Economic Burden of Undiagnosed Nonvalvular Atrial Fibrillation in the United States

Enhanced electrocardiogram classification using Gramian angular field transformation with multi-lead analysis and segmentation techniques

Conventional manual or feature-based ECG analysis methods are limited by time inefficiencies and human error. This study explores the potential of transforming 1D signals into 2D Gramian Angular Field (GAF) images for improved classification of four ECG categories: Atrial Fibrillation (AFib), Left Ventricular Hypertrophy (LVH), Right Ventricular Hypertrophy (RVH), and Normal ECG.•The study employed GAF transformations to convert 1D ECG signals into 2D representations at three resolutions: 5000 × 5000, 512 × 512, and 256 × 256 pixels.•Segmentation methods were applied to enhance feature localization.•The ConvNext deep learning model, optimized for image classification, was used to evaluate the transformed ECG images, with performance assessed through accuracy, precision, recall, and F1-score metrics.The 512 × 512 resolution achieved the optimal balance between computational efficiency and accuracy. F1-score for AFib, LVH, RVH and Normal ECG were 0.781, 0.71, 0.521 and 0.792 respectively. Segmentation methods improved classification performance, especially in detecting conditions like LVH and RVH. The 5000 × 5000 resolution offered the highest accuracy but was computationally intensive, whereas the 256 × 256 resolution showed reduced accuracy due to loss details.

Trends in health care expenditures and incremental health care cost in adults with atrial fibrillation in the United States

Background

Atrial fibrillation (AF) is associated with increased health care costs; however, comprehensive data on the financial burden of AF remain limited.

Objective

The purpose of this study was to delineate health care expenditures among patients with AF.

Methods

We used the longitudinal panels from the Medical Expenditure Panel Survey covering 2016-2019 to estimate health care expenditures associated with AF. We identified individuals 18 years and older with AF in the first year of each panel (2016-2018) by using the International Classification of Disease, Tenth Revision codes. Covariates included sociodemographic characteristics and comorbidities. Health care expenditures were derived from the second year of each panel (2017-2019) to reflect the cost of having the condition for an entire year. Adjusted mean annual costs were calculated, including total health care expenditure, hospital inpatient, emergency department visits, office-based visits, outpatient visits, home health visits, prescribed medicines, and other expenses. Adjusted models were used to estimate the mean annual incremental total health care cost associated with AF.

Results

The weighted study population included 3,080,055 adults with AF (382 respondents; mean age 71.5; 89.9% white). The adjusted annual total health care cost in adults with AF totaled $14,083 (95% confidence interval $10,887-$17,279) compared with $8771 (95% confidence interval $8106-$9436) for those without AF. The primary drivers of cost over time were hospital inpatient care, office-based visits, and prescribed medications. The annual incremental total health care cost associated with AF was $5312 per adult (in 2019 U.S. dollars).

Conclusion

Adults with AF in the United States face a higher financial burden across various health care services than do those without the condition, with consistently increasing expenses in inpatient care and prescribed medicines. Further research is needed to identify the independent contribution of AF to these costs.

Global burden of atrial fibrillation/atrial flutter and its attributable risk factors from 1990 to 2021

AIMS

To devise effective preventive measures, a profound understanding of the evolving patterns and trends in atrial fibrillation (AF) and atrial flutter (AFL) burdens is pivotal. Our study was designed to quantify the burden and delineate the risk factors associated with AF and AFL across 204 countries and territories spanning 1990-2021.

METHODS AND RESULTS

Data pertaining to AF and AFL were sourced from the Global Burden of Disease Study 2021. The burden of AF/AFL was evaluated using metrics such as incidence, disability-adjusted life years (DALYs), deaths, and their corresponding age-standardized rates (ASRs), stratified by age, sex, socio-demographic index (SDI), and human development index (HDI). The estimated annual percentage change was employed to quantify changes in ASRs. Population attributable fractions were calculated to determine the proportional contributions of major risk factors to age-standardized AF/AFL deaths. This analysis encompassed the period from 1990 to 2021. Globally, in 2021, there were 4.48 million incident cases [95% uncertainty interval (UI): 3.61-5.70], 8.36 million DALYs (95% UI: 6.97-10.13) and 0.34 million deaths (95% UI: 0.29-0.37) attributed to AF/AFL. The AF/AFL burden in 2021, as well as its trends from 1990 to 2021, displayed substantial variations based on gender, SDI quintiles, and geographical regions. High systolic blood pressure emerged as the leading contributor to age-standardized AF/AFL incidence, prevalence, death, and DALY rate globally among all potential risk factors, followed closely by high body mass index.

CONCLUSION

Our study underscores the enduring significance of AF/AFL as a prominent public health concern worldwide, marked by profound regional and national variations. Despite the substantial potential for prevention and management of AF/AFL, there is a pressing imperative to adopt more cost-effective strategies and interventions to target modifiable risk factors, particularly in areas where the burden of AF/AFL is high or escalating.

Effectiveness of Anticoagulants in Reducing Stroke Risk Among Patients With Atrial Fibrillation

Atrial fibrillation (AF) is a type of cardiac arrhythmia causing shortness of breath, lightheadedness, and palpitations. It may go unrecognized and asymptomatic among many patients. AF is not a potentially fatal arrhythmia; its hemodynamic, structural, and hemocoagulative effects have a significant impact on the standard of life, which can lead to various complications such as stroke. A stroke caused by AF leads to additional burdens on both patients and the global economy. Patients with AF can prevent strokes with oral anticoagulants; however, ensuring diligent adherence to medication is crucial for maximizing treatment efficacy. Since they have a lighter treatment load than warfarin, non-vitamin K antagonist oral anticoagulants (NOACs) are also recommended with better hope for medication adherence. Various anticoagulants such as warfarin and ximelagatran, among many more, are prescribed to patients who have the potential to reduce the incidence of stroke as well as alleviate their likelihood of developing other thromboembolic events that can decrease their quality of life. Economic and psychological burdens associated with diminished functionality can be prevented by anticoagulant therapy among AF patients, therefore reducing their economic and social burden. This is due to the negative association between stroke among AF patients and anticoagulation consumption.

The Cost of Atrial Fibrillation: A Systematic Review

OBJECTIVES

Atrial fibrillation (AF) is the most common cardiac arrhythmia, with an increasing incidence and prevalence because of progressively aging populations. Costs related to AF are both direct and indirect. This systematic review aims to identify the main cost drivers of the illness, assess the potential economic impact resulting from changes in care strategies, and propose interventions where they are most needed.

METHODS

A systematic literature search of the PubMed and Scopus databases was performed to identify analytical observational studies defining the cost of illness in cases of AF. The search strategy was based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 recommendations.

RESULTS

Of the 944 articles retrieved, 24 met the inclusion criteria. These studies were conducted in several countries. All studies calculated the direct medical costs, whereas 8 of 24 studies assessed indirect costs. The median annual direct medical cost per patient, considering all studies, was €9409 (13 333 US dollars in purchasing power parities), with a very large variability due to the heterogeneity of different analyses. Hospitalization costs are generally the main cost drivers. Comorbidities and complications, such as stroke, considerably increase the average annual direct medical cost of AF.

CONCLUSIONS

In most of the analyzed studies, inpatient care cost represents the main component of the mean direct medical cost per patient. Stroke and heart failure are responsible for a large share of the total costs; therefore, implementing guidelines to manage comorbidities in AF is a necessary step to improve health and mitigate healthcare costs.

Diagnosis of atrial fibrillation based on AI-detected anomalies of ECG segments

Early detection of atrial fibrillation (AF) is crucial for its effective management and prevention. Various methods for detecting AF using deep learning (DL) based on supervised learning with a large labeled dataset have a remarkable performance. However, supervised learning has several problems, as it is time-consuming for labeling and has a data dependency problem. Moreover, most of the DL methods do not provide any clinical evidence to physicians regarding the analysis of electrocardiography (ECG) for classification or detection of AF. To address these limitations, in this study, we proposed a novel AF diagnosis system using unsupervised learning for anomaly detection with three segments, PreQ, QRS, and PostS, based on the normal ECG. Two independent datasets, PTB-XL and China, were used in three experiments. We used a long short-term memory (LSTM)-based autoencoder to train the segments of the normal ECG. Based on the threshold of anomaly scores using mean squared error (MSE), it distinguished between normal and AF segments. In Experiment A, the best score was that of PreQ, which detected AF with an AUROC score of 0.96. In Experiment B and C for cross validation of each dataset, the best scores were also of PreQ, with AUROC scores of 0.9 and 0.95, respectively. To verify the significance of the anomaly score in distinguishing between AF and normal segments, we utilized an XG-Boosted model after generating anomaly scores in the three segments. The XG-Boosted model achieved an AUROC score of 0.98 and an F1 score of 0.94. AF detection using DL has been controversial among many physicians. However, our study differentiates itself from previous studies in that we can demonstrate evidence that distinguishes AF from normal segments based on the anomaly score.

Investigating the impact of undiagnosed anxiety and depression on health and social care costs and quality of life: cross-sectional study using household health survey data

BACKGROUND

There is uncertainty around the costs and health impacts of undiagnosed mental health problems.

AIMS

Using survey data, we aim to understand the costs and health-related quality-of-life decrements from undiagnosed anxiety/depression.

METHOD

We analysed survey data from two waves of the North West Coast Household Health Survey, which included questions on disease, medications, and Patient Health Questionnaire 9 (PHQ-9) and Generalised Anxiety Disorder 7 (GAD-7) scores (depression and anxiety scales). People were judged as having undiagnosed anxiety/depression problems if they scored ≥5 on the PHQ-9 or GAD-7, and did not declare a mental health issue or antidepressant prescription. Linear regression for EuroQol 5-Dimension 3-Level (EQ-5D-3L) index scores, and Tweedie regression for health and social care costs, were used to estimate the impact of undiagnosed mental health problems, controlling for age, gender, deprivation and other health conditions.

RESULTS

Around 26.5% of participants had undiagnosed anxiety/depression. The presence of undiagnosed anxiety/depression was associated with reduced EQ-5D-3L index scores (0.040 lower on average) and increased costs (£250 ($310) per year on average). Using a higher cut-off score of 10 on the PHQ-9 and GAD-7 for undiagnosed anxiety/depression had similar increased costs but a greater reduction in EQ-5D-3L index scores (0.076 on average), indicating a larger impact on health-related quality of life.

CONCLUSIONS

Having undiagnosed anxiety or depression increases costs and reduces health-related quality of life. Reducing stigma and increasing access to cost-effective treatments will have population health benefits.

Machine Learning-Based Prediction of Atrial Fibrillation Risk Using Electronic Medical Records in Older Aged Patients

Atrial fibrillation (AF) is an independent risk factor that increases the risk of stroke 5-fold. The purpose of our study was to develop a 1-year new-onset AF predictive model by machine learning based on 3-year medical information without electrocardiograms in our database to identify AF risk in older aged patients. We developed the predictive model according to the Taipei Medical University clinical research database electronic medical records, including diagnostic codes, medications, and laboratory data. Decision tree, support vector machine, logistic regression, and random forest algorithms were chosen for the analysis. A total of 2,138 participants (1,028 women [48.1%]; mean [standard deviation] age 78.8 [6.8] years) with AF and 8,552 random controls (after the matching process) without AF (4,112 women [48.1%]; mean [standard deviation] age 78.8 [6.8] years) were included in the model. The 1-year new-onset AF risk prediction model based on the random forest algorithm using medication and diagnostic information, along with specific laboratory data, attained an area under the receiver operating characteristic of 0.74, whereas the specificity was 98.7%. Machine learning-based model focusing on the older aged patients could offer acceptable discrimination in differentiating the risk of incident AF in the next year. In conclusion, a targeted screening approach using multidimensional informatics in the electronic medical records could result in a clinical choice with efficacy for prediction of the incident AF risk in older aged patients.

Cost Implications of Left Atrial Appendage Occlusion During Cardiac Surgery: A Cost Analysis of the LAAOS III Trial

Background The LAAOS III (Left Atrial Appendage Occlusion Study) clinical trial demonstrated that concomitant left atrial appendage (LAA) occlusion leads to a lower risk of ischemic stroke or systemic embolism compared with no occlusion in participants with atrial fibrillation and a CHA₂DS₂-VASc score of ≥2 undergoing cardiac surgery for another indication. We report the cost implications of concomitant LAA occlusion during cardiac surgery. Methods and Results Using LAAOS III data, we compared the costs (in US dollars) associated with LAA occlusion to no occlusion from the perspective of the Centers for Medicare and Medicaid Services. We calculated the average cost per participant during the trial by applying Medicare reimbursement costs to cardiovascular events for all trial participants. We conducted sensitivity analyses, varying the cost of stroke ±25% and occlusion technique use. Cost neutrality was defined as a mean cost difference within ±5% of the cost per participant in the no-occlusion group. Total study cost per participant was $3878 in the LAA occlusion group and $4490 in the no-occlusion group, a mean difference of -$612 (95% CI, -$1276 to $45). The main drivers of cost savings were fewer stroke events during the trial (mean difference of -$1021). In sensitivity analyses, LAA occlusion was cost saving for suture and stapler techniques but more expensive with closure device. Conclusions Concomitant LAA occlusion was cost saving for participants in LAAOS III. Our findings support concomitant LAA occlusion as an economically dominant strategy for patients with atrial fibrillation and a CHA₂DS₂-VASc score of ≥2 undergoing cardiac surgery.

Efficacy and Safety of Bruton Tyrosine Kinase Inhibitor Monotherapy Compared with Combination Therapy for Chronic Lymphocytic Leukemia and Small Lymphocytic Lymphoma: A Systematic Review and Meta-Analysis

The effectiveness and safety of combination treatments such as chemoimmunotherapies in chronic lymphocytic leukemia and small lymphocytic lymphoma (CLL/SLL) remain controversial. Bruton tyrosine kinase inhibitors (BTKis) are an effective therapy for CLL/SLL patients. This meta-analysis aimed to compare the efficacy and safety of BTKis versus combination therapy in CLL/SLL patients. We searched the PubMed, Cochrane, Medline, and Embase databases through February 2023 for relevant randomized controlled trials (RCTs). Four RCTs (including 1510 patients) were found and met the inclusion criteria. Progression-free survival (PFS) was significantly improved with BTKis when compared to the combination therapy (hazard ratio (HR), 0.30; 95% confidence interval (CI), 0.22–0.40), while a pooled analysis of overall survival did not favor single-agent BTKis over the combination therapy (HR, 0.87; 95% CI, 0.67–1.15). We observed consistent benefits for PFS among patients with high-risk disease characteristics. Although there was no difference in complete response between the two arms (risk ratio (RR), 0.54; 95% CI, 0.20–1.46), BTKi use was related to a better overall response rate (RR, 1.10; 95% CI, 1.04–1.16). The risk of grade ≥3 adverse events (AEs) was comparable between the two arms (RR, 0.82; 95% CI, 0.55–1.23). However, the risk of grade ≥3 AEs was significantly lower in the second-generation BTKi group than in the combination therapy group (RR, 0.73; 95% CI, 0.54–0.98). Overall, BTKis have superior efficacy compared to the combination regimens in patients with untreated or treated CLL/SLL without excess toxicity. Further studies are needed to confirm these results and determine the optimal therapy for managing patients with CLL/SLL.

[Asymptomatic atrial fibrillation : Screening and therapy]

Atrial fibrillation (AF) is the most common cardiac arrhythmia affecting millions of people worldwide and poses a significant burden to the health care system. AF screening of the general population or of a specific higher-risk population could not only lead to earlier detection of AF but also to a prompt initiation of an adequate therapy to prevent complications such as stroke or death and consecutively to a potential reduction of health care costs, especially in asymptomatic AF patients. To conduct screening programs, accessible new technology devices such as "wearables", smartwatches, and implantable event recorders provide an innovative solution. However, as data regarding screening are inconclusive, routine AF screening of the population is currently not recommended by the European Society of Cardiology. Recently published studies have indicated that anticoagulation and early rhythm control of asymptomatic AF could avoid occurrence of clinical endpoints. This article reports on the scientific results of the current literature as well as gaps of evidence and discusses possible treatment options of asymptomatic AF.

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.

Cost-effectiveness of targeted screening for non-valvular atrial fibrillation in the United Kingdom in older patients using digital approaches

AIM

Screening for non-valvular atrial fibrillation (NVAF) is key in identifying patients with undiagnosed disease who may be eligible for anticoagulation therapy. Understanding the economic value of screening is necessary to assess optimal strategies for payers and healthcare systems. We evaluated the cost effectiveness of opportunistic screening with handheld digital devices and pulse palpation, as well as targeted screening predictive algorithms for UK patients ≥75 years of age.

METHODS

A previously developed Markov cohort model was adapted to evaluate clinical and economic outcomes of opportunistic screening including pulse palpation, Zenicor (extended 14 days), KardiaMobile (extended), and two algorithms compared to no screening. Key model inputs including epidemiology estimates, screening effectiveness, and risks for medical events were derived from the STROKESTOP, ARISTOTLE studies, and published literature, and cost inputs were obtained from a UK national cost database. Health and cost outcomes, annually discounted at 3.5%, were reported for a cohort of 10,000 patients vs. no screening over a time horizon equivalent to a patient's lifetime, Analyses were performed from a UK National Health Services and personal social services perspective.

RESULTS

Zenicor, pulse palpation, and KardiaMobile were dominant (providing better health outcomes at lower costs) vs. no screening; both algorithms were cost-effective vs. no screening, with incremental cost-effectiveness ratios per quality-adjusted life-year (QALY) of £1,040 and £1,166. Zenicor, pulse palpation, and KardiaMobile remained dominant options vs. no screening in all scenarios explored. Deterministic sensitivity analyses indicated long-term stroke care costs, prevalence of undiagnosed NVAF in patients 75-79 years of age, and clinical efficacy of anticoagulant on stroke prevention were the main drivers of the cost-effectiveness results.

CONCLUSIONS

Screening for NVAF at ≥75 years of age could result in fewer NVAF-related strokes. NVAF screening is cost-effective and may be cost-saving depending on the program chosen.

How synergy between mechanistic and statistical models is impacting research in atrial fibrillation

Atrial fibrillation (AF) with multiple complications, high morbidity and mortality, and low cure rates, has become a global public health problem. Although significant progress has been made in the treatment methods represented by anti-AF drugs and radiofrequency ablation, the therapeutic effect is not as good as expected. The reason is mainly because of our lack of understanding of AF mechanisms. This field has benefited from mechanistic and (or) statistical methodologies. Recent renewed interest in digital twin techniques by synergizing between mechanistic and statistical models has opened new frontiers in AF analysis. In the review, we briefly present findings that gave rise to the AF pathophysiology and current therapeutic modalities. We then summarize the achievements of digital twin technologies in three aspects: understanding AF mechanisms, screening anti-AF drugs and optimizing ablation strategies. Finally, we discuss the challenges that hinder the clinical application of the digital twin heart. With the rapid progress in data reuse and sharing, we expect their application to realize the transition from AF description to response prediction.

Zanubrutinib versus bendamustine and rituximab in untreated chronic lymphocytic leukaemia and small lymphocytic lymphoma (SEQUOIA): a randomised, controlled, phase 3 trial

BACKGROUND

Zanubrutinib is a next-generation, selective Bruton tyrosine kinase inhibitor with efficacy in relapsed chronic lymphocytic leukemia (CLL) and small lymphocytic lymphoma (SLL). We compared zanubrutinib with bendamustine-rituximab to determine its effectiveness as frontline therapy in patients with CLL or SLL.

METHODS

We conducted an open-label, multicentre, phase 3 study at 153 academic or community hospitals in 14 countries and regions. Eligible patients had untreated CLL or SLL requiring treatment as per International Workshop on CLL criteria; were aged 65 years or older, or 18 years or older and had comorbidities; and had an Eastern Cooperative Oncology Group performance status score of 0-2. A central interactive web response system randomly assigned patients without del(17)(p13·1) to zanubrutinib (group A) or bendamustine-rituximab (group B) by sequential block method (permutated blocks with a random block size of four). Patients with del(17)(p13·1) were enrolled in group C and received zanubrutinib. Zanubrutinib was administered orally at 160 mg twice per day (28-day cycles); bendamustine at 90 mg/m² of body surface area on days 1 and 2 for six cycles plus rituximab at 375 mg/m² of body surface area the day before or on day 1 of cycle 1, and 500 mg/m² of body surface area on day 1 of cycles 2-6, were administered intravenously. The primary endpoint was progression-free survival per independent review committee in the intention-to-treat population in groups A and B, with minimum two-sided α of 0·05 for superiority. Safety was analysed in all patients who received at least one dose of study treatment. The study is registered with ClinicalTrials.gov, NCT03336333, and is closed to recruitment.

FINDINGS

Between Oct 31, 2017, and July 22, 2019, 590 patients were enrolled; patients without del(17)(p13·1) were randomly assigned to zanubrutinib (group A; n=241) or bendamustine-rituximab (group B; n=238). At median follow-up of 26·2 months (IQR 23·7-29·6), median progression-free survival per independent review committee was not reached in either group (group A 95% CI not estimable [NE] to NE; group B 28·1 months to NE). Progression-free survival was significantly improved in group A versus group B (HR 0·42 [95% CI 0·28 to 0·63]; two-sided p<0·0001). The most common grade 3 or worse adverse event was neutropenia (27 [11%] of 240 patients in group A, 116 [51%] of 227 in group B, and 17 [15%] of 111 patients in group C). Serious adverse events occurred in 88 (37%) of 240 patients in group A, 113 (50%) of 227 patients in group B, and 45 (41%) of 111 patients in group C. Adverse events leading to death occurred in 11 (5%) of 240 patients in group A, 12 (5%) of 227 patients in group B, and three (3%) of 111 patients in group C, most commonly due to COVID-19 (four [2%] of 240 patients in group A), diarrhoea, and aspiration pneumonia (two each [1%] of 227 patients in group B).

INTERPRETATION

Zanubrutinib significantly improved progression-free survival versus bendamustine-rituximab, with an acceptable safety profile consistent with previous studies. These data support zanubrutinib as a potential new treatment option for untreated CLL and SLL.

FUNDING

BeiGene.

Healthcare utilization and costs associated with a diagnosis of incident atrial fibrillation

Background

Atrial fibrillation (AF) is the most common heart rhythm disorder among adults and leads to substantial morbidity and mortality.

Objectives

The purpose of the study was to provide current estimates on the incremental healthcare utilization and cost burden associated with incident AF diagnosis in the United States.

Methods

Adults with an incident diagnosis of AF (2017–2020) were identified using the Optum Clinformatics database. Propensity matching was employed to match patients with incident AF to a comparator group of non-AF patients on several demographic and clinical characteristics. Outcomes including 12-month all-cause and cardiovascular (CV)-related healthcare utilization, as well as the medical cost associated with health services use, were assessed. Logistic and general linear models were used to examine study outcomes. Sub-analyses were performed to determine the incremental AF burden by specific sex and racial/ethnic categories.

Results

A total of 79,621 patients were identified in each cohort (AF and non-AF). As compared to the non-AF cohort, patients with AF had significantly higher all-cause inpatient visits (relative risk [RR] 1.77; 95% confidence interval [CI] 1.76–1.78), CV-related inpatient visits (RR 2.51; 95% CI 2:49–2:53), and CV-related emergency room visits (RR: 2.41; 95% CI 2:35–2:47). The mean total healthcare cost for patients with AF was $27,896 more (per patient per year) than the non-AF cohort ($63,031 vs $35,135, P < .001).

Conclusion

Medical services utilization and cost were significantly higher among AF patients than non-AF patients. Early treatment is likely to be critical to addressing the considerable disease burden imposed by AF.

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.

A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology

The artificial intelligence (AI) revolution is well underway, including in the medical field, and has dramatically transformed our lives. An understanding of the basics of AI applications, their development, and challenges to their clinical implementation is important for clinicians to fully appreciate the possibilities of AI. Such a foundation would ensure that clinicians have a good grasp and realistic expectations for AI in medicine and prevent discrepancies between the promised and real-world impact. When quantifying the track record for AI applications in cardiology, we found that a substantial number of AI systems are never deployed in clinical practice, although there certainly are many success stories. Successful implementations shared the following: they came from clinical areas where large amount of training data was available; were deployable into a single diagnostic modality; prediction models generally had high performance on external validation; and most were developed as part of collaborations with medical device manufacturers who had substantial experience with implementation of new technology. When looking into the current processes used for developing AI-based systems, we suggest that expanding the analytic framework to address potential deployment and implementation issues at project outset will improve the rate of successful implementation, and will be a necessary next step for AI to achieve its full potential in cardiovascular medicine.

Impact of Atrial Fibrillation on In-Hospital Mortality and Stroke in Acute Aortic Syndromes

BACKGROUND

Acute aortic syndromes may present with a number of cardiovascular complications, including atrial fibrillation. We assessed the prevalence of atrial fibrillation in patients presenting with acute aortic syndromes and evaluated atrial fibrillation's association with in-hospital mortality and stroke.

METHODS

Consecutive patients with acute aortic syndromes admitted to a single tertiary care center from January 2015 to March 2020 were included. We identified patients with atrial fibrillation on the presenting electrocardiogram.

RESULTS

A total of 309 patients with acute aortic syndromes were included in our analyses: 148 (48%) presented with Stanford type A and 161 (52%) with Stanford type B acute aortic syndromes. Twenty-seven (8.7%) patients had atrial fibrillation on the presenting electrocardiogram: 12 (44%) with type A and 15 (56%) with type B acute aortic syndromes. Patients with atrial fibrillation were older, more likely to be white, had a higher frequency of history of cancer, peripheral artery disease, cerebrovascular disease, and heart failure with preserved ejection fraction, compared with those without atrial fibrillation. Acute aortic syndromes patients with atrial fibrillation had higher frequencies of in-hospital mortality compared with those without atrial fibrillation (40.7% vs 12.4%, P < .0001). However, stroke frequencies did not differ between the 2 groups.

CONCLUSION

In patients presenting with acute aortic syndromes and atrial fibrillation, we observed higher frequencies of in-hospital mortality, without differences in the frequencies of stroke.

Relationship between predicting bleeding complication in patients undergoing stent implantation and subsequent dual antiplatelet therapy (PRECISE-DAPT) score and mortality among patients with atrial fibrillation undergoing percutaneous coronary intervention

BACKGROUND

The predicting bleeding complication in patients undergoing stent implantation and subsequent dual antiplatelet therapy, PRECISE-DAPT (P-DAPT) score has been validated in large cohorts as an effective tool in predicting bleeding complication after dual antiplatelet therapy (DAPT) as well as in predicting in-hospital mortality. The implication of using this score to predict outcomes, including mortality in patients with atrial fibrillation (AF) undergoing PCI is unknown.

OBJECTIVE

Role of P-DAPT score to study clinical outcomes, including mortality, hospitalization, and major bleeding, particularly among patients with AF.

METHODS

This is a retrospective observational study of 18,850 consecutive patients who underwent percutaneous coronary intervention (PCI) across a large multihospital healthcare system from 2010 to 2019. Patients were stratified into four groups depending on the presence or absence of AF and P-DAPT score, with score ≥ 25 defined as high risk. The primary outcome was all-cause mortality. The secondary outcomes evaluated were hospitalization and major bleeding.

RESULTS

In the unadjusted analyses, a P-DAPT score ≥ 25, in both AF and non-AF population, was associated with increased mortality, hospitalization, and bleeding. After adjusting for baseline covariates, no significant differences in major bleeding risk were found across the four groups. However, a P-DAPT score of ≥25 in AF patients was associated with a higher risk for hospitalizations related to cardiovascular causes (HR: 2.15 95% CI 2.00-2.3, p < .0001). Among AF patients, P-DAPT score ≥ 25 was found to be strongly associated with mortality (HR 3.5; 95% CI 2.95-4.25, p < .0001) as compared with AF patients with score < 25 (HR 1.18, 95% CI 0.88-1.54, p = .26).

CONCLUSION

In this large cohort of patients undergoing PCI, the P-DAPT score can help to identify patients at high risk for long-term mortality, particularly among those with atrial fibrillation.

Age and Sex Specific Prevalence of Clinical and Screen-Detected Atrial Fibrillation in Hospitalized Patients

BACKGROUND

The prevalence of atrial fibrillation (AF) is high in older patients. The present study aimed to estimate the age and sex specific prevalence of clinical and screen-detected atrial fibrillation (AF) in hospitalized patients.

METHODS

The STAR-FIB cohort study was a prospective cohort study recruiting participants from a large source population of hospitalized patients aged 65-84 years. The estimated size of the source population was 26,035 (95% CI 25,918-26,152), and 795 consenting patients without clinical AF were included in the cohort study after stratification by sex and age (49.2% females; mean age 74.7 years). Patients in the cohort study underwent three seven-day Holter ECGs in intervals of two months to screen for AF.

RESULTS

In the source population, the estimated prevalence of clinical AF was 22.2% (95% CI 18.4-26.1), 23.8% for males (95% CI 20.9-26.6) and 19.8% for females (95% CI 17.3-22.4; p for difference between sexes, 0.004). There was a linear trend for an increase in the prevalence of clinical AF with increasing age, overall and in both sexes. In the cohort study, AF was newly diagnosed in 38 patients, for an estimated prevalence of screen-detected AF of 4.9% overall (95% CI 3.3-6.6), 5.5% in males (95% CI 3.2-7.8) and 4.0% in females (95% CI 2.0-6.0; p for difference between sexes, 0.041). The estimated prevalence of screen-detected AF in the source population was 3.8% overall, 4.2% in males and 3.2% in females.

CONCLUSION

In a large hospital-based patient population aged 65-84 years, the prevalence of clinical AF and of screen-detected AF was 22.2% and 3.8%, respectively, and significantly higher in males than females.

Atrial Fibrillation Detected by Single Timepoint Handheld ECG Screening and the Risk of Ischemic Stroke

OBJECTIVE

We evaluated stroke risk in patients with single timepoint screen-detected atrial fibrillation (AF) and the effect of oral anticoagulants (OAC).

METHODS

Consecutive patients aged ≥65 years attending medical outpatient clinics were prospectively enrolled for AF-screening using handheld single-lead ECG (AliveCor) from 12/2014 to 12/2017 (NCT02409654). Repeated screening was performed in patients with >1 visit during this period. Three cohorts were formed, screen-detected AF, clinically-diagnosed AF and no AF. Ischemic stroke risk was estimated using adjusted sub-distribution hazard ratios (aSHR) from multivariate regression and no AF as reference, and stratified according to OAC use.

RESULTS

Of 11,972 subjects enrolled, 2,238 (18.7%) had clinically-diagnosed AF at study enrollment. The yield of screen-detected AF on initial screening was 2.3% (n=223/9,734). AF was clinically-diagnosed during follow-up in 2.3% (n=216/9,440) and during subsequent screening in 71 initially screen-negative patients. Compared to no AF, patients with screen-detected AF without OAC treatment had the highest stroke risk (aSHR 2.63; 95% confidence interval 1.46-4.72), while aSHR for clinically-diagnosed AF without OAC use was 2.01 (1.54-2.62). Among screen-detected AF the risk of stroke was significantly less with OAC (no strokes in 196 person-years) compared with those not given OAC (12 strokes in 429 person-years), p=0.01.

CONCLUSION

The prognosis of single timepoint ECG screen-detected AF is not benign. The risk of stroke is high enough to warrant OAC use, and reduced by OAC.

Role of digital health in detection and management of atrial fibrillation

Atrial fibrillation is a common arrhythmia associated with significant morbidity, mortality and decreased quality of life. Mobile health devices marketed directly to consumers capable of detecting atrial fibrillation through methods including photoplethysmography, single-lead ECG as well as contactless methods are becoming ubiquitous. Large-scale screening for atrial fibrillation is feasible and has been shown to detect more cases than usual care-however, controversy still exists surrounding screening even in older higher risk populations. Given widespread use of mobile health devices, consumer-driven screening is happening on a large scale in both low-risk and high-risk populations. Given that young people make up a large portion of early adopters of mobile health devices, there is the potential that many more patients with early onset atrial fibrillation will come to clinical attention requiring possible referral to genetic arrythmia clinic. Physicians need to be familiar with these technologies, and understand their risks, and limitations. In the current review, we discuss current mobile health devices used to detect atrial fibrillation, recent and upcoming trials using them for diagnosis of atrial fibrillation, practical recommendations for patients with atrial fibrillation diagnosed by a mobile health device and special consideration in young patients.

Diagnostic Accuracy of Ambulatory Devices in Detecting Atrial Fibrillation: Systematic Review and Meta-analysis

BACKGROUND

Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide. Early diagnosis of AF is crucial for preventing AF-related morbidity, mortality, and economic burden, yet the detection of the disease remains challenging. The 12-lead electrocardiogram (ECG) is the gold standard for the diagnosis of AF. Because of technological advances, ambulatory devices may serve as convenient screening tools for AF.

OBJECTIVE

The objective of this review was to investigate the diagnostic accuracy of 2 relatively new technologies used in ambulatory devices, non-12-lead ECG and photoplethysmography (PPG), in detecting AF. We performed a meta-analysis to evaluate the diagnostic accuracy of non-12-lead ECG and PPG compared to the reference standard, 12-lead ECG. We also conducted a subgroup analysis to assess the impact of study design and participant recruitment on diagnostic accuracy.

METHODS

This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. MEDLINE and EMBASE were systematically searched for articles published from January 1, 2015 to January 23, 2021. A bivariate model was used to pool estimates of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the summary receiver operating curve (SROC) as the main diagnostic measures. Study quality was evaluated using the quality assessment of diagnostic accuracy studies (QUADAS-2) tool.

RESULTS

Our search resulted in 16 studies using either non-12-lead ECG or PPG for detecting AF, comprising 3217 participants and 7623 assessments. The pooled estimates of sensitivity, specificity, PLR, NLR, and diagnostic odds ratio for the detection of AF were 89.7% (95% CI 83.2%-93.9%), 95.7% (95% CI 92.0%-97.7%), 20.64 (95% CI 10.10-42.15), 0.11 (95% CI 0.06-0.19), and 224.75 (95% CI 70.10-720.56), respectively, for the automatic interpretation of non-12-lead ECG measurements and 94.7% (95% CI 93.3%-95.8%), 97.6% (95% CI 94.5%-99.0%), 35.51 (95% CI 18.19-69.31), 0.05 (95% CI 0.04-0.07), and 730.79 (95% CI 309.33-1726.49), respectively, for the automatic interpretation of PPG measurements.

CONCLUSIONS

Both non-12-lead ECG and PPG offered high diagnostic accuracies for AF. Detection employing automatic analysis techniques may serve as a useful preliminary screening tool before administering a gold standard test, which generally requires competent physician analyses. Subgroup analysis indicated variations of sensitivity and specificity between studies that recruited low-risk and high-risk populations, warranting future validity tests in the general population.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42020179937; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=179937.

Why translation from basic discoveries to clinical applications is so difficult for atrial fibrillation and possible approaches to improving it

Atrial fibrillation (AF) is the most common sustained clinical arrhythmia, with a lifetime incidence of up to 37%, and is a major contributor to population morbidity and mortality. Important components of AF management include control of cardiac rhythm, rate, and thromboembolic risk. In this narrative review article, we focus on rhythm-control therapy. The available therapies for cardiac rhythm control include antiarrhythmic drugs and catheter-based ablation procedures; both of these are presently neither optimally effective nor safe. In order to develop improved treatment options, it is necessary to use preclinical models, both to identify novel mechanism-based therapeutic targets and to test the effects of putative therapies before initiating clinical trials. Extensive research over the past 30 years has provided many insights into AF mechanisms that can be used to design new rhythm-maintenance approaches. However, it has proven very difficult to translate these mechanistic discoveries into clinically applicable safe and effective new therapies. The aim of this article is to explore the challenges that underlie this phenomenon. We begin by considering the basic problem of AF, including its clinical importance, the current therapeutic landscape, the drug development pipeline, and the notion of upstream therapy. We then discuss the currently available preclinical models of AF and their limitations, and move on to regulatory hurdles and considerations and then review industry concerns and strategies. Finally, we evaluate potential paths forward, attempting to derive insights from the developmental history of currently used approaches and suggesting possible paths for the future. While the introduction of successful conceptually innovative new treatments for AF control is proving extremely difficult, one significant breakthrough is likely to revolutionize both AF management and the therapeutic development landscape.

Progress in cardiovascular bioprinting

Cardiovascular disease has been the leading cause of death globally for the past 15 years. Following a major cardiac disease episode, the ideal treatment would be the replacement of the damaged tissue, due to the limited regenerative capacity of cardiac tissues. However, we suffer from a chronic organ donor shortage which causes approximately 20 people to die each day waiting to receive an organ. Bioprinting of tissues and organs can potentially alleviate this burden by fabricating low cost tissue and organ replacements for cardiac patients. Clinical adoption of bioprinting in cardiovascular medicine is currently limited by the lack of systematic demonstration of its effectiveness, high costs, and the complexity of the workflow. Here, we give a concise review of progress in cardiovascular bioprinting and its components. We further discuss the challenges and future prospects of cardiovascular bioprinting in clinical applications.

Heart Disease and Stroke Statistics-2021 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 2021 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, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease.

RESULTS

Each of the 27 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, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Transfer Learning for Detection of Atrial Fibrillation in Deterministic Compressive Sensed ECG

Atrial Fibrillation (AF) is a cardiac condition resulting from uncoordinated contraction of the atria which may lead to an increase in the risk of heart attacks, strokes, and death. AF symptoms may go undetected and may require longterm monitoring of electrocardiogram (ECG) to be detected. Long-term ECG monitoring can generate a large amount of data which can increase power, storage, and the wireless transmission bandwidth of monitoring devices. Compressive Sensing (CS) is compression technique at the sampling stage which may save power, storage, and wireless bandwidth of monitoring devices. The reconstruction of compressive sensed ECG is a computationally expensive operation; therefore, detection of AF in compressive sensed ECG is warranted. This paper presents preliminary results of using deep learning to detect AF in deterministic compressive sensed ECG. MobileNetV2 convolutional neural network (CNN) was used in this paper. Transfer learning was utilized to leverage a pre-trained CNN with the final two layers retrained using 24 records from the Long-Term Atrial Fibrillation Database. The Short-Term Fourier Transform was used to generate spectrograms that were fed to the CNN. The CNN was tested on the MIT-BIH Atrial Fibrillation Database at the uncompressed, 50%, 75%, and 95% compressed ECG. The performance of the CNN was evaluated using weighted average precision (AP) and area under the curve (AUC) of the receiver operator curve (ROC). The CNN had AP of 0.80, 0.70, 0.70, and 0.57 at uncompressed, 50%, 75%, and 95% compression levels. The AUC was 0.87, 0.78, 0.79, and 0.75 at each compression level. The preliminary results show promise for using deep learning to detect AF in compressive sensed ECG.Clinical Relevance-This paper confirms that AF can be detected in compressive sensed ECG using deep learning, This will facilitate long-term ECG monitoring using wearable devices and will reduce adverse complications resulting from undiagnosed AF.

Outcome of Cardiac Monitor During Sleep Study for Screening of Subclinical Atrial Fibrillation

Background

There is growing evidence of a strong association between obstructive sleep apnea (OSA) and cardiovascular co-morbidities including atrial fibrillation (AF). We wanted to assess the usefulness of the overnight cardiac monitoring to screen for AF during the sleep study in patients newly diagnosed with OSA, in order to establish the usefulness of overnight active screening for subclinical AF during the sleep study in these patients.

Methods

A retrospective study in patients with new diagnosis of OSA carried out between January 2014 and December 2019 in the sleep clinic at King Abdulaziz University Hospital, Jeddah, Saudi Arabia. All patients newly diagnosed with OSA (apnea-hypopnea index >5) were selected to undergo a clinical questionnaire regarding symptoms, co-morbidities and risk factors. Subjects with history of cardiac arrhythmias or having anti-arrhythmic treatment were excluded. Eligible patients underwent an overnight rhythm monitoring to screen for AF or any rhythm disturbance.

Results

We included 250 respective patients with OSA, 54% were males and 82% aged more than 35 years. The majority of patients were married (83%), of Saudi nationality (81%), and 90% were overweight or obese, apnea hypopnea index (AHI) was mild (5-14) in 30%, moderate (15-29) in 38% and severe (30 or more) in 32% of the patients. No cardiac arrhythmia was detected in all the study population, while only two patients complained of palpitations and was due to sinus tachycardia. Assessment of other risk factors showed 26% cases of diabetes mellitus, 39% of hypertension, 1% of renal failure, 9% of ischemic heart disease, 17% of thyroid dysfunction, 6% of stroke and 4% of dyslipidemia.

Conclusion

The findings of this study show null incidence of cardiac arrhythmia during the apnea-hypopnea episodes in a cohort of patients with confirmed OSA. However, in view of the frequently reported association, the screening for subclinical atrial fibrillation needs long-term rhythm surveillance and should be targeted to symptomatic patients.

Epidemiology of Atrial Fibrillation in the 21st Century: Novel Methods and New Insights

Accompanying the aging of populations worldwide, and increased survival with chronic diseases, the incidence and prevalence of atrial fibrillation (AF) are rising, justifying the term global epidemic. This multifactorial arrhythmia is intertwined with common concomitant cardiovascular diseases, which share classical cardiovascular risk factors. Targeted prevention programs are largely missing. Prevention needs to start at an early age with primordial interventions at the population level. The public health dimension of AF motivates research in modifiable AF risk factors and improved precision in AF prediction and management. In this review, we summarize current knowledge in an attempt to untangle these multifaceted associations from an epidemiological perspective. We discuss disease trends, preventive opportunities offered by underlying risk factors and concomitant disorders, current developments in diagnosis and risk prediction, and prognostic implications of AF and its complications. Finally, we review current technological (eg, eHealth) and methodological (artificial intelligence) advances and their relevance for future prevention and disease management.

Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm

Aims: As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-prediction algorithms may help to efficiently identify people with undiagnosed AF. However, the cost-effectiveness of targeted screening remains uncertain. This study aimed to assess the cost-effectiveness of targeted screening, informed by a machine learning (ML) risk prediction algorithm, to identify patients with AF.Methods: Cost-effectiveness analyses were undertaken utilizing a hybrid screening decision tree and Markov disease progression model. Costs and outcomes associated with the detection of AF compared traditional systematic and opportunistic AF screening strategies to targeted screening informed by a ML risk prediction algorithm. Model analyses were based on adults ≥50 years and adopted the UK NHS perspective.Results: Targeted screening using the ML risk prediction algorithm required fewer patients to be screened (61 per 1,000 patients, compared to 534 and 687 patients in the systematic and opportunistic strategies) and detected more AF cases (11 per 1,000 patients, compared to 6 and 8 AF cases in the systematic and opportunistic screening strategies). The targeted approach demonstrated cost-effectiveness under base case settings (cost per QALY gained of £4,847 and £5,544 against systematic and opportunistic screening respectively). The targeted screening strategy was predicted to provide an additional 3.40 and 2.05 QALYs per 1,000 patients screened versus systematic and opportunistic strategies. The targeted screening strategy remained cost-effective in all scenarios evaluated.Limitations: The analysis relied on assumptions that include the extended period of patient life span and the lack of consideration for treatment discontinuations/switching, as well as the assumption that the ML risk-prediction algorithm will identify asymptomatic AF.Conclusions: Targeted screening using a ML risk prediction algorithm has the potential to enhance the clinical and cost-effectiveness of AF screening, improving health outcomes through efficient use of limited healthcare resources.

Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports on 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 2020 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, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals.

RESULTS

Each of the 26 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, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Accuracy of the Apple Watch 4 to Measure Heart Rate in Patients With Atrial Fibrillation

Background Wearable wrist-monitors offer an unobtrusive way to acquire heart rate data in an efficient manner. Previous work in this field has focused on studying healthy subjects during exercise but has yet to assess the efficacy of these devices in patients suffering from common cardiac arrhythmias such as atrial fibrillation. Objective The objective of this pilot study was to assess the accuracy of the Apple Watch heart rate monitor in fifty patients experiencing atrial fibrillation compared to telemetry. Results Results from this pilot clinical study demonstrated a correlation coefficient of 0.7 between all readings on the Apple Watch and telemetry. Furthermore, the Apple Watch assessed heart rate more accurately in patients who were in atrial fibrillation than in those that were not (r_c = 0.86, patients in AF, vs. r_c = 0.64, patients not in AF). Clinical Impact The presented data from this pilot study suggests that caution should be noted before using the Apple Watch 4 wearable wrist monitor to monitor heart rate in patients with cardiac arrhythmias such as atrial fibrillation.

Apple Watch, Wearables, and Heart Rhythm: where do we stand?

Atrial fibrillation (AF) poses a major health concern in the United States by affecting over 5 million people accounting for at least 15% to 25% of strokes. It can be asymptomatic or subclinical with its first presentation being stroke in 18%, and AF being only detected at the time of stroke. With evidence of subclinical AF associated with increased risk of ischemic stroke, recent developments indeed point towards wearables, especially smart watches, being quite effective and representing a novel method for screening for silent AF in the general population, and thereby reducing mortality and morbidity associated with it. This manuscript aims to review whether the photoplethysmography (PPG) technology, employed in the wearables to monitor heart rate, is accurate enough to aid in the diagnosis of AF that may remain asymptomatic or paroxysmal. It also explores the option of actually employing this method in the general population, the feasibility of this mode of diagnosis, sensitivity and specificity of this method compared to the conventional electrocardiogram (EKG), and the actual follow up with a practitioner and subsequent treatment of AF, if diagnosed. We conducted a Medline search using various combinations of "smart watch" "atrial fibrillation" "wearables", and "Kardia" to identify pivotal randomized trials published before June 1, 2019, for inclusion in this review. Concurrently, major practice guidelines, trial bibliographies, and pertinent reviews were examined to ensure inclusion of relevant trials. A consensus among the authors was used to choose items for narrative inclusion. The following section reviews data from pivotal trials to determine the effectiveness of smart watch technology in detecting AF in the general population. Trials reviewed evaluated apple watch, Kardia, Samsung wearables in diagnosis of AF. The fact that there is an increase in consumer use of wearables, smart devices, which can serve as health monitoring devices that can be used as a non-invasive, ambulatory assessment of heart rate and rhythm, is definitely novel. Intermittent short EKG recordings repeated over a longer-term period produced significantly better sensitivity for AF detection, with 4 times as many cases diagnosed compared with a single time-point measurement. Since there are limitations and further research into this new field is required, the wearable technology may not serve as the ultimate tool for diagnosis of AF, rather a nidus for the general population to seek medical advice for confirmation on being notified of having an irregular rhythm leading to prevention of morbidity and mortality associated with it.

Mobile Health Advances in Physical Activity, Fitness, and Atrial Fibrillation: Moving Hearts

The growing recognition that "health" takes place outside of the hospital and clinic, plus recent advances in mobile and wearable devices, have propelled the field of mobile health (mHealth). Cardiovascular disease and prevention are major opportunities for mHealth, as mobile devices can monitor key physiological signals (e.g., physical activity, heart rate and rhythm) for promoting healthy behaviors, detecting disease, and aid in ongoing care. In this review, the authors provide an update on cardiovascular mHealth by highlighting recent progress and challenges with mobile and wearable devices for assessing and promoting physical activity and fitness, and for monitoring heart rate and rhythm for the detection and management of atrial fibrillation.

Risk Factors for Symptomatic Atrial Fibrillation-Analysis of an Outpatient Database

Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in practice and is the leading cause of debilitating strokes with significant economic burden. It is currently not known whether asymptomatic undiagnosed AF should be treated if detected by various screening methods. Currently, United States guidelines have no recommendations for identifying patients with asymptomatic undiagnosed AF due to lack of evidence. The American Heart Association Center for Health Technology & Innovation undertook a plan to identify tools in 3 phases that may be useful in improving outcomes in patients with undiagnosed AF. In phase I we sought to identify AF risk factors that can be used to develop a risk score to identify high-risk patients using a large commercial insurance dataset. The principal findings of this study show that individuals at high risk for AF are those with advanced age, the presence of heart failure, coronary artery disease, hypertension, metabolic disorders, and hyperlipidemia. Our analysis also found that chronic respiratory failure was a significant risk factor for those over 65 years of age and chronic kidney disease for those less than 65 years of age.