain.

RESULTS

A total of 201 physicians participated in the study (64.2% cardiologists, 21.4% internists). 948 patients (58% men; mean age 72.8 years) were included. In 41.8% of patients, AF was classified as paroxysmal at diagnosis, 30.9% as persistent and 27.3% as permanent. The diagnosis was coincidental in 37%. It was considered that a delayed diagnosis occurred in 49.3% of patients. This delay was associated with the presence of permanent or persistent AF, older age or valvular disease. 74.8% of patients had some contact with the healthcare system in the preceding year. The diagnosis could have been established between 1 and 6 months earlier in 50.7% of cases and more than six months earlier in 20.1%. 54.4% of the patients had experienced AF compatible symptomatology previously. Of these, 32.6% had a consultation without a diagnosis.

CONCLUSIONS

In a significant proportion of AF cases, there is a diagnostic delay. Many people with compatible symptoms neither seek consultations nor contact the healthcare system facilities. Consequently, the opportunity for early diagnosis is lost.

Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention

Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet deploying these on resource-constrained wearable devices remains challenging. This study proposes integrating a customized channel attention mechanism to compress DL neural networks for AF detection, allowing the model to focus only on the most salient time-series features. The results demonstrate that applying compression through channel attention significantly reduces the total number of model parameters and file size while minimizing loss in detection accuracy. Notably, after compression, performance increases for certain model variants in key AF databases (ADB and C2017DB). Moreover, analyzing the learned channel attention distributions after training enhances the explainability of the AF detection models by highlighting the salient temporal ECG/PPG features most important for its diagnosis. Overall, this research establishes that integrating attention mechanisms is an effective strategy for compressing large DL models, making them deployable on low-power wearable devices. We show that this approach yields compressed, accurate, and explainable AF detectors ideal for wearables. Incorporating channel attention enables simpler yet more accurate algorithms that have the potential to provide clinicians with valuable insights into the salient temporal biomarkers of AF. Our findings highlight that the use of attention is an important direction for the future development of efficient, high-performing, and interpretable AF screening tools for wearable technology.

Skin sympathetic nerve activity in symptomatic and asymptomatic paroxysmal atrial fibrillation

BACKGROUND

The causes of symptoms in patients with paroxysmal atrial fibrillation (AF) remains unclear.

OBJECTIVE

The purpose of this study was to correlate the magnitudes of skin sympathetic nerve activity (SKNA) with symptoms in patients with AF.

METHODS

We prospectively enrolled patients with symptomatic paroxysmal AF for ambulatory electrocardiography and SKNA recording. Heart rhythms at the time of symptoms were categorized as AF or normal sinus rhythm (NSR). Maximal and average skin sympathetic nerve activity (aSKNA) and heart rate (HR) were compared between symptomatic and asymptomatic AF and NSR episodes using mixed effects models to account for within-patient correlations.

RESULTS

Among the 31 enrolled patients, 16 (52%) had at least 1 episode of AF, and 24 (77%) endorsed symptoms during the monitoring period. Compared with asymptomatic AF episodes, symptomatic AF episodes had higher maximal aSKNA (1.260 [interquartile range (IQR) 1.114-1.723] μV vs 1.108 [IQR 0.974-1.312] μV; P <0.001) and higher maximal HR (152 ± 24 bpm vs 132 ± 19 bpm; P <.001). Symptomatic NSR episodes were associated with higher maximal aSKNA (1.612 [IQR 1.287-2.027] μV vs 1.332 [IQR 1.033-1.668] μV; P = .001) and higher maximal HR (152 ± 24 bpm vs 105 ± 16 bpm; P <.001) than asymptomatic NSR episodes. Of the symptomatic episodes, 66 (73%) occurred during NSR and 24 (27%) during AF. All P values were obtained from mixed effects models.

CONCLUSION

Symptomatic episodes in patients with paroxysmal AF were more frequently associated with NSR than AF. Symptomatic AF and NSR episodes were associated with higher aSKNA than asymptomatic episodes. In patients with paroxysmal AF, symptoms correlate better with SKNA than heart rhythm.

Mobile Electrocardiograms in the Detection of Subclinical Atrial Fibrillation in High-Risk Outpatient Populations: Protocol for an Observational Study

BACKGROUND

Single-lead, smartphone-based mobile electrocardiograms (ECGs) have the potential to provide a noninvasive, rapid, and cost-effective means of screening for atrial fibrillation (AFib) in outpatient settings. AFib has been associated with various comorbid diseases that prompt further investigation and screening methodologies for at-risk populations. A simple 30-second sinus rhythm strip from the KardiaMobile ECG (AliveCor) can provide an effective screen for cardiac rhythm abnormalities.

OBJECTIVE

The aim of this study is to demonstrate the feasibility of performing Kardia-enabled ECG recordings routinely in outpatient settings in high-risk populations and its potential use in uncovering previous undiagnosed cases of AFib. Specific aim 1 is to determine the feasibility and accuracy of performing routine cardiac rhythm sampling in patients deemed at high risk for AFib. Specific aim 2 is to determine whether routine rhythm sampling in outpatient clinics with high-risk patients can be used cost-effectively in an outpatient clinic without increasing the time it takes for the patient to be seen by a physician.

METHODS

Participants were recruited across 6 clinic sites across the University of Florida Health Network: University of Florida Health Nephrology, Sleep Center, Ophthalmology, Urology, Neurology, and Pre-Surgical. Participants, aged 18-99 years, who agreed to partake in the study were given a consent form and completed a questionnaire regarding their past medical history and risk factors for cardiovascular disease. Single-lead, 30-second ECGs were taken by the KardiaMobile ECG device. If patients are found to have newly diagnosed AFib, the attending physician is notified, and a 12-lead ECG or standard ECG equivalent will be ordered.

RESULTS

As of March 1, 2024, a total of 2339 participants have been enrolled. Of the data collected thus far, the KardiaMobile rhythm strip reported 381 abnormal readings, which are pending analysis from a cardiologist. A total of 78 readings were labeled as possible AFib, 159 readings were labeled unclassified, and 49 were unreadable. Of note, the average age of participants was 61 (SD 10.25) years, and the average self-reported weight was 194 (SD 14.26) pounds. Additionally, 1572 (67.25%) participants report not regularly seeing a cardiologist. Regarding feasibility, the average length of enrolling a patient into the study was 3:30 (SD 0.5) minutes after informed consent was completed, and medical staff across clinic sites (n=25) reported 9 of 10 level of satisfaction with the impact of the screening on clinic flow.

CONCLUSIONS

Preliminary data show promise regarding the feasibility of using KardiaMobile ECGs for the screening of AFib and prevention of cardiological disease in vulnerable outpatient populations. The use of a single-lead mobile ECG strip can serve as a low-cost, effective AFib screen for implementation across free clinics attempting to provide increased health care accessibility.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52647.

An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models

The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.

Correlates and consequences of atrial fibrillation in a prospective study of 25 000 participants in the China Kadoorie Biobank

Aims

The prevalence of atrial fibrillation (AF) is positively correlated with prior cardiovascular diseases (CVD) and CVD risk factors but is lower in Chinese than Europeans despite their higher burden of CVD. We examined the prevalence and prognosis of AF and other electrocardiogram (ECG) abnormalities in the China Kadoorie Biobank.

Methods and results

A random sample of 25 239 adults (mean age 59.5 years, 62% women) had a 12-lead ECG recorded and interpreted using a Mortara VERITAS™ algorithm in 2013-14. Participants were followed up for 5 years for incident stroke, ischaemic heart disease, heart failure (HF), and all CVD, overall and by CHA2DS2-VASc scores, age, sex, and area. Overall, 1.2% had AF, 13.6% had left ventricular hypertrophy (LVH), and 28.1% had ischaemia (two-thirds of AF cases also had ischaemia or LVH). The prevalence of AF increased with age, prior CVD, and levels of CHA₂DS₂-VASc scores (0.5%, 1.3%, 2.1%, 2.9%, and 4.4% for scores <2, 2, 3, 4, and ≥5, respectively). Atrial fibrillation was associated with two-fold higher hazard ratios (HR) for CVD (2.15; 95% CI, 1.71-2.69) and stroke (1.88; 1.44-2.47) and a four-fold higher HR for HF (3.79; 2.21-6.49). The 5-year cumulative incidence of CVD was comparable for AF, prior CVD, and CHA₂DS₂-VASc scores ≥ 2 (36.7% vs. 36.2% vs. 37.7%, respectively) but was two-fold greater than for ischaemia (19.4%), LVH (18.0%), or normal ECG (14.1%), respectively.

Conclusion

The findings highlight the importance of screening for AF together with estimation of CHA₂DS₂-VASc scores for prevention of CVD in Chinese adults.

Many Models, Little Adoption-What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?

(1) Background: Atrial fibrillation (AF) is a major risk factor for stroke and is often underdiagnosed, despite being present in 13-26% of ischemic stroke patients. Recently, a significant number of machine learning (ML)-based models have been proposed for AF prediction and detection for primary and secondary stroke prevention. However, clinical translation of these technological innovations to close the AF care gap has been scant. Herein, we sought to systematically examine studies, employing ML models to predict incident AF in a population without prior AF or to detect paroxysmal AF in stroke cohorts to identify key reasons for the lack of translation into the clinical workflow. We conclude with a set of recommendations to improve the clinical translatability of ML-based models for AF. (2) Methods: MEDLINE, Embase, Web of Science, Clinicaltrials.gov, and ICTRP databases were searched for relevant articles from the inception of the databases up to September 2022 to identify peer-reviewed articles in English that used ML methods to predict incident AF or detect AF after stroke and reported adequate performance metrics. The search yielded 2815 articles, of which 16 studies using ML models to predict incident AF and three studies focusing on ML models to detect AF post-stroke were included. (3) Conclusions: This study highlights that (1) many models utilized only a limited subset of variables available from patients' health records; (2) only 37% of models were externally validated, and stratified analysis was often lacking; (3) 0% of models and 53% of datasets were explicitly made available, limiting reproducibility and transparency; and (4) data pre-processing did not include bias mitigation and sufficient details, leading to potential selection bias. Low generalizability, high false alarm rate, and lack of interpretability were identified as additional factors to be addressed before ML models can be widely deployed in the clinical care setting. Given these limitations, our recommendations to improve the uptake of ML models for better AF outcomes include improving generalizability, reducing potential systemic biases, and investing in external validation studies whilst developing a transparent modeling pipeline to ensure reproducibility.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Effectiveness of single-lead ECG devices for detecting atrial fibrillation: An overview of systematic reviews

BACKGROUND

Individuals with atrial fibrillation (AF) are at an increased risk for stroke. Early detection of undiagnosed AF by screening is recommended. Single-lead electrocardiogram (ECG) is the most widely used technology in AF detection. Several systematic reviews on the diagnostic accuracy of single-lead ECG devices for AF detection have been performed but have yielded inconclusive results.

AIMS

The aim of this study was to synthesize the available evidence on the effectiveness of single-lead ECG devices in detecting AF.

METHODS

An overview of systematic reviews was conducted. Five English databases (Cochrane Database of Systematic Reviews, PubMed, Embase, Ovid, and Web of Science) and two Chinese databases (Wanfang and CNKI) were searched from inception to July 31, 2021. Systematic reviews that examined the accuracy of tools based on single-lead ECG technology for detecting AF were included. A narrative data synthesis was performed.

RESULTS

Eight systematic reviews were finally included. Systematic reviews with meta-analysis showed that single-lead ECG-based devices had good sensitivity and specificity (both ≥90%) in detecting AF. According to subgroup analysis, the sensitivities of tools used in populations with a history of AF were all >90%. However, among handheld and thoracic placed single-lead ECG devices, large variations in diagnostic performance were observed.

LINKING EVIDENCE TO ACTION

Single-lead ECG devices can potentially be used for AF detection. Due to the heterogeneity in the study population and tools, future studies are warranted to explore the suitable circumstances in which each tool could be applied for AF screening in an effective and cost-effective manner.

Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm

AIMS

Guidelines recommend opportunistic screening for atrial fibrillation (AF), using a 30 s single-lead electrocardiogram (ECG) recorded by a wearable device. Since many patients have paroxysmal AF, identification of patients at high risk presenting with sinus rhythm (SR) may increase the yield of subsequent long-term cardiac monitoring. The aim is to evaluate an AI-algorithm trained on 10 s single-lead ECG with or without risk factors to predict AF.

METHODS AND RESULTS

This retrospective study used 13 479 ECGs from AF patients in SR around the time of diagnosis and 53 916 age- and sex-matched control ECGs, augmented with 17 risk factors extracted from electronic health records. AI models were trained and compared using 1- or 12-lead ECGs, with or without risk factors. Model bias was evaluated by age- and sex-stratification of results. Random forest models identified the most relevant risk factors. The single-lead model achieved an area under the curve of 0.74, which increased to 0.76 by adding six risk factors (95% confidence interval: 0.74-0.79). This model matched the performance of a 12-lead model. Results are stable for both sexes, over ages ranging from 40 to 90 years. Out of 17 clinical variables, 6 were sufficient for optimal accuracy of the model: hypertension, heart failure, valvular disease, history of myocardial infarction, age, and sex.

CONCLUSION

An AI model using a single-lead SR ECG and six risk factors can identify patients with concurrent AF with similar accuracy as a 12-lead ECG-AI model. An age- and sex-matched data set leads to an unbiased model with consistent predictions across age groups.

Wearable Devices: Implications for Precision Medicine and the Future of Health Care

Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions.

Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases

Despite all the advancements in science, medical knowledge, healthcare, and the healthcare industry, cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. The main reasons are the inadequacy of preventive health services and delays in diagnosis due to the increasing population, the failure of physicians to apply guide-based treatments, the lack of continuous patient follow-up, and the low compliance of patients with doctors' recommendations. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) are systems that support complex decision-making processes by using AI techniques such as data analysis, foresight, and optimization. Artificial intelligence-based CDSSs play an important role in patient care by providing more accurate and personalized information to healthcare professionals in risk assessment, diagnosis, treatment optimization, and monitoring and early warning of CVD. These are just some examples, and the use of AI for CVD decision support systems is rapidly evolving. However, for these systems to be fully reliable and effective, they need to be trained with accurate data and carefully evaluated by medical professionals.

Realtime Diagnosis from Electrocardiogram Artificial Intelligence-Guided Screening for Atrial Fibrillation with Long Follow-Up (REGAL): Rationale and design of a pragmatic, decentralized, randomized controlled trial

BACKGROUND

Atrial fibrillation (AF) is associated with increased risks of stroke and dementia. Early diagnosis and treatment could reduce the disease burden, but AF is often undiagnosed. An artificial intelligence (AI) algorithm has been shown to identify patients with previously unrecognized AF; however, monitoring these high-risk patients has been challenging. Consumer wearable devices could be an alternative to enable long-term follow-up.

OBJECTIVES

To test whether Apple Watch, used as a long-term monitoring device, can enable early diagnosis of AF in patients who were identified as having high risk based on AI-ECG.

DESIGN

The Realtime diagnosis from Electrocardiogram (ECG) Artificial Intelligence (AI)-Guided Screening for Atrial Fibrillation (AF) with Long Follow-up (REGAL) study is a pragmatic trial that will accrue up to 2,000 older adults with a high likelihood of unrecognized AF determined by AI-ECG to reach our target of 1,420 completed participants. Participants will be 1:1 randomized to intervention or control and will be followed up for 2 years. Patients in the intervention arm will receive or use their existing Apple Watch and iPhone and record a 30-second ECG using the watch routinely or if an abnormal heart rate notification is prompted. The primary outcome is newly diagnosed AF. Secondary outcomes include changes in cognitive function, stroke, major bleeding, and all-cause mortality. The trial will utilize a pragmatic, digitally-enabled, decentralized design to allow patients to consent and receive follow-up remotely without traveling to the study sites.

SUMMARY

The REGAL trial will examine whether a consumer wearable device can serve as a long-term monitoring approach in older adults to detect AF and prevent cognitive function decline. If successful, the approach could have significant implications on how future clinical practice can leverage consumer devices for early diagnosis and disease prevention.

CLINICALTRIALS

GOV: : NCT05923359.

Patients Who Have Pre-Existing Atrial Fibrillation Require Increased Postoperative Care Following Total Joint Arthroplasty

BACKGROUND

Approximately 9% of total joint arthroplasty (TJA) patients have pre-existing atrial fibrillation (AF). This study examined the effect of pre-existing AF on TJA outcomes.

METHODS

We conducted a 1:3 propensity match of 545 TJA patients who have pre-existing AF to TJA patients who do not have AF at a tertiary care center between January 1st, 2012, and January 1st, 2021. Bivariate and multivariate regressions were performed. Changes over time were evaluated.

RESULTS

Patients undergoing total knee arthroplasty (TKA) who have pre-existing AF, experienced more post-operative AFs (P < .001), acute kidney injuries (P = .026), post-operative complications (POC) (P < .001), and 30-day readmissions (P = .036). Patients undergoing total hip arthroplasty (THA) who have pre-existing AF experienced more post-operative AFs (P < .001), pulmonary embolisms (P < .001), increased estimated blood losses (P = .007), more blood transfusions (P = .002), more POCs (P < .001), and longer lengths of stay (LOS) (P < .002). Over time, POC and LOS decreased in both groups, but remained increased in TJA patients who have pre-existing AF. Multivariate analyses of TKA patients showed an increased odds ratio (OR) of any POCs (P < .001), while THA patients had an increased OR of any POCs (P = .01), and LOS (P = .002).

CONCLUSION

Patients who have pre-existing AF undergoing TJA have more POCs. TKA patients have more readmissions. THA patients have longer LOS. These findings demonstrate the importance of enhanced peri-operative medical management in patients who have pre-existing AF undergoing TJA.

Atrial fibrillation as a novel risk factor for retinal stroke: A protocol for a population-based retrospective cohort study

Central retinal artery occlusion (CRAO; retinal stroke or eye stroke) is an under-recognized, disabling form of acute ischemic stroke which causes severe visual loss in one eye. The classical risk factor for CRAO is ipsilateral carotid stenosis; however, nearly half of patients with CRAO do not have high-grade carotid stenosis, suggesting that other cardiovascular risk factors may exist for CRAO. Specifically, prior studies have suggested that cardioembolism, driven by underlying atrial fibrillation, may predispose patients to CRAO. We describe the design of an observational, population-based study in this protocol. We evaluate two specific objectives: 1) To determine if atrial fibrillation is an independent risk factor for CRAO after adjusting for medical and cardiovascular risk; 2) To determine if use of oral anticoagulation can modify the risk of CRAO for patients with atrial fibrillation. This protocol lays out our strategy for cohort definition, case and control definition, comorbidity ascertainment, and statistical methods.

Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias

Early identification of atrial fibrillation (AF) can reduce the risk of stroke, heart failure, and other serious cardiovascular outcomes. However, paroxysmal AF may not be detected even after a two-week continuous monitoring period. We developed a model to quantify the risk of near-term AF in a two-week period, based on AF-free ECG intervals of up to 24 h from 459,889 patch-based ambulatory single-lead ECG (modified lead II) recordings of up to 14 days. A deep learning model was used to integrate ECG morphology data with demographic and heart rhythm features toward AF prediction. Observing a 1-day AF-free ECG recording, the model with deep learning features produced the most accurate prediction of near-term AF with an area under the curve AUC = 0.80 (95% confidence interval, CI = 0.79-0.81), significantly improving discrimination compared to demographic metrics alone (AUC 0.67; CI = 0.66-0.68). Our model was able to predict incident AF over a two-week time frame with high discrimination, based on AF-free single-lead ECG recordings of various lengths. Application of the model may enable a digital strategy for improving diagnostic capture of AF by risk stratifying individuals with AF-negative ambulatory monitoring for prolonged or recurrent monitoring, potentially leading to more rapid initiation of treatment.

Cardiac-Targeting Peptide: From Discovery to Applications

Despite significant strides in prevention, diagnosis, and treatment, cardiovascular diseases remain the number one cause of mortality in the United States, with rates climbing at an alarming rate in the developing world. Targeted delivery of therapeutics to the heart has been a lofty goal to achieve with strategies ranging from direct intra-cardiac or intra-pericardial delivery, intra-coronary infusion, to adenoviral, lentiviral, and adeno-associated viral vectors which have preference, if not complete cardio-selectivity, for cardiac tissue. Cell-penetrating peptides (CPP) are 5-30-amino-acid-long peptides that are able to breach cell membrane barriers while carrying cargoes up to several times their size, in an intact functional form. Identified nearly three decades ago, the first of these CPPs came from the HIV coat protein transactivator of transcription. Although a highly efficient CPP, its clinical utility is limited by its robust ability to cross any cell membrane barrier, including crossing the blood-brain barrier and transducing neuronal tissue non-specifically. Several strategies have been utilized to identify cell- or tissue-specific CPPs, one of which is phage display. Using this latter technique, we identified a cardiomyocyte-targeting peptide (CTP) more than a decade ago, a finding that has been corroborated by several independent labs across the world that have utilized CTP for a myriad of different purposes in pre-clinical animal models. The goal of this publication is to provide a comprehensive review of the identification, validation, and application of CTP, and outline its potential in diagnostic and therapeutic applications especially in the field of targeted RNA interference.

MUE-CoT: multi-scale uncertainty entropy-aware co-training framework for left atrial segmentation

Objective.Accurate left atrial segmentation is the basis of the recognition and clinical analysis of atrial fibrillation. Supervised learning has achieved some competitive segmentation results, but the high annotation cost often limits its performance. Semi-supervised learning is implemented from limited labeled data and a large amount of unlabeled data and shows good potential in solving practical medical problems.Approach. In this study, we proposed a collaborative training framework for multi-scale uncertain entropy perception (MUE-CoT) and achieved efficient left atrial segmentation from a small amount of labeled data. Based on the pyramid feature network, learning is implemented from unlabeled data by minimizing the pyramid prediction difference. In addition, novel loss constraints are proposed for co-training in the study. The diversity loss is defined as a soft constraint so as to accelerate the convergence and a novel multi-scale uncertainty entropy calculation method and a consistency regularization term are proposed to measure the consistency between prediction results. The quality of pseudo-labels cannot be guaranteed in the pre-training period, so a confidence-dependent empirical Gaussian function is proposed to weight the pseudo-supervised loss.Main results.The experimental results of a publicly available dataset and an in-house clinical dataset proved that our method outperformed existing semi-supervised methods. For the two datasets with a labeled ratio of 5%, the Dice similarity coefficient scores were 84.94% ± 4.31 and 81.24% ± 2.4, the HD95values were 4.63 mm ± 2.13 and 3.94 mm ± 2.72, and the Jaccard similarity coefficient scores were 74.00% ± 6.20 and 68.49% ± 3.39, respectively.Significance.The proposed model effectively addresses the challenges of limited data samples and high costs associated with manual annotation in the medical field, leading to enhanced segmentation accuracy.

Cost of treatment failure: Medicare spending on stroke for atrial fibrillation patients not receiving anticoagulation

BACKGROUND

It is well known that atrial fibrillation (AF) patients not receiving anticoagulants are at higher risk of Ischemic Stroke (IS).

OBJECTIVE

Our objective is to estimate how much the Medicare program spends during one-year treating a Medicare beneficiary (MB) with AF who were not being anticoagulated prior to or during their IS hospitalization.

METHODS

This cross-sectional study population consisted of all MBs in the fee-for-service program who were discharged from a hospitalization for IS having AF during 2018. Patients were excluded for a prior history of stroke or already receiving long-term anticoagulants. Medicare spending was defined as paid claims during the index hospitalization and all facility claims that began within 12-months of the index hospital discharge date even if admission occurred in 2019.

RESULTS

The final sample was 50,509 MBs. Average Medicare Part A spending per beneficiary was $46,867 ± $49,212, for a total of nearly $2.5 billion. Highest average spending per MB was for hospital services $25,848, of which $15,790 ± $20,984 occurred during the index hospitalization, and $10,058 ± $21,956 for rehospitalization. The Medicare program average MB spending included $8131 ± $14,979 at skilled nursing facilities, $5538 ± $12,739 at rehabilitation facilities, and $3056 ± $7495 for outpatient facilities or emergency departments.

CONCLUSION

MBs with AF who are not treated with anticoagulants and then suffer an ischemic stroke result in one-year Medicare Part A program spending of approximately $47,000 per person compared to an average spending of approximately $12,800 per beneficiary in the Medicare program in 2018 [1]. Identification and anticoagulation treatment in AF could result in significant healthcare savings.

Solutions for Atrial Fibrillation Edvocacy (SAFE): Improving Awareness And Access To Afib Screening For Detection And Referral For Treatment

Background: Atrial Fibrillation (Afib) can lead to stroke and heart failure, and early detection of Afib is an effective method of preventing these life-threatening conditions. An estimated 2.7 million Americans are living with Afib1, a number that is expected to rise dramatically in the coming years. Methods: The aim of this demonstration project was to create an additional access point in the community at local pharmacies for Afib screening, detection, and referral to physicians for follow-up and initiation of evidence-based therapy when appropriate. This prospective research study was conducted with 14 community pharmacies across the US, in which a total of 650 patients were screened for Afib. Pharmacists conducted SAFEty Risk Assessments that consisted of completion of a Stroke Risk Scorecard and EKG determination utilizing AliveCor's KardiaMobile® 6L device. Results: In 552 (82.5%) of 669 total EKG readings, a "normal" rhythm was detected, and in 117 (17.5%) EKG readings an abnormal detection occurred. A total of 12 out of 650 patients (1.8%) received EKG readings of Afib, which is greater than double the expected prevalence of Afib in the US (0.81%), a statistically significant finding (p < 0.0001). Other notable findings included 42 (6.3%) EKG readings of Wide QRS, and 26 (3.9%) EKG readings of tachycardia. A total of 44 patients were referred to physicians for follow-up on their risk for Afib. Conclusions: Community pharmacies offer a unique, valuable access point for patients to receive Afib screenings. Pharmacists are well positioned to make a significant contribution in the cardiovascular health of their patients and increase the value of team-based health care.

Can we collect health-related quality of life information from anticoagulated atrial fibrillation participants who have recently experienced a bleed? An observational feasibility study in primary and secondary care in Wales and through a UK online forum

OBJECTIVE

To evaluate the feasibility of recruiting participants diagnosed with atrial fibrillation (AF) taking oral anticoagulation therapies (OATs) and recently experiencing a bleed to collect health-related quality of life (HRQoL) information.

DESIGN

Observational feasibility study. The study aimed to determine the feasibility of recruiting participants with minor and major bleeds, the most appropriate route for recruitment and the appropriateness of the patient-reported outcome measures (PROMs) selected for collecting HRQoL information in AF patients, and the preferred format of the surveys.

SETTING

Primary care, secondary care and via an online patient forum.

PARTICIPANTS

The study population was adult patients (≥18) with AF taking OATs who had experienced a recent major or minor bleed within the last 4 weeks.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcomes - PROMs: EuroQol 5 Dimensions-5 Levels, Perception of Anticoagulant Treatment Questionnaire, part 2 only (part 2), atrial fibrillation effect on quality of life. Secondary outcomes - Location of bleed, bleed severity, current treatment, patient perceptions of HRQoL in relation to bleeding events.

RESULTS

We received initial expressions of interest from 103 participants. We subsequently recruited 32 participants to the study-14 from primary care and 18 through the AF forum. No participants were recruited through secondary care. Despite 32 participants consenting, only 26 initial surveys were completed. We received follow-up surveys from 11 participants (8 primary care and 3 AF forum). COVID-19 had a major impact on the study.

CONCLUSIONS

Primary care was the most successful route for recruitment. Most participants recruited to the study experienced a minor bleed. Further ways to recruit in secondary care should be explored, especially to capture more serious bleeds.

TRIAL REGISTRATION NUMBER

The study is registered in the Clinicaltrials.gov database, NCT04921176.

Retinal Ischemic Perivascular Lesions in Individuals With Atrial Fibrillation

Background We previously demonstrated that retinal ischemic perivascular lesions (RIPLs), which are indicative of ischemia in the middle retina, may be a biomarker of ischemic cardiovascular disease. In this study, we sought to determine the relationship between RIPLs and atrial fibrillation, a common source of cardiac emboli. Methods and Results In this case-control study, we identified individuals between the ages of 50 and 90 years who had undergone macular spectral domain optical coherence tomography imaging. Individuals with atrial fibrillation were identified, and age- and sex-matched individuals from the same pool, but without a diagnosis of atrial fibrillation, were selected as controls. Spectral domain optical coherence tomography scans were reviewed by 3 independent and masked observers for presence of RIPLs. The relationship between RIPLs and atrial fibrillation was analyzed using multivariable logistic regression models. There were 106 and 91 subjects with and without atrial fibrillation, respectively. The percentage of subjects with RIPLs was higher in the atrial fibrillation group compared with the control group (57.5% versus 37.4%; P=0.005). After adjusting for age, sex, smoking history, hypertension, diabetes, coronary artery disease, carotid stenosis, stroke, and myocardial infarction, the presence of RIPLs was significantly associated with atrial fibrillation, with an odds ratio of 1.91 (95% CI, 1.01-3.59). Conclusions RIPLs are significantly associated with atrial fibrillation, independent of underlying ischemic heart disease or cardiovascular risk factors. This association may inform the diagnostic cardiovascular workup for individuals with RIPLs incidentally detected on optical coherence tomography scan of the macula.

Cardiomyocyte-Targeting Peptide to Deliver Amiodarone

BACKGROUND

Amiodarone is underutilized due to significant off-target toxicities. We hypothesized that targeted delivery to the heart would lead to the lowering of the dose by utilizing a cardiomyocyte-targeting peptide (CTP), a cell-penetrating peptide identified by our prior phage display work.

METHODS

CTP was synthesized thiolated at the N-terminus, conjugated to amiodarone via Schiff base chemistry, HPLC purified, and confirmed with MALDI/TOF. The stability of the conjugate was assessed using serial HPLCs. Guinea pigs (GP) were injected intraperitoneally daily with vehicle (7 days), amiodarone (7 days; 80 mg/kg), CTP-amiodarone (5 days; 26.3 mg/kg), or CTP (5 days; 17.8 mg/kg), after which the GPs were euthanized, and the hearts were excised and perfused on a Langendorff apparatus with Tyrode's solution and blebbistatin (5 µM) to minimize the contractions. Voltage (RH237) and Ca2+-indicator dye (Rhod-2/AM) were injected, and fluorescence from the epicardium split and was captured by two cameras at 570-595 nm for the cytosolic Ca2+ and 610-750 nm wavelengths for the voltage. Subsequently, the hearts were paced at 250 ms with programmed stimulation to measure the changes in the conduction velocities (CV), action potential duration (APD), and Ca2+ transient durations at 90% recovery (CaTD90). mRNA was extracted from all hearts, and RNA sequencing was performed with results compared to the control hearts.

RESULTS

The CTP-amiodarone remained stable for up to 21 days at 37 °C. At ~1/15th of the dose of amiodarone, the CTP-amiodarone decreased the CV in hearts significantly compared to the control GPs (0.92 ± 0.05 vs. 1.00 ± 0.03 ms, p = 0.0007), equivalent to amiodarone alone (0.87 ± 0.08 ms, p = 0.0003). Amiodarone increased the APD (192 ± 5 ms vs. 175 ± 8 ms for vehicle, p = 0.0025), while CTP-amiodarone decreased it significantly (157 ± 16 ms, p = 0.0136), similar to CTP alone (155 ± 13 ms, p = 0.0039). Both amiodarone and CTP-amiodarone significantly decreased the calcium transients compared to the controls. CTP-amiodarone and CTP decreased the CaTD90 to an extent greater than amiodarone alone (p < 0.001). RNA-seq showed that CTP alone increased the expression of DHPR and SERCA2a, while it decreased the expression of the proinflammatory genes, NF-kappa B, TNF-α, IL-1β, and IL-6.

CONCLUSIONS

Our data suggest that CTP can deliver amiodarone to cardiomyocytes at ~1/15th the total molar dose of the amiodarone needed to produce a comparable slowing of CVs. The ability of CTP to decrease the AP durations and CaTD90 may be related to its increase in the expression of Ca-handling genes, which merits further study.

Global, regional, and national burden of heart failure associated with atrial fibrillation

BACKGROUND

Heart failure is a leading cause of mortality and morbidity worldwide, and Atrial fibrillation (AF) is among many modifiable risk factors for heart failure. No estimates are available on the magnitude of the burden of heart failure associated with AF, and this study estimated the global, regional, and national burdens associated with AF.

METHODS

We used the comparative risk assessment method to estimate the disease burden in terms of prevalence and years lived with disability (YLD). The population-attributable fraction for heart failure and AF was calculated from prevalence estimates of AF and the recalculated relative risks of heart failure associated with AF from a systematic review summarising the longitudinal association between AF and outcomes. The burden of heart failure was retrieved from the Global Burden of Disease database.

RESULTS

Globally, 2.6% (95% uncertainty interval 1.3 to 4.7%) of the burden of heart failure is associated with AF. This was 1.5 (95% UI 0.6 to 3.2) million people in 2019, a 49.8% increase from 1990. The highest prevalence was from South-East Asia, East Asia and Oceania. The highest YLD was estimated for Central Europe, Eastern Europe and Central Asia. High-income countries showed a sharp decline in the age standardised prevalence and YLD rates from 1990 to 2019.

CONCLUSION

The burden of heart failure associated with AF has increased substantially over the past two decades despite the advances in AF management. However, falling prevalence and YLD rates of heart failure associated with AF in high-income countries over time indicate that reducing this burden is possible.

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.

Six-lead electrocardiography compared to single-lead electrocardiography and photoplethysmography of a wrist-worn device for atrial fibrillation detection controlled by premature atrial or ventricular contractions: six is smarter than one

Background

Smartwatches are commonly capable to record a lead-I-like electrocardiogram (ECG) and perform a photoplethysmography (PPG)-based atrial fibrillation (AF) detection. Wearable technologies repeatedly face the challenge of frequent premature beats, particularly in target populations for screening of AF.

Objective

To investigate the potential diagnostic benefit of six-lead ECG compared to single-lead ECG and PPG-based algorithm for AF detection of the wrist-worn device.

Methods and results

From the database of DoubleCheck-AF 249 adults were enrolled in AF group (n = 121) or control group of SR with frequent premature ventricular (PVCs) or atrial (PACs) contractions (n = 128). Cardiac rhythm was monitored using a wrist-worn device capable of recording continuous PPG and simultaneous intermittent six-lead standard-limb-like ECG. To display a single-lead ECG, the six-lead ECGs were trimmed to lead-I-like ECGs. Two diagnosis-blinded cardiologists evaluated reference, six-lead and single-lead ECGs as "AF", "SR", or "Cannot be concluded". AF detection based on six-lead ECG, single-lead ECG, and PPG yielded a sensitivity of 99.2%, 95.7%, and 94.2%, respectively. The higher number of premature beats per minute was associated with false positive outcomes of single-lead ECG (18.80 vs. 5.40 beats/min, P < 0.01), six-lead ECG (64.3 vs. 5.8 beats/min, P = 0.018), and PPG-based detector (13.20 vs. 5.60 beats/min, P = 0.05). Single-lead ECG required 3.4 times fewer extrasystoles than six-lead ECG to result in a false positive outcome. In a control subgroup of PACs, the specificity of six-lead ECG, single-lead ECG, and PPG dropped to 95%, 83.8%, and 90%, respectively. The diagnostic value of single-lead ECG (AUC 0.898) was inferior to six-lead ECG (AUC 0.971) and PPG-based detector (AUC 0.921). In a control subgroup of PVCs, the specificity of six-lead ECG, single-lead ECG, and PPG was 100%, 96.4%, and 96.6%, respectively. The diagnostic value of single-lead ECG (AUC 0.961) was inferior to six-lead ECG (AUC 0.996) and non-inferior to PPG-based detector (AUC 0.954).

Conclusions

A six-lead wearable-recorded ECG demonstrated the superior diagnostic value of AF detection compared to a single-lead ECG and PPG-based AF detection. The risk of type I error due to the widespread use of smartwatch-enabled single-lead ECGs in populations with frequent premature beats is significant.

Cardiomyocyte Targeting Peptide to Deliver Amiodarone

Background

Amiodarone is underutilized due to significant off-target toxicities. We hypothesized that targeted delivery to the heart would lead to lowering of dose by utilizing a cardiomyocyte targeting peptide (CTP), a cell penetrating peptide identified by our prior phage display work.

Methods

CTP was synthesized thiolated at the N-terminus, conjugated to amiodarone via Schiff base chemistry, HPLC purified and confirmed with MALDI/TOF. Stability of the conjugate was assessed using serial HPLCs. Guinea pigs (GP) were injected intraperitoneally daily with vehicle (7 days), amiodarone (7 days; 80mg/Kg), CTP-amiodarone (5 days;26.3mg/Kg), or CTP (5 days; 17.8mg/Kg), after which GPs were euthanized, hearts excised, perfused on a Langendorff apparatus with Tyrode's solution and blebbistatin (5μM) to minimize contractions. Voltage (RH237) and Ca 2+ -indicator dye (Rhod-2/AM) were injected, fluorescence from the epicardium split and focused on two cameras capturing at 570-595nm for cytosolic Ca 2+ and 610-750nm wavelengths for voltage. Subsequently, hearts were paced at 250ms with programmed stimulation to measure changes in conduction velocities (CV), action potential duration (APD) and Ca 2+ transient durations at 90% recovery (CaTD 90 ). mRNA was extracted from all hearts and RNA sequencing performed with results compared to control hearts.

Results

CTP-amiodarone remained stable for up to 21 days at 37°C. At ∼1/15 th of the dose of amiodarone, CTP-amiodarone decreased CV in hearts significantly compared to control GPs (0.92±0.05 vs. 1.00±0.03m/s, p=0.0007), equivalent to amiodarone alone (0.87±0.08ms, p=0.0003). Amiodarone increased APD (192±5ms vs. 175±8ms for vehicle, p=0.0025), while CTP-amiodarone decreased it significantly (157±16ms, p=0.0136) similar to CTP alone (155±13ms, p=0.0039). Both amiodarone and CTP-amiodarone significantly decreased calcium transients compared to controls. CTP-amiodarone and CTP decreased CaTD 90 to an extent greater than amiodarone alone (p<0.001). RNA-seq showed that CTP alone increased the expression of DHPR and SERCA2a, while decreasing expression of proinflammatory genes NF-kappa B, TNF-α, IL-1β, and IL-6.

Conclusions

Our data suggests that CTP can deliver amiodarone to cardiomyocytes at ∼1/15 th the total molar dose of amiodarone needed to produce comparable slowing of CVs. The ability of CTP to decrease AP durations and CaTD 90 may be related to its increase in expression of Ca-handling genes, and merits further study.

Predictors of participation in atrial fibrillation screening among community residents in China

BACKGROUND

Atrial fibrillation (AF) is associated with increased stroke risk, but many cases of AF remain undiagnosed. Screening is suggested for early detection of AF. However, nonparticipation in screening is frequently reported, and the underlying causes of why patients choose to participate or not are poorly understood.

OBJECTIVES

To explore factors associated with participation in AF screening in a high-risk population in China.

METHODS

A cross-sectional study was conducted among community residents who were at risk for AF in Guangzhou, China, from February to September 2022. Data on AF knowledge, attitude, subjective norms, perceived behavioral control, and willingness to participate in AF screening were collected. Sociodemographic and clinical data were also collected. Multiple linear stepwise regression analysis was performed to explore predictors for participation in AF screening.

RESULTS

In total, 420 participants were included, with a mean age of 74.4 (±7.43) years old. The results showed that 77.4% of participants reported that they were likely to participate in AF screening. The results of multiple linear regression showed that perceived behavioral control (β =0.591, 95% CI: 0.071, 0.094), attitude (β =0.085, 95% CI: 0.001, 0.025), and having an AF diagnosis (β =0.098, 95% CI: 0.068, 0.494) were positively associated with willingness to participate in AF screening.

CONCLUSIONS

Chinese patients are more likely to participate in AF screening when they perceive great control over their behavior, hold a positive attitude, and have an AF diagnosis. The findings provide clues for future interventions aimed at improving the AF screening participation rate.

Atrial fibrillation incidence and outcomes in two cohorts of octogenarians: LiLACS NZ

BACKGROUND

Atrial fibrillation (AF), the most common cardiac arrhythmia in the general population, has significant healthcare burden. Little is known about AF in octogenarians.

OBJECTIVE

To describe the prevalence and incidence rate of AF in New Zealand (NZ) octogenarians and the risk of stroke and mortality at 5-year follow-up.

DESIGN

Longitudinal Cohort Study.

SETTING

Bay of Plenty and Lakes health regions of New Zealand.

SUBJECTS

Eight-hundred-seventy-seven (379 indigenous Māori, 498 non-Māori) were included in the analysis.

METHODS

AF, stroke/TIA events and relevant co-variates were established annually using self-report and hospital records (and ECG for AF). Cox proportional-hazards regression models were used to determine the time dependent AF risk of stroke/TIA.

RESULTS

AF was present in 21% at baseline (Māori 26%, non-Māori 18%), the prevalence doubled over 5-years (Māori 50%, non-Māori 33%). 5-year AF incidence was 82.6 /1000-person years and at all times AF incidence for Māori was twice that of non-Māori. Five-year stroke/TIA prevalence was 23% (22% in Māori and 24% non- Māori), higher in those with AF than without. AF was not independently associated with 5-year new stroke/TIA; baseline systolic blood pressure was. Mortality was higher for Māori, men, those with AF and CHF and statin use was protective. In summary, AF is more prevalent in indigenous octogenarians and should have an increased focus in health care management. Further research could examine treatment in more detail to facilitate ethnic specific impact and risks and benefits of treating AF in octogenarians.

Protocol for a Systematic Review and Individual Participant Data Meta-Analysis of Randomized Trials of Screening for Atrial Fibrillation to Prevent Stroke

INTRODUCTION

 Atrial fibrillation (AF) is a common cause of stroke. Timely diagnosis of AF and treatment with oral anticoagulation (OAC) can prevent up to two-thirds of AF-related strokes. Ambulatory electrocardiographic (ECG) monitoring can identify undiagnosed AF in at-risk individuals, but the impact of population-based ECG screening on stroke is uncertain, as ongoing and published randomized controlled trials (RCTs) have generally been underpowered for stroke.

METHODS AND ANALYSIS

 The AF-SCREEN Collaboration, with support from AFFECT-EU, have begun a systematic review and individual participant data meta-analysis of RCTs evaluating ECG screening for AF. The primary outcome is stroke. Secondary outcomes include AF detection, OAC prescription, hospitalization, mortality, and bleeding.After developing a common data dictionary, anonymized data will be collated from individual trials into a central database. We will assess risk of bias using the Cochrane Collaboration tool, and overall quality of evidence with the Grading of Recommendations Assessment, Development and Evaluation approach.We will pool data using random effects models. Prespecified subgroup and multilevel meta-regression analyses will explore heterogeneity. We will perform prespecified trial sequential meta-analyses of published trials to determine when the optimal information size has been reached, and account for unpublished trials using the SAMURAI approach.

IMPACT AND DISSEMINATION

 Individual participant data meta-analysis will generate adequate power to assess the risks and benefits of AF screening. Meta-regression will permit exploration of the specific patient, screening methodology, and health system factors that influence outcomes.

TRIAL REGISTRATION NUMBER

 PROSPERO CRD42022310308.

Contemporary prevalence estimates of undiagnosed and diagnosed atrial fibrillation in the United States

BACKGROUND

Atrial fibrillation (AF) prevalence estimates vary and have been based on cohorts with clinically established or diagnosed disease. Undiagnosed AF prevalence estimates are less certain as they are based on nongeneralizable convenience samples.

HYPOTHESIS

Because AF is often asymptomatic, it my remain undiagnosed until the development of complications such as stroke or heart failure. Consequently, the observed prevalence of diagnosed AF from the literature may underestimate total disease burden. We therefore sought to estimate the total prevalence of both diagnosed and undiagnosed AF.

METHODS

We performed a retrospective cohort study from 2012 to 2017 using data from five US medical claims data sets. Undiagnosed AF prevalence was estimated based on the observed incidence of ischemic stroke, systemic embolism (SE), and AF incidence after a stroke/SE. The diagnosed AF cohort included AF patients between Q1 2014 and Q3 2015. The undiagnosed AF cohort were patients with assumed undiagnosed AF in the year before a stroke/SE and who were newly diagnosed with AF in the 3-month poststroke/SE. Stroke/SE incidence was calculated among all AF patients and the ratio of number of undiagnosed AF patients to stroke rate was created. Age- and sex-adjusted estimates were stratified by period of assumed undiagnosed AF before poststroke/SE AF diagnosis (1 or 2 years).

RESULTS

The estimated US prevalence of AF (diagnosed and undiagnosed) in Q3 2015 was 5 628 000 cases, of which 591 000 cases (11%) were undiagnosed. The assumed 2-year undiagnosed AF prevalence was 23% (1 531 000) of the total prevalent patients with AF (6 568 000). Undiagnosed (vs. diagnosed) AF patients were older and had higher CHA2DS2-VASc scores. Of undiagnosed AF, 93% had CHA2DS2-VASc ≥2 and met OAC criteria.

CONCLUSIONS

These contemporary estimates demonstrate the high prevalence of undiagnosed AF in the United States. Undiagnosed AF patients are composed of primarily elderly individuals who if diagnosed, would meet criteria for stroke prevention therapy.

Utility of short-term telemetry heart rhythm monitoring and CHA2DS2-VASc stratification in patients presenting with suspected cerebrovascular accident

BACKGROUND

Inpatient telemetry heart rhythm monitoring overuse has been linked to higher healthcare costs.

AIM

To evaluate if CHA₂DS₂-VASc score could be used to indicate if a patient admitted with possible cerebrovascular accident (CVA) or transient ischemic attack (TIA) requires inpatient telemetry monitoring.

METHODS

A total of 257 patients presenting with CVA or TIA and placed on telemetry monitoring were analyzed retrospectively. We investigated the utility of telemetry monitoring to diagnose atrial fibrillation/flutter and the CHA₂DS₂-VASc scoring tool to stratify the risk of having CVA/TIA in these patients.

RESULTS

In our study population, 63 (24.5%) of the patients with CVA/TIA and telemetry monitoring were determined to have no ischemic neurologic event. Of the 194 (75.5) patients that had a confirmed CVA/TIA, only 6 (2.3%) had an arrhythmia detected during their inpatient telemetry monitoring period. Individuals with a confirmed CVA/TIA had a statistically significant higher CHA₂DS₂-VASc score compared to individuals without an ischemic event (3.59 vs 2.61, P < 0.001).

CONCLUSION

Given the low percentage of inpatient arrhythmias identified, further research should focus on discretionary use of inpatient telemetry on higher risk patients to diagnose the arrhythmias commonly leading to CVA/TIA. A prospective study assessing event rate of CVA/TIA in patients with higher CHA₂DS₂-VASc score should be performed to validate the CHA₂DS₂-VASc score as a possible risk stratifying tool for patients at risk for CVA/TIA.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial

BACKGROUND

Previous atrial fibrillation screening trials have highlighted the need for more targeted approaches. We did a pragmatic study to evaluate the effectiveness of an artificial intelligence (AI) algorithm-guided targeted screening approach for identifying previously unrecognised atrial fibrillation.

METHODS

For this non-randomised interventional trial, we prospectively recruited patients with stroke risk factors but with no known atrial fibrillation who had an electrocardiogram (ECG) done in routine practice. Participants wore a continuous ambulatory heart rhythm monitor for up to 30 days, with the data transmitted in near real time through a cellular connection. The AI algorithm was applied to the ECGs to divide patients into high-risk or low-risk groups. The primary outcome was newly diagnosed atrial fibrillation. In a secondary analysis, trial participants were propensity-score matched (1:1) to individuals from the eligible but unenrolled population who served as real-world controls. This study is registered with ClinicalTrials.gov, NCT04208971.

FINDINGS

1003 patients with a mean age of 74 years (SD 8·8) from 40 US states completed the study. Over a mean 22·3 days of continuous monitoring, atrial fibrillation was detected in six (1·6%) of 370 patients with low risk and 48 (7·6%) of 633 with high risk (odds ratio 4·98, 95% CI 2·11-11·75, p=0·0002). Compared with usual care, AI-guided screening was associated with increased detection of atrial fibrillation (high-risk group: 3·6% [95% CI 2·3-5·4] with usual care vs 10·6% [8·3-13·2] with AI-guided screening, p<0·0001; low-risk group: 0·9% vs 2·4%, p=0·12) over a median follow-up of 9·9 months (IQR 7·1-11·0).

INTERPRETATION

An AI-guided targeted screening approach that leverages existing clinical data increased the yield for atrial fibrillation detection and could improve the effectiveness of atrial fibrillation screening.

FUNDING

Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery.

Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial

Digital smart devices have the capability of detecting atrial fibrillation (AF), but the efficacy of this type of digital screening has not been directly compared to usual care for detection of treatment-relevant AF. In the eBRAVE-AF trial ( NCT04250220 ), we randomly assigned 5,551 policyholders of a German health insurance company who were free of AF at baseline (age 65 years (median; interquartile range (11) years, 31% females)) to digital screening (n = 2,860) or usual care (n = 2,691). In this siteless trial, for digital screening, participants used a certified app on their own smartphones to screen for irregularities in their pulse waves. Abnormal findings were evaluated by 14-day external electrocardiogram (ECG) loop recorders. The primary endpoint was newly diagnosed AF within 6 months treated with oral anti-coagulation by an independent physician not involved in the study. After 6 months, participants were invited to cross-over for a second study phase with reverse assignment for secondary analyses. The primary endpoint of the trial was met, as digital screening more than doubled the detection rate of treatment-relevant AF in both phases of the trial, with odds ratios of 2.12 (95% confidence interval (CI), 1.19-3.76; P = 0.010) and 2.75 (95% CI, 1.42-5.34; P = 0.003) in the first and second phases, respectively. This digital screening technology provides substantial benefits in detecting AF compared to usual care and has the potential for broad applicability due to its wide availability on ordinary smartphones. Future studies are needed to test whether digital screening for AF leads to better treatment outcomes.

What is next for screening for undiagnosed atrial fibrillation? Artificial intelligence may hold the key

Atrial fibrillation (AF) is increasingly common, though often undiagnosed, leaving many people untreated and at elevated risk of ischaemic stroke. Current European guidelines do not recommend systematic screening for AF, even though a number of studies have shown that periods of serial or continuous rhythm monitoring in older people in the general population increase detection of AF and the prescription of oral anticoagulation. This article discusses the conflicting results of two contemporary landmark trials, STROKESTOP and the LOOP, which provided the first evidence on whether screening for AF confers a benefit for people in terms of clinical outcomes. The benefit and efficiency of systematic screening for AF in the general population could be optimized by targeting screening to only those at higher risk of developing AF. For this purpose, evidence is emerging that prediction models developed using artificial intelligence in routinely collected electronic health records can provide strong discriminative performance for AF and increase detection rates when combined with rhythm monitoring in a clinical study. We consider future directions for investigation in this field and how this could be best aligned to the current evidence base to target screening in people at elevated risk of stroke.

Usability of a smartwatch for atrial fibrillation detection in older adults after stroke

Background

Smartwatches can be used for atrial fibrillation (AF) detection, but little is known about how older adults at risk for AF perceive their usability.

Methods

We employed a mixed-methods study design using data from the ongoing Pulsewatch study, a randomized clinical trial (NCT03761394) examining the accuracy of a smartwatch-smartphone app dyad (Samsung/Android) compared to usual care with a patch monitor (Cardea SOLO™ ECG System) for detection of AF among older stroke survivors. To be eligible to participate in Pulsewatch, participants needed to be at least 50 years of age, have had an ischemic stroke, and have no major contraindications to anticoagulation therapy should AF be detected. After 14 days of use, usability was measured by the System Usability Scale (SUS) and investigator-generated questions. Qualitative interviews were conducted, transcribed, and coded via thematic analysis.

Results

Ninety participants in the Pulsewatch trial were randomized to use a smartwatch-smartphone app dyad for 14 days (average age: 65 years, 41% female, 87% White), and 46% found it to be highly usable (SUS ≥68). In quantitative surveys, participants who used an assistive device (eg, wheelchair) and those with history of anxiety or depression were more likely to report anxiety associated with watch use. In qualitative interviews, study participants reported wanting a streamlined system that was more focused on rhythm monitoring and a smartwatch with a longer battery life. In-person training and support greatly improved their experience, and participants overwhelmingly preferred use of a smartwatch over traditional cardiac monitoring owing to its comfort, appearance, and convenience.

Conclusion

Older adults at high risk for AF who were randomized to use a smartwatch-app dyad for AF monitoring over 14 days found it to be usable for AF detection and preferred their use to the use of a patch monitor. However, participants reported that a simpler device interface and longer smartwatch battery life would increase the system's usability.

Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England

Aims

The aim of the PULsE-AI trial was to assess the effectiveness of a machine learning risk-prediction algorithm in conjunction with diagnostic testing for identifying undiagnosed atrial fibrillation (AF) in primary care in England.

Methods and results

Eligible participants (aged ≥30 years without AF diagnosis; n = 23 745) from six general practices in England were randomized into intervention and control arms. Intervention arm participants, identified by the algorithm as high risk of undiagnosed AF (n = 944), were invited for diagnostic testing (n = 256 consented); those who did not accept the invitation, and all control arm participants, were managed routinely. The primary endpoint was the proportion of AF, atrial flutter, and fast atrial tachycardia diagnoses during the trial (June 2019-February 2021) in high-risk participants. Atrial fibrillation and related arrhythmias were diagnosed in 5.63% and 4.93% of high-risk participants in intervention and control arms, respectively {odds ratio (OR) [95% confidence interval (CI)]: 1.15 (0.77-1.73), P = 0.486}. Among intervention arm participants who underwent diagnostic testing (28.1%), 9.41% received AF and related arrhythmia diagnoses [vs. 4.93% (control); OR (95% CI): 2.24 (1.31-3.73), P = 0.003].

Conclusion

The AF risk-prediction algorithm accurately identified high-risk participants in both arms. While the proportions of AF and related arrhythmia diagnoses were not significantly different between high-risk arms, intervention arm participants who underwent diagnostic testing were twice as likely to receive arrhythmia diagnoses compared with routine care. The algorithm could be a valuable tool to select primary care groups at high risk of undiagnosed AF who may benefit from diagnostic testing.

Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study

Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.

NExUS-Heart: Novel examinations using smart technologies for heart health-Data sharing from commercial wearable devices and telehealth engagement in participants with or at risk of atrial fibrillation

Background

Telemedicine and commercial wearable devices capable of detecting atrial fibrillation (AF) have revolutionized arrhythmia care during coronavirus disease 2019. However, not much is known about virtual patient-provider interactions or device sharing behaviors.

Objective

The purpose of this study was to characterize how participants with or at risk of AF are engaging with their providers in the context of telemedicine and using commercially wearable devices to manage their health.

Methods

We developed a survey to describe participant behaviors around telemedicine encounters and commercial wearable device use. The survey was distributed to participants diagnosed with AF or those at risk of AF (as determined by being at least 65 years old and having a CHA₂DS₂-VASc stroke risk score of >2) in the University of Massachusetts Memorial Health Care system.

Results

The survey was distributed to 23,530 patients, and there were 1222 (5.19%) participant responses. Among the participants, 327 (26.8%) had AF and 895 (73.2%) were at risk of AF. Neither device ownership nor device type use differed by AF status. After adjusting for covariates that may influence surveyed participant communication patterns, we found that participants with AF were more likely to share their wearable device-derived data with providers (adjusted odds ratio 1.87; 95% confidence interval 1.02-3.41). Rates of sharing physical activity or sleep data were low for both groups and did not differ by AF status.

Conclusion

Compared with participants at risk of developing AF, those with AF were more likely to share heart rate and rhythm data from their commercial wearable devices with providers. However, both groups had similar rates of sharing physical activity and sleep data, telemedicine engagement, and technology use and ownership.

Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Atrial fibrillation: a geriatric perspective on the 2020 ESC guidelines

BACKGROUND

The Task Force for the diagnosis and management of atrial fibrillation (AF) of the European Society of Cardiology (ESC) published in 2020 the updated Guidelines for the Diagnosis and Management of Atrial Fibrillation with the contribution of the European Heart Rhythm Association (EHRA) of the ESC and the European Association for Cardiothoracic Surgery (EACTS).

METHODS AND RESULTS

In this narrative viewpoint, we approach AF from the perspective of aging medicine and try to provide the readers with information usually neglected in clinical routine, mainly due to the fact that while the large majority of AF patients in real life are older, frail and cognitively impaired, these are mostly excluded from clinical trials, and physicians' attitudes often prevail over standardized algorithms.

CONCLUSIONS

On the basis of existing evidence, (1) opportunistic AF screening by pulse palpation or ECG rhythm strip is cost-effective, and (2) whereas advanced chronological age by itself is not a contraindication to AF treatment, a Comprehensive Geriatric Assessment (CGA) including frailty, cognitive impairment, falls and bleeding risk may assist in clinical decision making to provide the best individualized treatment.

How to level the risk of bleeding while taking anticoagulant therapy in patients with atrial fibrillation?

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmias, while AF is the main risk factor for ischemic stroke. For a long time, vitamin K antagonists, primarily warfarin, have been the standard for the prevention of thromboembolic complications in patients with AF. 5 years ago, dabigatran, rivaroxaban and apixaban occupied the 2nd place in the structure of anticoagulant prescribing (AC), giving way to warfarin. The well-known reason for the inadequate use of AC is the risk of bleeding. There are a large number of validated scales for assessing the risk of hemorrhagic complications, in particular HAS-BLED, ABC, HEMORRHR2HAGES, ATRIA, ORBIT, but the estimated high risk of bleeding should not be the only and absolute limitation to the appointment of AC. Despite the real risk of bleeding on the background of taking AC in patients with AF, an integrated approach taking into account not only the risk factors of the thromboembolic complications, but also hemorrhagic complications, with an emphasis on the existing comorbidities, the presence of comorbidity, old age, etc., will allow an individual approach to the choice of AC and its dose, contributing to the optimization of the management of such kind of patients.

Contactless facial video recording with deep learning models for the detection of atrial fibrillation

Atrial fibrillation (AF) is often asymptomatic and paroxysmal. Screening and monitoring are needed especially for people at high risk. This study sought to use camera-based remote photoplethysmography (rPPG) with a deep convolutional neural network (DCNN) learning model for AF detection. All participants were classified into groups of AF, normal sinus rhythm (NSR) and other abnormality based on 12-lead ECG. They then underwent facial video recording for 10 min with rPPG signals extracted and segmented into 30-s clips as inputs of the training of DCNN models. Using voting algorithm, the participant would be predicted as AF if > 50% of their rPPG segments were determined as AF rhythm by the model. Of the 453 participants (mean age, 69.3 ± 13.0 years, women, 46%), a total of 7320 segments (1969 AF, 1604 NSR & 3747others) were analyzed by DCNN models. The accuracy rate of rPPG with deep learning model for discriminating AF from NSR and other abnormalities was 90.0% and 97.1% in 30-s and 10-min recording, respectively. This contactless, camera-based rPPG technique with a deep-learning model achieved significantly high accuracy to discriminate AF from non-AF and may enable a feasible way for a large-scale screening or monitoring in the future.

Intermittent trapping of spiral waves in a cardiac model

Spiral waves are found in many excitable systems and are thought to play a role in the incoherent electrical activation that underlies cardiac arrhythmias. It is well-known that spiral waves can be permanently trapped by local heterogeneities. In this paper, we demonstrate that spiral waves can also be intermittently trapped by such heterogeneities. Using simulations of a cardiac model in two dimensions, we show that a tissue heterogeneity of sufficient strength or size can result in a spiral wave that is trapped for a few rotations, after which it dislodges and meanders away from the heterogeneity. We also show that these results can be captured by a particle model in which the particle represents the spiral wave tip. For both models, we construct a phase diagram which quantifies which parameter combinations of heterogeneity size and strength result in permanent, intermittent, or no trapping. Our results are consistent with clinical observations in patients with atrial fibrillation that showed that spiral wave reentry can be intermittent.

A novel contact-free atrial fibrillation monitor: a pilot study

Aims

Atrial fibrillation (AF) is a major cause of morbidity and mortality. Current guidelines support performing electrocardiogram (ECG) screenings to spot AF in high-risk patients. The purpose of this study was to validate a new algorithm aimed to identify AF in patients measured with a recent FDA-cleared contact-free optical device.

Methods and results

Study participants were measured simultaneously using two devices: a contact-free optical system that measures chest motion vibrations (investigational device, 'Gili') and a standard reference bed-side ECG monitor (Mindray^®). Each reference ECG was evaluated by two board certified cardiologists that defined each trace as: regular rhythm, AF, other irregular rhythm or indecipherable/missing. A total of 3582, 30-s intervals, pertaining to 444 patients (41.9% with a history of AF) were made available for analysis. Distribution of patients with active AF, other irregular rhythm, and regular rhythm was 16.9%, 29.5%, and 53.6% respectively. Following application of cross-validated machine learning approach, the observed sensitivity and specificity were 0.92 [95% confidence interval (CI): 0.91-0.93] and 0.96 (95% CI: 0.95-0.96), respectively.

Conclusion

This study demonstrates for the first time the efficacy of a contact-free optical device for detecting AF.

Slow Down: New-Onset Atrial Fibrillation Challenges for Ambulatory Surgical Facilities

As patient safety liaisons (PSLs), we are continually educating and collaborating with our Pennsylvania healthcare facilities. We often are asked questions about reportable events under the Medical Care Availability and Reduction of Error (MCARE) Act. One of the most common examples we discuss is cancellations and transfers out of an ambulatory surgical facility (ASF). The top three reasons for cancellations include preop instructions not followed, missed medical issues identified during preop screening, and no shows. The top three reasons for transfers include cardiac arrhythmias, aspiration, and hypertension. Interestingly, between discussions with facilities and review of event reports, new-onset atrial fibrillation (AF) has come up often as a common reason for cancellations or transfers out of the ASF setting, especially in gastrointestinal (GI) procedures. In fact, as PSLs, when educating ASFs on reportable events we often give the example of placing a patient on the cardiac monitor in preop and the patient is found to be in AF. In 2009, it was estimated that 13.1% of AF cases were undiagnosed,3 which may explain why patients are presenting with new-onset AF. People with AF are at an increased risk of complications (e.g., stroke);3,4 therefore, a cancellation or transfer may be necessary depending on the patient’s condition. These events are considered reportable to the Pennsylvania Patient Safety Reporting System (PA-PSRS) under MCARE.