Silent atrial fibrillation: epidemiology, diagnosis, and clinical impact

Atrial Fibrillation Screening in the Elderly: A Cost-Effectiveness Analysis for Public Health Policy

Background

Atrial fibrillation (AF) screening identifies undiagnosed patients who can benefit from anticoagulant therapy, thereby reducing the risk of ischemic stroke. However, the long-term outcomes and costs related to population screening for this purpose in the Asian elderly remain unknown.

Objectives

This study aimed to evaluate the cost-effectiveness of population screening for AF in the elderly in Taiwan and explore the optimal screening strategy from the health care sector's perspective.

Methods

Using a Markov decision-analytic model, we simulated lifetime outcomes and costs of AF screening in a cohort of 10,000 individuals aged 75. Comparative analyses with a nonscreening approach assessed prevented ischemic strokes, quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs). Sensitivity analyses were conducted to address model uncertainty, while scenario analyses were performed to determine the optimal age and frequency of screening.

Results

One-time population screening for AF among 75-year-olds prevented 45 ischemic strokes and gained 47.42 QALYs, with an additional cost of $592,450 (ICER: $12,493 per QALY gained). The cost-effectiveness of screening remained robust in sensitivity analyses, with anticoagulant effectiveness in ischemic stroke prevention being the most influential factor. Similar ICERs were observed for individuals aged 65 to 80 years. Implementing annual screening for individuals aged 65 to 80 years yielded an ICER of approximately $18,000 per QALY gained.

Conclusions

Both one-time and annual population screening for AF in individuals aged 65 to 80 years appear to be cost-effective. Further research is needed to assess budgetary and feasibility aspects to establish an optimal screening program.

Relationship between predictive factors and atrial high-rate episodes in heart failure with reduced ejection fraction patients with cardiac implantable electronic devices

OBJECTIVE

Atrial high-rate episodes (AHRE) are atrial tachyarrhythmia episodes detected by implanted cardiac devices, characterised by an atrial rate exceeding 180-190 beats per minute. Recent studies have linked AHRE to the development of atrial fibrillation (AF) and increased stroke risk, especially when episodes last longer than 5-6 min. This study aimed to evaluate the relationship between predictive factors and the occurrence of AHRE in heart failure with reduced ejection fraction (HFrEF) patients with cardiac implantable electronic devices (CIEDs).

METHODS

This single-centre, retrospective study included a cohort of 155 consecutive HFrEF patients with CIEDs, but without a diagnosis of AF, enrolled between January 2023 and December 2023. Patients were stratified based on the presence of AHRE detected during device interrogation.

RESULTS

The average age of the patients was 54 ± 14 years, and 83.2% of the study population were male. Patients with AHRE had a reduced left ventricular ejection fraction (LVEF) (p = 0.026) and an increased left atrial diameter (p < 0.001) compared to the group without AHRE. Patients with AHRE had elevated levels of creatinine, uric acid, and TSH compared to those without AHRE (p = 0.006, p = 0.021, p = 0.009, respectively). In the univariable logistic regression analysis, LVEF (HR = 0.690, 95% CI = 0.610-0.913, p = 0.013), left atrial diameter (HR = 8.215, 95% CI = 1.557-43.34, p < 0.001), creatinine (HR = 7.369, 95% CI = 1.749-31.05, p = 0.006), uric acid (HR = 1.073, 95% CI = 1.062-1.196, p = 0.020), total cholesterol (HR = 0.989, 95% CI = 0.979-0.999, p = 0.031), and C-reactive protein levels (HR = 1.146, 95% CI = 1.002-1.131, p = 0.047) were identified as independent predictors of AHRE. In multivariable logistic regression analysis, left ventricular ejection fraction (HR = 0.890, 95% CI = 0.795-0.998, p = 0.046), left atrial diameter (HR = 8.215, 95% CI = 1.557-43.34, p < 0.001), and uric acid concentration (HR = 1.650, 95% CI = 1.063-2.561, p = 0.025) were identified as predictors of atrial high-rate events.

CONCLUSION

LVEF, left atrial diameter, and uric acid level were found to be independent predictors of AHRE in patients with HFrEF. Identifying AHRE is crucial for risk stratification and guiding therapeutic decisions to improve patient outcome.

Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores

BACKGROUND AND AIMS

Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical models and AF polygenic score (PGS).

METHODS

Electrocardiograms in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with pre-existing AF. The primary outcome was incident AF at 5 years. An ECG-AI model was developed by splitting patients into non-overlapping data sets: 70% for training, 10% for validation, and 20% for testing. The performance of ECG-AI, clinical models, and PGS was assessed in the test data set. The ECG-AI model was externally validated in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital data set.

RESULTS

A total of 669 782 ECGs from 145 323 patients were included. Mean age was 61 ± 15 years, and 58% were male. The primary outcome was observed in 15% of patients, and the ECG-AI model showed an area under the receiver operating characteristic (AUC-ROC) curve of .78. In time-to-event analysis including the first ECG, ECG-AI inference of high risk identified 26% of the population with a 4.3-fold increased risk of incident AF (95% confidence interval: 4.02-4.57). In a subgroup analysis of 2301 patients, ECG-AI outperformed CHARGE-AF (AUC-ROC = .62) and PGS (AUC-ROC = .59). Adding PGS and CHARGE-AF to ECG-AI improved goodness of fit (likelihood ratio test P < .001), with minimal changes to the AUC-ROC (.76-.77). In the external validation cohort (mean age 59 ± 18 years, 47% male, median follow-up 1.1 year), ECG-AI model performance remained consistent (AUC-ROC = .77).

CONCLUSIONS

ECG-AI provides an accurate tool to predict new-onset AF in a tertiary cardiac centre, surpassing clinical and PGS.

Rates of Visual Field Progression Before and After the Onset of Atrial Fibrillation

PRCIS

This case-control study investigated the effect of atrial fibrillation (AF) on the progression of glaucoma. The presence of AF and related microvascular damage was associated with a slightly faster visual field loss in glaucoma patients.

PURPOSE

To investigate the effect of atrial fibrillation (AF) on glaucoma progression.

METHODS

In this longitudinal case-control study, a total of 144 eyes from 105 patients with primary open angle glaucoma were included. Forty-eight eyes of case developed AF during the follow-up followed for 15.6 years. Ninety-six eyes of control that did not have AF at baseline or during follow-up matched for age, baseline glaucoma severity, and follow-up period were followed for an average of 14.7 years. Mixed-effects linear models were used to calculate the difference in the VF MD slopes before and after the AF. CHADS 2 and CHA 2 DS 2 -VASc scores were used to evaluate the risk of thrombosis event, and related microvascular damage was assessed based on these scores.

RESULTS

The rate of VF MD change was -0.20 (-0.42 to 0.02) dB/y before AF and -0.28 (-0.47 to -0.09) dB/y after AF for the patients with AF, and -0.21 (-0.25 to -0.17) dB/y for the control. In the multivariable models, the VF slope difference before and after the onset of AF [-0.10 (-0.14 to -0.05) dB/y, P <0.001], higher CHADS 2 score [-0.07 (-0.13 to 0.00) dB/y per 1 unit, P =0.040], and higher CHA 2 DS 2 -VASc score [-0.05 (-0.10 to 0.00) dB/y per 1 unit, P =0.039] were associated with faster VF MD loss.

CONCLUSIONS

The presence of atrial fibrillation and related microvascular damage might accelerate visual field loss. This underscores the need for a comprehensive medical history and management of cardiovascular risk factors to mitigate increased VF loss in glaucoma.

The burden of atrial fibrillation on emergency medical services: A population-based cohort study

BACKGROUND

Atrial fibrillation (AF) is a growing burden on healthcare resources, despite improvements in prevention and management. AF is a common cause of hospitalisation, and Emergency Medical Services (EMS) use. However, there is a paucity of data describing the burden of AF on EMS. We aimed to determine the prevalence, characteristics, and outcomes of patients presenting with AF to EMS using a large population-based sample.

METHODS

Consecutive attendances for AF in Victoria, Australia (January 2015-June 2019) were included if patients had a diagnosis of "AF" or "arrhythmia" with AF on electrocardiogram. Data were individually linked to emergency, hospital, and mortality records.

RESULTS

Of 2,613,056 EMS attendances, 16,525 were a first attendance for AF and linked to hospital records. Median (IQR) age was 76 (67,84) years (43% female). Seventy-eight percent had high thromboembolic risk (CHA2DS2-VASc score ≥ 2), and 72% had a heart rate ≥ 100 bpm. Forty-two percent of patients received no treatment by paramedics and 99.4% were transported to hospital. Fifty-three percent were discharged from ED. Median length of hospital stay was 2 days. Of 2542 cases reattended for AF, 19% occurred within 30 days, with increased odds for females and those of low socioeconomic status. Overall, 24% died during the study period, 12% within 30 days. Increasing age, heart failure, stroke, COPD, and low socioeconomic status increased the odds of 30-day mortality.

CONCLUSIONS

EMS utilisation for AF is common and associated with frequent reattendance. Further studies are required to investigate novel pathways of care to reduce AF burden on healthcare systems.

Ablation therapy following unsuccessful electrical cardioversion in patients with persistent atrial fibrillation

Electrical cardioversion (ECV) a widely utilized intervention for persistent atrial fibrillation (AF) aimed at restoring sinus rhythm. However, ECV can be ineffective, raising questions about subsequent treatment options. This study aimed to compare the outcomes of non-ablation therapy versus ablation therapy following unsuccessful ECV. A total of 125 consecutive patients with persistent AF who underwent unsuccessful ECV between November 2017 and August 2023 was included in this retrospective analysis. Of these, 51.2% received only medical therapy (non-ablation therapy group, n = 64), while 48.8% underwent AF ablation (ablation therapy group, n = 61). Various ablation methods were employed, including catheter and thoracoscopic ablation. Ablation therapy was associated with significantly better AF-free survival compared to non-ablation therapy [hazard ratio (HR), 0.37; 95% confidence interval (CI) 0.22-0.61; p < 0.01]. There was no difference of AF-free survival between catheter ablation and thoracoscopic ablation groups (HR 0.79, 95% CI 0.34-1.83; p = 0.58). AF duration > 5 year (HR 1.51; 95% CI 0.930-2.437; p = 0.10), BMI ≤ 25 kg/m2 (HR 1.61; 95% CI 1.004-2.581; p = 0.05) and diabetes (HR 2.38; 95% CI 0.902-6.266; p = 0.08) were considerable as predictor of AF recurrence. Ablation therapy following unsuccessful ECV was associated with maintaining sinus rhythm, regardless of the specific ablation method utilized.

A Comprehensive Review of Cryptogenic Stroke and Atrial Fibrillation: Real-World Insights Into the Role of Insertable Cardiac Monitors

Cryptogenic stroke, a subtype of ischemic stroke with no identifiable cause after comprehensive evaluation, presents a unique challenge in stroke prevention. Atrial fibrillation (AF), a common risk factor for ischemic stroke, is often underdiagnosed in these patients due to its intermittent, asymptomatic nature. Early detection of AF is critical, as anticoagulation therapy significantly reduces the risk of recurrent stroke in AF patients. However, traditional short-term monitoring methods frequently fail to identify paroxysmal AF. Insertable cardiac monitors (ICMs) offer a novel solution by providing continuous, long-term heart rhythm monitoring, which has proven effective in detecting occult AF. Real-world data further support the clinical value of ICMs in guiding the initiation of anticoagulation therapy, ultimately improving stroke prevention strategies. Despite some limitations, such as false positives and the invasive nature of the device, ICMs have emerged as a critical tool in modern stroke management. As technology evolves, future advancements may further enhance AF detection by integrating artificial intelligence and wearable devices. This review provides a comprehensive overview of the role of AF in cryptogenic stroke, the importance of early detection, and the growing significance of ICMs in clinical practice.

Atrial Fibrillation Prevalence Rates and Its Association with Cardiovascular-Kidney-Metabolic Factors: SIMETAP-AF Study

Background and Objectives: Atrial fibrillation (AF) is the most frequent arrhythmia and the main cause of hospital admissions for cardioembolic stroke. The SIMETAP research project aims to update the prevalence rates of cardiovascular, renal, or metabolic factors and to evaluate their respective associations with factors that could be related. The present study aims to assess the AF prevalence rates in an adult population and its association with cardiovascular-kidney-metabolic (CKM) factors. Materials and Methods: This cross-sectional observational study was conducted in a primary care setting, with a population-based random sample of 6588 people aged 18.0-102.8 years. Crude and adjusted prevalence rates of AF were calculated. The associations of CKM factors with AF were assessed using bivariate and multivariate analysis. Results: The age- and sex-adjusted prevalence rates of AF were 2.9% in the overall adult population, 6.1% in the population aged ≥50 years, and 12.9% in the population aged ≥70 years, with no significant differences by sex. AF prevalence in the population under 50 years of age barely reached 1‰. Heart failure (HF), hypertension, chronic kidney disease (CKD), stroke, low HDL-cholesterol, and prediabetes were independent CKM factors associated with AF in the overall population, as were the same factors, except prediabetes, in the population ≥50 years old (p < 0.001). High or very high vascular risk was present in 92.4% [95% CI: 89.1-95.7]) of the population with AF. Conclusions: The adjusted prevalence rate of AF in the population aged 50 years or older was 6.1%, twice that of the overall adult population and half that of the population aged 70 years or older. The main independent CKM factors associated with AF were HF, stroke, CKD, hypertension, and low HDL-cholesterol.

Implementation of an atrial fibrillation better care (ABC) pathway management strategy: Findings from the Iranian registry of atrial fibrillation

Introduction

The Atrial Fibrillation Better Care (ABC) pathway is such an integrated care approach, recommended in guidelines. The aim of this service evaluation study was to evaluate the impact of implementing the ABC pathway in Iranian population.

Methods

In this prospective longitudinal cohort study, consecutive patients with a diagnosis of AF were initially evaluated. Patients at baseline were evaluated in hence of adherence to ABC pathway strategy. After study enrolment, first and second follow-up service evaluations were conducted at 6 and 12 months, respectively.

Results

The use of vitamin K antagonists (VKA) decreased from 25.1 % at enrolment to 13.8 % at follow-up; instead, non-VKA oral anticoagulants (NOAC) utilization increased from 40.0 % to 86.1 %, while antiplatelet treatment decreased from 34.9 % to 0 %. Use of antihypertensive drugs, ARBs, diuretics, and statins increased after implementation of the ABC pathway. Implementation of the ABC pathway approach led to a decrease in the occurrence of stroke/TIA (from 6.3 % to 2.2 %, p = 0.002), systemic thromboembolism (from 1.4 % to 0.0 %, p = 0.04), nose bleeds (from 0.8 % to 0.6 %, p = 0.04), skin bruising (from 1.2 % to 0.0 %, p = 0.002), and heart failure (from 7.7 % to 4.7 %, p = 0.04). The proportion of patients in EHRA Class I-II increased from 93.3 % at enrolment to 98.1 % at follow-up.

Conclusion

In this first study from a Middle East population, compliance with ABC pathway strategy in the management of AF was associated with optimization of management and general improvements in patient outcomes during follow-up.

A Narrowband IoT Personal Sensor for Long-Term Heart Rate Monitoring and Atrial Fibrillation Detection

Long-term patient monitoring is required for detection of episodes of atrial fibrillation, one of the most widespread cardiac pathologies. Today, the most used non-invasive technique is Holter electrocardiographic (ECG) monitoring, which can often prove ineffective because of the short duration of recordings (e.g., one day). Other techniques such as photo-plethysmography are adopted by smartwatches for much longer duration monitoring, but this has the disadvantage of offering only intermittent measurements. This study proposes an Internet of Things (IoT) sensor that can provide a very long period of continuous monitoring. The sensor consists of an ECG-integrated Analog Front End (MAX30003), a microcontroller (STM32F401RE), and an IoT narrowband module (STEVAL-STMODLTE). The instantaneous heart rate is extracted from the ECG recording in real time. At intervals of two minutes, the sequence of inter-beat intervals is transmitted to an IoT cloud platform (ThingSpeak). Settled atrial fibrillation event recognition software runs on the cloud and generates alerts when it recognizes such arrhythmia. Performances of the proposed sensor were evaluated by generating analog ECG signals from a public dataset of ECG signals with atrial fibrillation episodes, the MIT-BIH Atrial Fibrillation Database, each recording lasting approximately 10 h. Software implementing the Lorentz algorithm, one of the best detectors of atrial fibrillation, was implemented on the cloud platform. The accuracy, sensitivity, and specificity in recognizing atrial fibrillation episodes of the proposed system was calculated by comparison with a cardiologist's reference data. Across all patients, the proposed method achieved an accuracy of 0.88, a sensitivity 0.71, and a specificity 0.99. The results obtained suggest that the developed system can continuously record and transmit heart rhythms effectively and efficiently and, in addition, offers considerable performance in recognizing atrial fibrillation episodes in real time.

Factors Affecting Late Atrial Fibrillation and Its Association With Coronary Artery Bypass Outcomes

BACKGROUND

Although predictors and outcomes of postoperative atrial fibrillation (POAF) are well studied, evidence is lacking concerning postdischarge late/recurrent atrial fibrillation (AF). This study evaluated factors affecting late/recurrent AF and its association with coronary artery bypass grafting (CABG) outcomes in a real-world setting.

METHODS

From 2012 through 2016, 5175 patients were included. Independent factors associated with late/recurrent AF were identified in a competing risk setting. Cox proportional hazard regression was used to evaluate the association between late/recurrent AF and study outcomes, consisting of all-cause mortality, major adverse cardio-cerebrovascular events, acute coronary syndrome, cerebrovascular events, and heart failure admissions.

RESULTS

During a median follow-up of 60 months (quartile 1-quartile 3, 59.3-60.7 months), late/recurrent AF developed in 85 patients (1.64%). Independent factors associated with late/recurrent AF were age (subdistribution hazard ratio [sHR], 1.04; 95% CI, 1.02-1.07), left-ventricular ejection fraction (sHR, 0.97; 95% CI, 0.95-0.99), length of stay (sHR, 1.02; 95% CI, 1.01-1.04), and POAF (sHR, 4.02; 95% CI, 2.50-6.45). Late/recurrent AF was not significantly associated with all-cause mortality and major adverse cardio-cerebrovascular events at unadjusted or adjusted levels (adjusted hazard ratio, 0.80 [95% CI, 0.50-1.28] and 0.74 [95% CI, 0.48-1.13], respectively). Nevertheless, it significantly increased the unadjusted risk of cerebrovascular events (hazard ratio, 2.28; 95% CI, 01.07-4.87), which disappeared after adjustments.

CONCLUSIONS

Patients with advanced age, a lower left-ventricular ejection fraction, and POAF are more likely to have late/recurrent clinical AF. Albeit counterintuitive, late/recurrent AF was not independently associated with worse midterm post-CABG outcomes. These observations need to be further elucidated in larger-scale studies and interpreted in the context of a developing country with limited resources for late AF surveillance.

Cardiac Blood-Based Biomarkers of Myocardial Stress as Predictors of Atrial Fibrillation Development in Patients With Embolic Stroke of Undetermined Source/Cryptogenic Stroke: A Systematic Review and Meta-Analysis

BACKGROUND AND PURPOSE

Undiagnosed atrial fibrillation (AF) is a major risk factor for stroke that can go unnoticed in individuals with embolic stroke of undetermined source (ESUS) or cryptogenic stroke (CS). Early detection is critical for stroke prognosis and secondary prevention. This study aimed to determine if blood biomarkers of myocardial stress can accurately predict AF in patients with ESUS/CS, which would allow the identification of those who would benefit from closer monitoring.

METHODS

In February 2023 we performed a systematic date-unrestricted search of three databases for studies on patients with ESUS/CS who were subsequently diagnosed with AF. We examined the relationships between AF and serum myocardial stress markers such as brain natriuretic peptide (BNP), N-terminal-pro-BNP (NT-proBNP), midregional proatrial natriuretic peptide, and troponin.

RESULTS

Among the 1,527 studies reviewed, 23 eligible studies involving 6,212 participants, including 864 with AF, were analyzed. A meta-analysis of 9 studies indicated that they demonstrated a clear association between higher NT-proBNP levels and an increased risk of AF, with adjusted and raw data indicating 3.06- and 9.03-fold higher AF risks, respectively. Lower NT-proBNP levels had a pooled negative predictive value of 91.7%, indicating the potential to rule out AF with an 8% false-negative rate.

CONCLUSIONS

Further research is required to fully determine the potential of biomarkers for AF detection after stroke, as results from previous studies lack homogeneity. However, lower NT-proBNP levels have potential in ruling out AF in patients with ESUS/CS. Combining them with other relevant biomarkers may enhance the precision of identifying patients who will not benefit from extended monitoring, which would optimize resource allocation and patient care.

Deep learning evaluation of echocardiograms to identify occult atrial fibrillation

Atrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0.96 (0.95-0.96), AUPRC 0.91 (0.90-0.92)). Among TTEs in sinus rhythm, the model predicted the presence of concurrent paroxysmal AF (AUC 0.74 (0.71-0.77), AUPRC 0.19 (0.16-0.23)). Model discrimination remained similar in an external cohort of 10,203 TTEs (AUC of 0.69 (0.67-0.70), AUPRC 0.34 (0.31-0.36)). Performance held across patients who were women (AUC 0.76 (0.72-0.81)), older than 65 years (0.73 (0.69-0.76)), or had a CHA2DS2VASc ≥2 (0.73 (0.79-0.77)). The model performed better than using clinical risk factors (AUC 0.64 (0.62-0.67)), TTE measurements (0.64 (0.62-0.67)), left atrial size (0.63 (0.62-0.64)), or CHA2DS2VASc (0.61 (0.60-0.62)). An ensemble model in a cohort subset combining the TTE model with an electrocardiogram (ECGs) deep learning model performed better than using the ECG model alone (AUC 0.81 vs. 0.79, p = 0.01). Deep learning using TTEs can predict patients with active or occult AF and could be used for opportunistic AF screening that could lead to earlier treatment.

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.

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.

Ratio of early transmitral inflow velocity to early diastolic strain rate predicts atrial fibrillation following acute myocardial infarction

The ratio of early transmitral filling velocity to early diastolic strain rate (E/SRe) has been proposed as a new non-invasive measurement of left ventricular filling pressure. We aimed to investigate the ability of E/SRe to predict atrial fibrillation (AF) after ST-elevation myocardial infarction (STEMI). This was a prospective cohort study of patients (n = 369) with STEMI. Patients underwent an echocardiographic examination a median of two days after pPCI. By echocardiography, transmitral early filling velocity (E) was measured by pulsed-wave Doppler, and early diastolic strain rate (SRe) was measured by speckle tracking of the left ventricle. E was indexed to SRe and the early myocardial relaxation velocity (e') to obtain the E/SRe and E/e', respectively. The endpoint was new-onset AF. During follow-up (median 5.6 years, IQR: 5.0-6.1 years), 23 (6%) of the 369 patients developed AF. In unadjusted analyses, both E/SRe and E/e' were significantly associated with AF [E/SRe: HR = 1.06; (1.03-1.10); p < 0.001, per 10 increase] and [E/e': HR = 1.11 (1.05-1.17); p < 0.001, per 1 increase] and had equal Harrell's C-statistic of 0.71. However, only E/SRe remained an independent predictor after multivariable adjustments for clinical and echocardiographic parameters [E/SRe: HR = 1.06 (1.00-1.11); p = 0.044, per 10 increase]. E/SRe was further significantly associated with AF in patients with E/e' < 14 HR = 1.09 (1.01-1.17); p = 0.030, per 10 increase), also after multivariable adjustments. E/SRe is an independent predictor of AF in STEMI patients, even in subjects with seemingly normal filling pressure.

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.

A Simple Score for Predicting Paroxysmal Atrial Fibrillation in Patients with Embolic Stroke of Undetermined Source in a Tunisian Cohort Study

BACKGROUND

The annualized recurrent stroke rate in patients with Embolic Stroke of Undetermined Source (ESUS) under antiplatelet therapy is around 4.5%. Only a fraction of these patients will develop atrial fibrillation (FA), to which a stroke can be attributed retrospectively. The challenge is to identify patients at risk of occult AF during follow-up.

OBJECTIVE

This work aims to determine clinical factors and electrocardiographic and ultrasound parameters that can predict occult AF in patients with ESUS and build a simple predictive score applicable worldwide.

METHODS

This is a single-center, registry-based retrospective study conducted at the stroke unit of Sahloul University Hospital, Sousse, Tunisia, between January 2016 and December 2020. Consecutive patients meeting ESUS criteria were monitored for a minimum of one year, with a standardized follow-up consisting of outpatient visits, including ECG every three months and a new 24-hour Holter monitoring in case of palpitations. We performed multivariate stepwise regression to identify predictors of new paroxysmal AF among initial clinical, electrocardiographic (ECG and 24-hour Holter monitoring) and echocardiographic parameters. The coefficient of each independent covariate of the fitted multivariable model was used to generate an integerbased point-scoring system.

RESULTS

Three hundred patients met the criteria for ESUS. Among them, 42 (14%) patients showed at least one episode of paroxysmal AF during a median follow-up of two years. In univariate analysis, age, gender, coronary artery disease, history of ischemic stroke, higher NIHSS at admission and lower NIHSS at discharge, abnormal P-wave axis, prolonged P-wave duration, premature atrial contractions (PAC) frequency of more than 500/24 hours, and left atrial (LA) mean area of more than 20 cm2 were associated with the risk of occurrence of paroxysmal AF. We proposed an AF predictive score based on (1.771 x NIHSS score at admission) + (10.015 x P-wave dispersion; coded 1 if yes and 0 if no) + (9.841x PAC class; coded 1 if ≥500 and 0 if no) + (9.828x LA class surface; coded 1 if ≥20 and 0 if no) + (0.548xNIHSS score at discharge) + 0.004. A score of ≥33 had a sensitivity of 76% and a specificity of 93%.

CONCLUSION

In this cohort of patients with ESUS, NIHSS at both admission and discharge, Pwave dispersion, PAC≥500/24h on a 24-hour Holter monitoring, and LA surface area≥20 cm2 provide a simple AF predictive score with very reasonable sensitivity and specificity and is applicable almost worldwide. An external validation of this score is ongoing.

Vitamin D deficiency and atrial fibrillation: A cross sectional single center study

BACKGROUND

Atrial fibrillation (AF) augments the risk of stroke by 4-5 times. Vitamin D is pivotal in numerous metabolic pathways. A handful of studies have explored the correlation between vitamin D deficiency (VDD) and AF outcomes. Hence, the authors sought to assess the relationship between VDD and AF outcomes.

METHODS

From December 2021 to February 2023, 190 patients with AF were incorporated into the authors' study. Given the seasonal fluctuation of vitamin D levels, these levels were examined from the start of December until the end of March.

RESULTS

The final analysis comprised 190 patients (55.8% male) with an average age of 46.22±15.03. Vitamin D deficiency, insufficiency, and sufficiency were noted in 77 (40.5%), 46 (24.2%), and 67 (35.3%) patients, respectively. Fatigue and syncope were significantly more prevalent in the VDD group than in other groups. Three-vessel disease was more frequent in the VDD group (p-value=0.04). Mortality was more prevalent in patients with VDD (6.31%) compared to the VDI (2.10%) and VDS (0.05%) groups (p = 0.03). Successful cardioversion was significantly more prevalent in the VDS group (p = 0.03).

CONCLUSION

A sufficient level of vitamin D was linked with a better response to cardioversion. However, low vitamin D levels are correlated with higher mortality in AF patients.

Evaluation of atrial fibrillation using wearable device signals and home blood pressure data in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) Study: A Subgroup Analysis (MIPACT-AFib)

Background

The rising adoption of wearable technology increases the potential to identify arrhythmias. However, specificity of these notifications is poorly defined and may cause anxiety and unnecessary resource utilization. Herein, we report results of a follow-up screening protocol for incident atrial fibrillation/flutter (AF) within a large observational digital health study.

Methods

The MIPACT Study enrolled 6,765 adult patients who were provided an Apple Watch and blood pressure (BP) monitors. From March to July 2019, participants were asked to contact the study team for any irregular heart rate (HR) notification. They were assessed using structured questionnaires and asked to provide 6 Apple Watch EKGs. Those with arrhythmias or non-diagnostic EKGs were sent 7-day monitors. The EHR was reviewed after 3 years to determine if participants developed arrhythmias.

Results

86 participants received notifications and met inclusion criteria. Mean age was 50.5 (SD 16.9) years, and 46 (53.3%) were female. Of 76 participants assessed by the study team, 32 (42.1%) reported anxiety surrounding notifications. Of 59 participants who sent at least 1 EKG, 52 (88.1%) were in sinus rhythm, 3 (5.1%) AF, 2 (3.4%) indeterminate, and 2 (3.4%) sinus bradycardia. Cardiac monitor demonstrated AF in 2 of 3 participants with AF on Apple Watch EKGs. 2 contacted their PCPs and were diagnosed with AF. In total, 5 cases of AF were diagnosed with 1 additional case identified during EHR review.

Conclusion

Wearable devices produce alarms that can frequently be anxiety provoking. Research is needed to determine the implications of these alarms and appropriate follow-up.

Diagnosis and Treatment of New-Onset Perioperative Atrial Fibrillation

This article reviews medical and surgical risk factors for developing atrial fibrillation (AF), the most common sustained dysrhythmia in the United States. Evidence for assessment and management of patients with AF, including AF newly identified in the preoperative clinic, immediately preoperatively, intraoperatively, and unstable AF, is presented. A stepwise approach to guide anesthetic decision-making in the assessment of newly identified preoperative AF is proposed. Anesthetic considerations, including the potential impacts of anesthetic and vasopressor selection, and current evidence related to rate control and rhythm control via pharmacologic or electrical cardioversion as well as anticoagulation strategies are discussed.

Improving Automatic Smartwatch Electrocardiogram Diagnosis of Atrial Fibrillation by Identifying Regularity within Irregularity

Smartwatches equipped with automatic atrial fibrillation (AF) detection through electrocardiogram (ECG) recording are increasingly prevalent. We have recently reported the limitations of the Apple Watch (AW) in correctly diagnosing AF. In this study, we aim to apply a data science approach to a large dataset of smartwatch ECGs in order to deliver an improved algorithm. We included 723 patients (579 patients for algorithm development and 144 patients for validation) who underwent ECG recording with an AW and a 12-lead ECG (21% had AF and 24% had no ECG abnormalities). Similar to the existing algorithm, we first screened for AF by detecting irregularities in ventricular intervals. However, as opposed to the existing algorithm, we included all ECGs (not applying quality or heart rate exclusion criteria) but we excluded ECGs in which we identified regular patterns within the irregular rhythms by screening for interval clusters. This "irregularly irregular" approach resulted in a significant improvement in accuracy compared to the existing AW algorithm (sensitivity of 90% versus 83%, specificity of 92% versus 79%, p < 0.01). Identifying regularity within irregular rhythms is an accurate yet inclusive method to detect AF using a smartwatch ECG.

Characterizing Real-World Implementation of Consumer Wearables for the Detection of Undiagnosed Atrial Fibrillation in Clinical Practice: Targeted Literature Review

BACKGROUND

Atrial fibrillation (AF), the most common cardiac arrhythmia, is often undiagnosed because of lack of awareness and frequent asymptomatic presentation. As AF is associated with increased risk of stroke, early detection is clinically relevant. Several consumer wearable devices (CWDs) have been cleared by the US Food and Drug Administration for irregular heart rhythm detection suggestive of AF. However, recommendations for the use of CWDs for AF detection in clinical practice, especially with regard to pathways for workflows and clinical decisions, remain lacking.

OBJECTIVE

We conducted a targeted literature review to identify articles on CWDs characterizing the current state of wearable technology for AF detection, identifying approaches to implementing CWDs into the clinical workflow, and characterizing provider and patient perspectives on CWDs for patients at risk of AF.

METHODS

PubMed, ClinicalTrials.gov, UpToDate Clinical Reference, and DynaMed were searched for articles in English published between January 2016 and July 2023. The searches used predefined Medical Subject Headings (MeSH) terms, keywords, and search strings. Articles of interest were specifically on CWDs; articles on ambulatory monitoring tools, tools available by prescription, or handheld devices were excluded. Search results were reviewed for relevancy and discussed among the authors for inclusion. A qualitative analysis was conducted and themes relevant to our study objectives were identified.

RESULTS

A total of 31 articles met inclusion criteria: 7 (23%) medical society reports or guidelines, 4 (13%) general reviews, 5 (16%) systematic reviews, 5 (16%) health care provider surveys, 7 (23%) consumer or patient surveys or interviews, and 3 (10%) analytical reports. Despite recognition of CWDs by medical societies, detailed guidelines regarding CWDs for AF detection were limited, as was the availability of clinical tools. A main theme was the lack of pragmatic studies assessing real-world implementation of CWDs for AF detection. Clinicians expressed concerns about data overload; potential for false positives; reimbursement issues; and the need for clinical tools such as care pathways and guidelines, preferably developed or endorsed by professional organizations. Patient-facing challenges included device costs and variability in digital literacy or technology acceptance.

CONCLUSIONS

This targeted literature review highlights the lack of a comprehensive body of literature guiding real-world implementation of CWDs for AF detection and provides insights for informing additional research and developing appropriate tools and resources for incorporating these devices into clinical practice. The results should also provide an impetus for the active involvement of medical societies and other health care stakeholders in developing appropriate tools and resources for guiding the real-world use of CWDs for AF detection. These resources should target clinicians, patients, and health care systems with the goal of facilitating clinician or patient engagement and using an evidence-based approach for establishing guidelines or frameworks for administrative workflows and patient care pathways.

Cardiovascular Manifestations of Human Monkeypox Virus: An Updated Review

Cardiovascular manifestations in human monkeypox virus (MPXV) infection has gained increasing recognition as significant complications with both social and clinical implications. Myocarditis, viral pericarditis, heart failure, and arrhythmias can occur, leading to adverse effects on individuals' health and quality of life. Understanding the detailed pathophysiology of these cardiovascular manifestations is essential for improved diagnosis and management. The social implications of these cardiovascular complications are multifaceted, ranging from public health concerns and the impact on individuals' quality of life to psychological distress and social stigma. Clinically, diagnosing, and managing these complications present challenges, requiring a multidisciplinary approach and specialized care. The burden on healthcare resources necessitates preparedness and resource allocation to effectively address these complications. We delve into the pathophysiological mechanisms involved, including viral-induced cardiac damage, immune response, and inflammatory processes. Additionally, we explore the types of cardiovascular manifestations and their clinical presentations. Addressing cardiovascular manifestations' social and clinical implications in MPXV infection requires a comprehensive approach involving healthcare professionals, public health authorities, and communities. By prioritizing research, enhancing diagnosis and treatment strategies, and promoting preventive measures, we can mitigate the impact of these complications, improve patient care, and protect public health.

Reaching the Frail Elderly for the Diagnosis and Management of Atrial Fibrillation-REAFEL

BACKGROUND

Frail elderly patients are exposed to suffering strokes if they do not receive timely anticoagulation to prevent stroke associated to atrial fibrillation (AF). Evaluation in the cardiological ambulatory can be cumbersome as it often requires repeated visits.

AIM

To develop and implement CardioShare, a shared-care model where primary care leads patient management, using a compact Holter monitor device with asynchronous remote support from cardiologists.

METHODS

CardioShare was developed in a feasibility phase, tested in a pragmatic cluster randomization trial (primary care clinics as clusters), and its implementation potential was evaluated with an escalation test. Mixed methods were used to evaluate the impact of this complex intervention, comprising quantitative observations, semi-structured interviews, and workshops.

RESULTS

Between February 2020 and December 2021, 314 patients (30% frail) were included, of whom 75% had AF diagnosed/not found within 13 days; 80% in both groups avoided referral to cardiologists. Patients felt safe and primary care clinicians satisfied. In an escalation test, 58 primary-care doctors evaluated 93 patients over three months, with remote support from four hospitals in the Capital Region of Denmark.

CONCLUSIONS

CardioShare was successfully implemented for AF evaluation in primary care.

Obesity-Related Atrial Fibrillation: Cardiac Manifestation of a Systemic Disease

Atrial fibrillation (AF) is the most common arrhythmia worldwide and is associated with increased morbidity and mortality. The mechanisms underlying AF are complex and multifactorial. Although it is well known that obesity is a strong risk factor for AF, the mechanisms underlying obesity-related AF are not completely understood. Current evidence proposes that in addition to overall hemodynamic changes due to increased body weight, excess adiposity raises systemic inflammation and oxidative stress, which lead to adverse atrial remodeling. This remodeling includes atrial fibrosis, atrial dilation, decreased electrical conduction between atrial myocytes, and altered ionic currents, making atrial tissue more vulnerable to both the initiation and maintenance of AF. However, much remains to be learned about the mechanistic links between obesity and AF. This knowledge will power the development of novel diagnostic tools and treatment options that will help combat the rise of the global AF burden among the obesity epidemic.

The causal effects of genetically determined human blood metabolites on the risk of atrial fibrillation

Background

Blood metabolites have been found related to atrial fibrillation (AF), but the causal role is still unclear. Mendel randomization (MR) can give information about the causality between blood metabolites and AF.

Methods

Two-sample MR analysis was used to evaluate the causality between 486 blood metabolites and AF. Firstly, the genome-wide association study (GWAS) data for AF (from Nielsen et al.) was analyzed and some metabolites were identified. Then another GWAS data for AF (from Roselli et al.) was repeatedly analyzed to verify the results. Inverse variance weighted method was mainly used to determine the causality, and MR-egger, Weighted Median, and MR-PRESSO models were used as supplements of MR. Cochran's Q test was used to assess heterogeneity. And MR-Egger intercept and MR-PRESSO global test were performed to measure pleiotropy.

Results

The study used Bonferroni's corrected P value (P < 1.03 × 10-4) as the significance threshold. After MR analysis and replication analysis, we found two overlapped metabolites. Among which tryptophan betaine was the most significant causal metabolite in both AF GWAS data (from Nielsen et al.) (odds ratio (OR) = 0.83, 95% confidence interval (CI) = 0.76-0.90, P = 9.37 × 10-6) and AF GWAS data (from Roselli et al.) (OR = 0.82, 95% CI = 0.76-0.88, P = 2.00 × 10-7), while uridine was nominally significant metabolites in both AF GWAS data (from Nielsen et al.) (OR = 0.58, 95% CI = 0.40-0.84, P = 0.004) and AF GWAS data (from Roselli et al.) (OR = 0.56, 95% CI = 0.35-0.88, P = 0.01). And the results of sensitivity analysis showed that none of them had obvious heterogeneity or pleiotropy.

Conclusion

The study identified several blood metabolites that were causally related to AF, which may provide new perspectives on the pathogenesis of AF.

The use of digital health in heart rhythm care

INTRODUCTION

Digital health is a broad term that includes telecommunication technologies to collect, share and manipulate health information to improve patient health and health care services. With the growing use of wearables, artificial intelligence, machine learning, and other novel technologies, digital health is particularly relevant to the field of cardiac arrhythmias, with roles pertinent to education, prevention, diagnosis, management, prognosis, and surveillance.

AREAS COVERED

This review summarizes information on the clinical use of digital health technology in arrhythmia care and discusses its opportunities and challenges.

EXPERT OPINION

Digital health has begun to play an essential role in arrhythmia care regarding diagnostics, long-term monitoring, patient education and shared decision making, management, medication adherence, and research. Despite remarkable advances, integrating digital health technologies into healthcare faces challenges, including patient usability, privacy, system interoperability, physician liability, analysis and incorporation of the huge amount of real-time information from wearables, and reimbursement. Successful implementation of digital health technologies requires clear objectives and deep changes to existing workflows and responsibilities.

High-Dose Oral Amiodarone for Cardioversion of Atrial Fibrillation: A Case Report

With the rising prevalence of atrial fibrillation (AF), it has become a global health problem with increasing complications and high medical costs. Here, we describe the case of a 52-year-old woman with chest discomfort and frequent palpitations for the last few months. A careful clinical and instrumental examination showed that the patient had AF. Sinus rhythm was restored by cardioversion using high-dose oral amiodarone therapy. Although this medication can be an alternative with several advantages over electrical cardioversion in the future, further studies are needed to establish its efficacy and safety profile.

Genome and atrial fibrillation

Atrial fibrillation (AF), the most common type of arrhythmia, can cause several adverse effects, such as stroke, heart failure, and cognitive dysfunction, also in addition to reducing quality of life and increasing mortality. Evidence suggests that AF is caused by a combination of genetic and clinical predispositions. In line with this, genetic studies on AF have progressed significantly through linkage studies, genome-wide association studies, use of polygenic risk scores, and studies on rare coding variations, gradually elucidating the relationship between genes and the pathogenesis and prognosis of AF. This article will review current trends in genetic analysis concerning AF.

Does early detection of atrial fibrillation reduce the risk of thromboembolic events? Rationale and design of the Heartline study

BACKGROUND

The impact of using direct-to-consumer wearable devices as a means to timely detect atrial fibrillation (AF) and to improve clinical outcomes is unknown.

METHODS

Heartline is a pragmatic, randomized, and decentralized application-based trial of US participants aged ≥65 years. Two randomized cohorts include adults with possession of an iPhone and without a history of AF and those with a diagnosis of AF taking a direct oral anticoagulant (DOAC) for ≥30 days. Participants within each cohort are randomized (3:1) to either a core digital engagement program (CDEP) via iPhone application (Heartline application) and an Apple Watch (Apple Watch Group) or CDEP alone (iPhone-only Group). The Apple Watch Group has the watch irregular rhythm notification (IRN) feature enabled and access to the ECG application on the Apple Watch. If an IRN notification is issued for suspected AF then the study application instructs participants in the Apple Watch Group to seek medical care. All participants were "watch-naïve" at time of enrollment and have an option to either buy or loan an Apple Watch as part of this study. The primary end point is time from randomization to clinical diagnosis of AF, with confirmation by health care claims. Key secondary endpoint are claims-based incidence of a 6-component composite cardiovascular/systemic embolism/mortality event, DOAC medication use and adherence, costs/health resource utilization, and frequency of hospitalizations for bleeding. All study assessments, including patient-reported outcomes, are conducted through the study application. The target study enrollment is approximately 28,000 participants in total; at time of manuscript submission, a total of 26,485 participants have been enrolled into the study.

CONCLUSION

The Heartline Study will assess if an Apple Watch with the IRN and ECG application, along with application-facilitated digital health engagement modules, improves time to AF diagnosis and cardiovascular outcomes in a real-world environment.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04276441.

Cost-Effectiveness of Atrial Fibrillation Screening Strategies: A Systematic Review

Background

Atrial fibrillation (AF) is the most common cardiac arrhythmia. AF is associated with an increased risk of stroke. We aimed to review systematically the cost-effectiveness of screening strategies for patients with AF.

Methods

To find related research and articles, articles published in Iranian and international databases by using a combination of MeSH (Medical Subject Headings) terms and based on inclusion and exclusion criteria were searched and reviewed until Dec 2020. The main outcome measures of the final articles were incremental cost-effectiveness ratios (ICER) per gained or additional quality-adjusted life years (QALYs), additional case detected, and avoided stroke.

Results

Out of 3,360 studies found, finally, fifteen studies were included in the research. The lowest ICER numerical value was 78.39 for AF screening using ECG for 65-85 yr old Japanese women. The highest value of this index is equal to 70864.31 for performing ECG monitoring for more than 60 d for Canadians over 80 yr without AF history. In two studies, the results were expressed with the years of life gained (YLG measure. Of course, in one study, the results were not reported with this measure, and in one study, the results were reported with ICER.

Conclusion

Most of the studies acknowledged the cost-effectiveness of different AF screening strategies. However, studies that confirmed the cost-effectiveness of population-based screening were more than studies that confirmed the cost-effectiveness of other screening strategies.

The N-Glycosylation of Total Plasma Proteins and IgG in Atrial Fibrillation

Atrial fibrillation is a disease with a complex pathophysiology, whose occurrence and persistence are caused not only by aberrant electrical signaling in the heart, but by the development of a susceptible heart substrate. These changes, such as the accumulation of adipose tissue and interstitial fibrosis, are characterized by the presence of inflammation. N-glycans have shown great promise as biomarkers in different diseases, specifically those involving inflammatory changes. To assess the changes in the N-glycosylation of the plasma proteins and IgG in atrial fibrillation, we analyzed the N-glycosylation of 172 patients with atrial fibrillation, before and six months after a pulmonary vein isolation procedure, with 54 cardiovascularly healthy controls. An analysis was performed using ultra-high-performance liquid chromatography. We found one oligomannose N-glycan structure from the plasma N-glycome and six IgG N-glycans, mainly revolving around the presence of bisecting N-acetylglucosamine, that were significantly different between the case and control groups. In addition, four plasma N-glycans, mostly oligomannose structures and a derived trait that was related to them, were found to be different in the patients who experienced an atrial fibrillation recurrence during the six-month follow-up. IgG N-glycosylation was extensively associated with the CHA2DS2-VASc score, confirming its previously reported associations with the conditions that make up the score. This is the first study looking at the N-glycosylation patterns in atrial fibrillation and warrants further investigation into the prospect of glycans as biomarkers for atrial fibrillation.

Potential of electronic devices for detection of health problems in older adults at home: A systematic review and meta-analysis

OBJECTIVE

The aim of this review was to evaluate the overall diagnostic performance of e-devices for detection of health problems in older adults at home.

METHODS

A systematic review was conducted following the PRISMA-DTA guidelines.

RESULTS

31 studies were included with 24 studies included in meta-analysis. The included studies were divided into four categories according to the signals detected: physical activity (PA), vital signs (VS), electrocardiography (ECG) and other. The meta-analysis showed the pooled estimates of sensitivity and specificity were 0.94 and 0.98 respectively in the 'VS' group. The pooled sensitivity and specificity were 0.97 and 0.98 respectively in the 'ECG' group.

CONCLUSIONS

All kinds of e-devices perform well in diagnosing the common health problems. While ECG-based health problems detection system is more reliable than VS-based ones. For sole signal detection system has limitation in diagnosing specific health problems, more researches should focus on developing new systems combined of multiple signals.

A Smartwatch System for Continuous Monitoring of Atrial Fibrillation in Older Adults After Stroke or Transient Ischemic Attack: Application Design Study

BACKGROUND

The prevalence of atrial fibrillation (AF) increases with age and can lead to stroke. Therefore, older adults may benefit the most from AF screening. However, older adult populations tend to lag more than younger groups in the adoption of, and comfort with, the use of mobile health (mHealth) apps. Furthermore, although mobile apps that can detect AF are available to the public, most are designed for intermittent AF detection and for younger users. No app designed for long-term AF monitoring has released detailed system design specifications that can handle large data collections, especially in this age group.

OBJECTIVE

This study aimed to design an innovative smartwatch-based AF monitoring mHealth solution in collaboration with older adult participants and clinicians.

METHODS

The Pulsewatch system is designed to link smartwatches and smartphone apps, a website for data verification, and user data organization on a cloud server. The smartwatch in the Pulsewatch system is designed to continuously monitor the pulse rate with embedded AF detection algorithms, and the smartphone in the Pulsewatch system is designed to serve as the data-transferring hub to the cloud storage server.

RESULTS

We implemented the Pulsewatch system based on the functionality that patients and caregivers recommended. The user interfaces of the smartwatch and smartphone apps were specifically designed for older adults at risk for AF. We improved our Pulsewatch system based on feedback from focus groups consisting of patients with stroke and clinicians. The Pulsewatch system was used by the intervention group for up to 6 weeks in the 2 phases of our randomized clinical trial. At the conclusion of phase 1, 90 trial participants who had used the Pulsewatch app and smartwatch for 14 days completed a System Usability Scale to assess the usability of the Pulsewatch system; of 88 participants, 56 (64%) endorsed that the smartwatch app is "easy to use." For phases 1 and 2 of the study, we collected 9224.4 hours of smartwatch recordings from the participants. The longest recording streak in phase 2 was 21 days of consecutive recordings out of the 30 days of data collection.

CONCLUSIONS

This is one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring. In this paper, we report on the system's usability and opportunities to increase the acceptability of mHealth solutions among older patients with cognitive impairment.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03761394; https://www.clinicaltrials.gov/ct2/show/NCT03761394.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1016/j.cvdhj.2021.07.002.

Atrial fibrillation: Epigenetic aspects and role of sodium-glucose cotransporter 2 inhibitors

Atrial fibrillation (AF) is the most frequent arrhythmia and is associated with substantial morbidity and mortality. Pathophysiological aspects consist in the activation of pro-fibrotic signaling and Ca²⁺ handling abnormalities at atrial level. Structural and electrical remodeling creates a substrate for AF by triggering conduction abnormalities and cardiac arrhythmias. The care of AF patients focuses predominantly on anticoagulation, symptoms control and the management of risk factors and comorbidities. The goal of AF therapy points to restore sinus rhythm, re-establish atrioventricular synchrony and improve atrial contribution to the stroke volume. New layer of information to better comprehend AF pathophysiology, and identify targets for novel pharmacological interventions consists of the epigenetic phenomena including, among others, DNA methylation, histone modifications and noncoding RNAs. Moreover, the benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in diabetic and non-diabetic patients at cardiovascular risk as well as emerging evidence on the ability of SGLT2i to modify epigenetic signature in cardiovascular diseases provide a solid background to investigate a possible role of this drug class in the onset and progression of AF. In this review, following a summary of pathophysiology and management, epigenetic mechanisms in AF and the potential of sodium-glucose SGLT2i in AF patients are discussed.

Artificial intelligence-enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation

Background

Paroxysmal atrial fibrillation (AF) often eludes early diagnosis, resulting in significant morbidity and mortality. Artificial intelligence (AI) has been used to predict AF from sinus rhythm electrocardiograms (ECGs), but AF prediction using sinus rhythm mobile electrocardiograms (mECG) remains unexplored.

Objective

The purpose of this study was to investigate the utility of AI to predict AF events prospectively and retrospectively using sinus rhythm mECG data.

Methods

We trained a neural network to predict AF events from sinus rhythm mECGs obtained from users of the Alivecor KardiaMobile 6L device. We tested our model on sinus rhythm mECGs within ±0-2 days, ±3-7 days, and ±8-30 days from AF events to determine the optimal screening window. Finally, we tested our model on mECGs from before an AF event to determine whether AF can be predicted prospectively.

Results

We included 73,861 users with 267,614 mECGs (mean age 58.14 years; 35% women). Users with paroxysmal AF contributed 60.15% of mECGs. Model performance on the test set comprising control and study samples across all windows of interest showed an area under the curve (AUC) score of 0.760 (95% confidence interval [CI] 0.759-0.760), sensitivity of 0.703 (95% CI 0.700-0.705), specificity of 0.684 (95% CI 0.678-0.685), and accuracy of 69.4% (95% CI 0.692-0.700). Model performance was better on ±0-2 day samples (sensitivity 0.711; 95% CI 0.709-0.713) and worse on the ±8-30 day window (sensitivity 0.688; 95% CI 0.685-0.690), with performance on the ±3-7 day window falling in between (sensitivity 0.708; 95% CI 0.704-0.710).

Conclusion

Neural networks can predict AF using a widely scalable and cost-effective mobile technology prospectively and retrospectively.

Correlation between Serum Myosin Light Chain 4 Levels and Recurrence after Radiofrequency Ablation in Patients with Atrial Fibrillation

Atrial fibrillation (AF) is the most common arrhythmia that is harmful to human health. This study aims to explore the relationship between myosin light chain 4 (MYL4) and AF recurrence after radiofrequency ablation (RFA). Patients with AF (n = 85) were enrolled, and healthy subjects (n = 90) with normal sinus rhythm and no previous history of AF were selected as controls. The serum levels of MYL4, transforming growth factor (TGF) -β1, and procollagen type-I C-terminal propeptide (PICP) were determined. The correlation between MYL4 and atrial fibrosis remodeling indicators (TGF-β1/PICP) and left atrial diameter (LAD) was analyzed. The influence of MYL4 on AF recurrence after RFA was evaluated, and the independent correlation between them was assessed. Patients with AF and the controls showed no significant differences in age, gender, body mass index, systolic blood pressure, diastolic blood pressure, left ventricular ejection fraction, triglycerides, total cholesterol, high-density lipoprotein, low-density lipoprotein, white blood cell count, neutrophil/lymphocyte ratio, brain natriuretic peptide, and history of smoking, drinking, hypertension, and diabetes (P > 0.05), but with increased LAD in patients with AF (P < 0.01). Serum MYL4 level was reduced in patients with AF (0.6 ± 0.2) compared with that of controls (0.1 ± 0.6) (P < 0.01), and it was negatively correlated with TGF-β1, PICP, and LAD (r = -0.2389, P < 0.05; r = -0.5174, P < 0.01; r = -0.3191; P < 0.01). Low levels of MYL4 increased the risk of AF recurrence after RFA (χ² = 16.64; P < 0.0001). A low MYL4 level in patients with AF showed a poorer prognosis. Serum MYL4 level and AF type were independent risk factors affecting AF recurrence after RFA.

Relationship between nutritional status and length of hospital stay among patients with atrial fibrillation - a result of the nutritional status heart study

Background

Nutritional status is related to the prognosis and length of hospital stay (LOHS) of patients with atrial fibrillation (AF). This study aimed to assess how nutritional status affects LOHS for patients with AF.

Methods

We performed retrospective analysis of the medical records of 1,813 patients admitted urgently with a diagnosis of AF to the Institute of Heart Diseases of the University Clinical Hospital in Wroclaw, Poland.

Results

In total, 1,813 patients were included in the analysis. The average LOHS in the entire group was 3.53 ± 3.41 days. The mean BMI was 28.7 kg/m² (SD: 5.02). Patients who were hospitalized longer were statistically more likely to have a Nutritional Risk Score (NRS) ≥3 (p = 0.028). A higher percentage of longer hospitalized patients with LDL levels below 70 mg/dl (p < 0.001) and those with HDL ≥40 mg/dl (p < 0.001) were observed. Study participants with NRS ≥3 were an older group (M = 76.3 years), with longer mean LOHS (M = 4.44 days). The predictors of LOHS in the univariate model were age (OR = 1.04), LDL (OR = 0.99), HDL (OR = 0.98), TC (OR = 0.996), CRP (OR = 1, 02, p < 0.001), lymphocytes (OR = 0.97, p = 0.008) and in the multivariate model were age, LDL (mg/dl), HDL (mg/dl), Na, and K.

Conclusion

For nutritional status, factors indicating the risk of prolonged hospitalization in patients with AF are malnutrition, lower serum LDL, HDL, potassium, and sodium levels identified at the time of admission to the cardiology department. Assessment of nutritional status in patients with AF is important both in the context of evaluating obesity and malnutrition status, as both conditions can alter the prognosis of patients. Further studies are needed to determine the exact impact of the above on the risk of prolonged hospitalization.

Atrial Fibrillation, thromboembolic risk, and the potential role of the natriuretic peptides, a focus on BNP and NT-proBNP - A narrative review

Atrial fibrillation (AF) is one of the most commonly encountered arrythmia in clinical practice. AF itself can be driven by genetic predisposition, ectopic electrical activity, and abnormal atrial tissue substrates. Often there is no single etiological mechanism, but rather a combination of factors that feed back to remodel and worsen tissue substrate, "AF begets AF". The clinical consequences of AF can often include emboli, heart failure, and early mortality. The classical AF cardioembolic (CE) concept requires thrombus formation in the left atrial appendage, with subsequent embolization. The temporal dissociation between AF occurrence and CE events has thrown doubt on AF as the driver of this mechanism. Instead, there has been a resurgence of the "atrial cardiomyopathy" (ACM) concept. An ACM is proposed as a potential mechanism of embolic disease through promotion of prothrombotic mechanisms, with AF instead reflecting atrial disease severity. Regardless, AF has been implicated in 25% to 30% of cryptogenic strokes. Natriuretic peptide(NP)s have been shown to be elevated in AF, with higher levels of both NT-proBNP and BNP being predictive of incidental AF. NPs potentially reflect the atrial environment and could be used to identify an underlying ACM. Therefore, this narrative review examines this evidence and mechanisms that may underpin the role of NPs in identifying atrial dysfunction, with focus on both, BNP and NTproBNP. We explore their potential role in the prediction and screening for both, ACM and AF. Moreover, we compare both NPs directly to ascertain a superior biomarker.

Correlation Between Serum Albumin and D-Dimer Levels in 909 Patients with Non-Valvular Atrial Fibrillation: A Retrospective Study from a Single Center in China

BACKGROUND D-dimer level can reflect the hypercoagulable state of atrial fibrillation (AF) and predict thromboembolic events. However, no effective indicator associated with D-dimer of AF patients has been found to prevent thromboembolic events in AF. This retrospective study from a single center aimed to investigate the correlation between serum albumin and D-dimer levels in 909 patients with non-valvular AF (NVAF) and 653 subjects in sinus rhythm. MATERIAL AND METHODS A total of 909 NVAF patients and 653 sex- and age-matched sinus rhythm participants were used to compare serum albumin and D-dimer levels. Serum albumin was determined by colorimetry, and D-dimer level was determined by latex-enhanced photoimmunoassay. We analyzed the correlation of serum albumin and D-dimer with NVAF by correlation analysis, logistic regression analysis, and receiver operating characteristic (ROC) curve. RESULTS Albumin (P<0.001) and D-dimer (P<0.001) were significantly associated with NVAF. Among NVAF patients, D-dimer level was negatively correlated with albumin levels (P<0.001), and albumin level was an independent risk factor of abnormal D-dimer level (>0.5 ug/mL), which was also an effective predictor of abnormal D-dimer level (the area under the ROC curve was 0.77, P<0.001), and the optimal cutoff value was 36.95 g/L. CONCLUSIONS Serum albumin and D-dimer levels were significantly associated with NVAF. In NVAF patients, D-dimer level was inversely correlated with albumin levels, and albumin level was an independent risk factor and effective predictor of abnormal D-dimer level. Close examination and supplementation of serum albumin can prevent thromboembolic events, but further clinical research and confirmation are needed.

Neurovisceral interactions within the brain-heart axis as the basis of neurocardiology

The use of a systematic approach to the study of the etiology of a certain pathology makes it possible to improve the understanding of its pathogenesis, as well as to develop more effective diagnostic and therapeutic approaches, including improving the prediction of its risk. Within this review, we will consider such an area of interdisciplinary research as neurocardiology, which studies the brain-heart axis. Examples of cardiovascular diseases associated with organic and functional disorders of this axis will be considered, as well as the prospects for research in this area and their translational significance for clinical medicine.

Multimodal Approach for the Prediction of Atrial Fibrillation Detected After Stroke: SAFAS Study

Background

Intensive screening for atrial fibrillation (AF) has led to a better recognition of this cause in stroke patients. However, it is currently debated whether AF Detected After Stroke (AFDAS) has the same pathophysiology and embolic risk as prior-to-stroke AF. We thus aimed to systematically approach AFDAS using a multimodal approach combining clinical, imaging, biological and electrocardiographic markers.

Methods

Patients without previously known AF admitted to the Dijon University Hospital (France) stroke unit for acute ischemic stroke were prospectively enrolled. The primary endpoint was the presence of AFDAS at 6 months, diagnosed through admission ECG, continuous electrocardiographic monitoring, long-term external Holter during the hospital stay, or implantable cardiac monitor if clinically indicated after discharge.

Results

Of the 240 included patients, 77 (32%) developed AFDAS. Compared with sinus rhythm patients, those developing AFDAS were older, more often women and less often active smokers. AFDAS patients had higher blood levels of NT-proBNP, osteoprotegerin, galectin-3, GDF-15 and ST2, as well as increased left atrial indexed volume and lower left ventricular ejection fraction. After multivariable analysis, galectin-3 ≧ 9 ng/ml [OR 3.10; 95% CI (1.03–9.254), p = 0.042], NT-proBNP ≧ 290 pg/ml [OR 3.950; 95% CI (1.754–8.892, p = 0.001], OPG ≥ 887 pg/ml [OR 2.338; 95% CI (1.015–5.620), p = 0.046) and LAVI ≥ 33.5 ml/m2 [OR 2.982; 95% CI (1.342–6.625), p = 0.007] were independently associated with AFDAS.

Conclusion

A multimodal approach combining imaging, electrocardiography and original biological markers resulted in good predictive models for AFDAS. These results also suggest that AFDAS is probably related to an underlying atrial cardiopathy.

Clinical Trial Registration

[www.ClinicalTrials.gov], identifier [NCT03570060].

Performance of an electronic health record-based predictive model to identify patients with atrial fibrillation across countries

Background

Atrial fibrillation (AF) burden on patients and healthcare systems warrants innovative strategies for screening asymptomatic individuals.

Objective

We sought to externally validate a predictive model originally developed in a German population to detect unidentified incident AF utilising real-world primary healthcare databases from countries in Europe and Australia.

Methods

This retrospective cohort study used anonymized, longitudinal patient data from 5 country-level primary care databases, including Australia, Belgium, France, Germany, and the UK. The study eligibility included adult patients (≥45 years) with either an AF diagnosis (cases) or no diagnosis (controls) who had continuous enrolment in the respective database prior to the study period. Logistic regression was fitted to a binary response (yes/no) for AF diagnosis using pre-determined risk factors.

Results

AF patients were from Germany (n = 63,562), the UK (n = 42,652), France (n = 7,213), Australia (n = 2,753), and Belgium (n = 1,371). Cases were more likely to have hypertension or other cardiac conditions than controls in all validation datasets compared to the model development data. The area under the receiver operating characteristic (ROC) curve in the validation datasets ranged from 0.79 (Belgium) to 0.84 (Germany), comparable to the German study model, which had an area under the curve of 0.83. Most validation sets reported similar specificity at approximately 80% sensitivity, ranging from 67% (France) to 71% (United Kingdom). The positive predictive value (PPV) ranged from 2% (Belgium) to 16% (Germany), and the number needed to be screened was 50 in Belgium and 6 in Germany. The prevalence of AF varied widely between these datasets, which may be related to different coding practices. Low prevalence affected PPV, but not sensitivity, specificity, and ROC curves.

Conclusions

AF risk prediction algorithms offer targeted ways to identify patients using electronic health records, which could improve screening number and the cost-effectiveness of AF screening if implemented in clinical practice.

Investigation of the Evolution of Wavelet Higher-Order Dynamics in Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with significant morbidity and mortality. Owing to the advances in sensor technology and the emergence of wearable devices that enable daily self-monitoring, ECG signal processing methods for the automatic detection of AF are more pertinent than ever. In this paper, we investigate the use of wavelet higher-order statistics (WHOS) for feature extraction and differentiation between normal sinus rhythm and AF. The proposed approach captures the evolution of the WHOS dynamics and quantifies the changes in the time-varying characteristics of the frequency couplings caused by AF. Results obtained from the statistical analysis of a dataset of 5834 single-lead ECG recordings, reveal 46/50 statistically significant features and provide insight into the complexity of the evolution of the ECG non-linearities during AF.

Impact of dronedarone on patients with atrial fibrillation and diabetes: A sub-analysis of the ATHENA and EURIDIS/ADONIS studies

AIM

This post hoc analysis evaluated efficacy and safety of dronedarone in atrial fibrillation (AF) and atrial flutter (AFL) patients with/without diabetes.

METHODS

Patients were categorized according to baseline diabetes status. Time-to-event analyses were performed using Kaplan-Meier method. Hazard-ratios were assessed using Cox models.

RESULTS

945/4628 (dronedarone = 482; placebo = 463) patients in ATHENA and 215/1237 (dronedarone = 148; placebo = 67) patients in EURIDIS/ADONIS studies had diabetes. In ATHENA, there were higher rates of CV hospitalization/death in patients with diabetes (39.5%) than without diabetes (34.7%). Incidence of first CV hospitalization/death was lower in patients with diabetes treated with dronedarone (35.1%) than placebo (44.1%), and time to this event was longer in those treated with dronedarone than placebo (log-rank p = 0.005). Median AF/AFL recurrence time was longer in patients treated with dronedarone than placebo in patients with diabetes (ATHENA: 722 vs 527 days, log-rank p = 0.004; EURIDIS/ADONIS: 100 vs 23 days, log-rank p = 0.15) or without diabetes (ATHENA: 741 vs 492 days, log-rank p < 0.0001; EURIDIS/ADONIS: 120 vs 59 days, log-rank p = 0.0002). Occurrence of any treatment-related adverse events with dronedarone was similar for patients with/without diabetes and was comparable to placebo.

CONCLUSIONS

Dronedarone reduced incidence of CV hospitalization/death, AF/AFL recurrence and increased time to these events in AF/AFL patients with/without diabetes.

TRIAL REGISTRATION

Not applicable, as it was a post hoc analysis. This article is based on previously conducted studies (ATHENA: NCT00174785, EURIDIS: NCT00259428, and ADONIS: NCT00259376).

Reaching Frail Elderly Patients to Optimize Diagnosis and Management of Atrial Fibrillation (REAFEL): A Feasibility Study of a Cross-Sectoral Shared-Care Model

Introduction: Atrial fibrillation (AF) management in primary care often requires a referral to cardiology clinics, which can be strenuous for frail patients. We developed “cardio-share” (CS), a new cross-sector collaboration model, to ease this process. General practitioners (GPs) can use a compact Holter monitor (C3 from Cortrium) to receive remote advice from the cardiologist. Objective: To test the feasibility and acceptability of the CS model to manage suspected AF in frail elderly patients. Methods: We used a mixed methods design, including the preparation of qualitative semistructured interviews of GPs and nurses. Results: Between MAR-2019 and FEB-2020, 54 patients were consulted through the CS model, of whom 35 underwent C3 Holter monitoring. The time from referral to a final Holter report was shortened from a mean (SD) of 117 (45) days in usual care to 30 days (13) with the CS model. Furthermore, 90% of the patients did not need to attend visits at the cardiology clinic. The GPs and nurses highlighted the ease of using the C3 monitor. Their perception was that patients were confident in the GPs’ collaboration with cardiologists. Conclusions: The CS model using a C3 monitor for AF is both feasible and seems acceptable to GPs. The elapsed time from referral to the Holter report performed for the diagnosis was significantly reduced.

Prediction of Atrial Fibrillation Being Asymptomatic at First Onset by Cardiac Pacing

Asymptomatic or silent atrial fibrillation (AF) has long been a clinical problem due to the incidence of ischemic stroke. A method is needed to predict the development of silent AF before the occurrence of ischemic stroke. This study was focused on the symptoms of AF, especially palpitation, in pacemaker patients. We assessed the hypothesis that absence of palpitation during rapid ventricular pacing could be a predictor of future onset AF being asymptomatic.In this study, we assessed the presence of symptoms during RV pacing and AF symptoms on 145 pacemaker patients at the outpatient clinic by VVI pacing at 120 ppm. The relationship between symptoms during RV pacing and symptom during AF was assessed. The predictive value of absence of symptom during RV pacing on AF being asymptomatic was assessed.Of 145 patients, 74 had previous AF episode. Among the AF patients, absence of symptom during VVI pacing was associated with AF being asymptomatic.Of 145 patients, 71 had no previous AF events. There were 14 patients who had new-onset AF or atrial flutter (AFL) after the device implantation. Four of the 14 patients (28.6%) were symptomatic during first AF/AFL episode, and 10 (71.4%) were asymptomatic during first-onset AF. All ten patients who were asymptomatic during cardiac pacing test were asymptomatic during their initial episodes of AF as well.This study showed that absence of symptoms during rapid ventricular pacing was associated with first-onset AF being asymptomatic.

Artificial intelligence-electrocardiography to detect atrial fibrillation: trend of probability before and after the first episode

Aims

Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF.

Methods and results

We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined >8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA₂DS₂-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2-5 years prior to AF, 23.6% 1-2 years prior to AF, 34.0% 0-3 months prior to AF, 40.9% 0-3 months after AF, 35.2% 1-2 years after AF, and 42.2% 2-5 years after AF (P < 0.001). The rate of increase prior to AF was higher for age >50 years CHA₂DS₂-VASc score ≥4.

Conclusion

The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1-2 years following AF and continues to increase thereafter.