Detection Of Atrial Fibrillation Episodes Research Articles

Electrocardiogram sampling frequency for the optimal performance of complexity analysis and machine learning models: Discrimination between subjects with and without paroxysmal atrial fibrillation using sinus rhythm electrocardiograms

BackgroundCurrent clinical practice to diagnose atrial fibrillation (AF) requires repeated, episodic monitoring and significantly underperform in their ability to detect AF episodes. ObjectiveThere is therefore potential for artificial intelligence based methods to assist in the detection of AF. Better understanding the optimal parameters for this detection can potentially improve the sensitivity in detecting AF. MethodTen second, 12-lead electrocardiogram signals were analysed using complexity algorithms combined with machine learning techniques to predict patients who had a previously detected AF episode but had since returned to normal sinus rhythm. An investigation was performed into the impact of sampling frequency of the electrocardiogram signal on the accuracy of the machine learning models used. ResultsUsing a single complexity algorithm showed a peak accuracy of 0.69 when using signals sampled at 125Hz. In particular it was noted that improved accuracy occurred when using lead V6 compared to other available leads. ConclusionBased on these results there is potential for 12-lead electrocardiogram signals to be recorded at 125Hz as standard and used in conjunction with complexity analysis to aid in the detection of subjects with AF.

Performance of apple watch for detecting atrial fibrillation among a known atrial fibrillation population

Abstract Background Photoplethysmography (PPG) based consumer accessible devices with algorithms for irregular rhythm detection are gaining popularity as an opportunistic screening tool for atrial arrhythmias, and are FDA cleared for use in patients without a history of atrial fibrillation (AF). However, these devices are increasingly used for the management of AF, a purpose for which some algorithms are not intended. Purpose To compare the performance of the Apple Watch Irregular Rhythm Notification (IRN) with an implantable cardiac monitor (ICM) for the detection of atrial fibrillation (AF) in patients with known AF from the DEFINE AFib study. Methods Patients with known AF, a LINQ or LINQ II ICM, and an Apple Watch with PPG-based IRN activated were included in the analysis. ICM detected AF episodes served as the standard to which Apple Watch IRNs were compared. All AF episodes detected by the ICM device were manually adjudicated by two trained, independent reviewers with a third independent reviewer in situations of disagreement. Only AF episodes ≥60 minutes during a 75-minute window in which the Apple Watch was monitoring were included in accordance with the known Apple Watch algorithm detection parameters. To eliminate oversampling of ICM detected episodes compared to Apple Watch IRNs, 1,000 simulations of one randomly selected episode per subject was done to avoid single-subject bias. Results In total, 97 subjects were analyzed of which 42 had an AF episode ≥60 minutes. Applying the 75-minute window of Apple Watch monitoring to episodes ≥60 minutes, 20 subjects had qualifying, adjudicated ICM detected AF. These 20 subjects accounted for 481 qualifying AF episodes. Of the ICM detected episodes, 191 (40%) occurred while an Apple Watch was not being worn. In total, 11/20 (55%) subjects had an IRN generated by the Apple Watch. Adjudication of the ICM episodes resulted in 290 true positive AF episodes ≥60 minutes during a 75-minute window while the watch was actively monitoring, with 74 (26%) of those episodes triggering an IRN resulting in a per episode sensitivity of 24 ± 7% and PPV of 91 ± 4% (Figure 1A). The efficacy of Apple Watch to detect AF episodes of differing lengths was assessed for episodes between 1-6 hours, 6-12 hours, 12-24 hours, and > 24 hours with sensitivities of 19 ± 19%, 38 ± 8%, 32 ± 10%, and 31 ± 8%, respectively (Figure 1B). The Apple Watch generated an annualized false positive rate of 2.25 IRNs for every patient year of monitoring. At the subject level, Apple Watch had a sensitivity of 48%, specificity of 100%, PPV of 100%, and NPV of 71% (Figure 1A). Conclusions In patients with a history of AF, the Apple Watch IRN was most sensitive at the individual subject level while watch performance was lower by episode. The current IRN algorithm favors longer episode durations. The limited sensitivity, dependence on user wear-time, and reliance on long-durations of AF limit the use of Apple Watch for AF management.

Incidence of atrial fibrillation detected by an insertable cardiac monitor in a large real-world cohort of heart failure patients with reduced and preserved ejection fraction

Abstract Background Subcutaneous implantable cardiac monitors (ICM) have the capability to detect atrial fibrillation (AF) episodes with high degree of accuracy [1]. Objective We investigated the incidence of AF in patients implanted with an ICM and a clinical history of heart failure (HF) with reduced or preserved left ventricular ejection fraction (LVEF). Methods Patients with history of HF admissions who were implanted with an ICM were identified from the aggregated and de-identified electronic health record (EHR) database during 2007-2021. The device collected data were merged with the EHR data to create a de-identified database of real-world patients. Patients were included if they had ≥180 days of device follow-up. ICMs detect AF based on incoherence of RR intervals and absence of single p-wave between two R-waves [1]. All ICM detected AF episodes stored with at least 30 seconds of ECG at onset were adjudicated using an artificial intelligence (AI) model. The AI model was previously trained and validated using over 60K manually adjudicated episodes and was shown to have an accuracy of over 97% [2]. ICM detected AF episodes with AI model based true probability of AF ≥ 0.9 was analyzed. The Kaplan-Meier incidence curves for AF incidence as a function of episode duration, clinical history of AF, and LVEF are reported. Results A total of 1020 LINQ ICM patients with history of HF admission prior to implant were identified from the deidentified real-world dataset. LVEF was available in 889 (87%) patients, of whom 394 (44%) had EF <50%, and 495 (56%) had EF≥50%. NYHA class was available in 296 (29%) patients, of whom 39 (13%) were class I, 126 (43%) were class II, 112 (38%) were class III, and 19 (6%) were class IV. Patients had an average age of 68±13 years with 52% being males and had clinical history of hypertension in 95%, diabetes in 56%, coronary artery disease in 75%, AF in 57%, stroke/TIA in 51%, and renal dysfunction in 53%. A total of 911 patients with ≥180 days of follow-up (average follow-up of 25.8 months) were included. A total of 8407 episodes from 358 patients were classified by the AI model as true AF. The incidence of AF as a function of episode duration, clinical history of AF and reduced vs preserved LVEF are shown in Figures A, B, and C respectively. Incidence of AF, as detected by an ICM over 42 months of follow-up, was estimated to be 38% to 46% depending on AF episode duration. Estimated 42-month AF incidence was 23% in patients with no clinical history of AF. The 42-month AF incidence was 44% vs 47% in HF patients with reduced versus preserved LVEF. Conclusion Incidence of AF, as detected by an ICM over 3 years of follow-up, was estimated to be in close to half of ICM patients with history of HF events. Close to one fourth of the patients with no prior clinical history of AF had newly detected AF. AF incidence was similar in HF patients with preserved versus reduced LVEF in this real-world cohort.

The incidence of atrial fibrillation detected by implantable loop recorders: a comparison between patients with and without embolic stroke of undetermined source.

Stroke is the most debilitating outcome of atrial fibrillation (AF). The use of implantable loop recorders increases the detection of AF episodes among patients with embolic stroke of undetermined source. The significance of device-detected AF, or subclinical AF, is unknown. This study aimed to compare the incidence of AF detected by implantable loop recorder in patients with and without embolic stroke of undetermined source. We retrospectively studied all patients without known AF who were referred to our institution for implantable loop recorder implantation following embolic stroke of undetermined source, syncope, or palpitations from March 2009 to November 2019. The primary endpoint was any detection of AF or atrial flutter by implantable loop recorder. Seven hundred and fifty patients were included and followed up for a mean duration of 731 days (SD 443). An implantable loop recorder was implanted following embolic stroke of undetermined source in 323 and for assessment of syncope, palpitations, or another reason in 427 patients. The incidence of AF was significantly (P < 0.001) higher among patients with embolic stroke of undetermined source compared with the non-embolic stroke of undetermined source group; 48.6% vs. 13.8% (for any duration of AF) and 32.2% vs. 12.4% (for AF lasting ≥30 s) both P < 0.001. Kaplan-Meier analysis showed significantly higher incidence of AF for incremental durations of AF up to >5.5 h, but not >24 h. This was driven by longest AF durations of <6 min and between 5.5 h and 24 h, suggesting a bimodal distribution. In a multivariable Cox regression analysis, embolic stroke of undetermined source independently conferred an almost 5-fold increase in the hazard for any duration of AF. The incidence of AF is significantly higher amongst embolic stroke of undetermined source vs. non-embolic stroke of undetermined source patients monitored constantly by an implantable loop recorder. A high number of embolic stroke of undetermined source survivors have short-duration AF episodes. Further work is needed to determine the optimal treatment strategy of these AF episodes in embolic stroke of undetermined source.

Left atrial strain after paroxysmal atrial fibrillation linac-based cardiac radioablation in elderly population.

Catheter ablation of atrial fibrillation (AF) may increase the risk of complication in aged patients. Stereotactic arrhythmia radioablation (STAR) is a non-invasive therapeutic alternative for cardiac arrhythmia. This sub-study evaluated left atrial strain (LAS) in elderly AF patients underwent STAR. Symptomatic paroxysmal AF patients aged > 70 years, with antiarrhythmic drugs failure or intolerance, enrolled in a phase II trial that have demonstrated the feasibility of LINAC-based STAR (total dose of 25Gy single fraction delivered in 3min), performed a 15-day electrocardiogram Holter monitoring to detect AF episodes (≥ 30s) and an echocardiographic LAS evaluation before and after STAR (at 1-, 3-, 6- and 12-month). Out 18 patients underwent STAR in the trial, 16 (7 males, 78 ± 5 years) completed the follow-up for LAS study. No baseline difference in echocardiographic and LAS parameters was observed between the 9 patients with AF recurrence during follow-up and those who maintained sinus rhythm. At 6- and 12-month after STAR, LAS reservoir was lower in patients with AF episodes than those without (respectively, p = 0.039 and p = 0.001). Values of left atrial area and volume as well as LAS conduit and contractile phase were not statistically significant different by patient's outcome across evaluations. Although no baseline LAS parameter before STAR seems predict AF recurrence after treatment in elderly patients, lower values of reservoir phase were observed during follow-up in those experiencing AF episodes. More research is needed to better assess the value of LAS monitoring in paroxysmal AF patients underwent Stereotactic cardiac radioablation.

Continuing evaluation of atrial fibrillation detection after cryptogenic stroke: one-year findings from a multi-center study with confirm Rx ICM

Abstract Background The detection of atrial fibrillation (AF) after a cryptogenic stroke (CS) carries crucial therapeutic implications. While clinical trials have meticulously explored the incidence of AF in CS patients, real-world data on this topic remain relatively sparse. In this study, we aimed to assess the incidence of AF among patients with cryptogenic stroke, who underwent the implantation of an insertable cardiac monitor (ICM). The abstract reports the interim results from the CS sub-study of the Smart Registry. Methods A prospective, single-arm, multi-center registry was conducted to identify AF in patients with cryptogenic stroke using the Confirm R ICM. Inclusion criteria comprised participants aged 40 years or older who had experienced cryptogenic strokes within a 90-day window. Prior to enrollment, comprehensive diagnostic assessments were performed to meticulously rule out all potential sources of embolism, in accordance with the minimal standards of the American and European Guidelines. At each follow-up visit, expert electrophysiologists reviewed and adjudicated ICM detected AF episodes. The primary endpoint was the cumulative incidence of true device-detected AF (lasting more than 30 seconds) at 6 months. Kaplan-Meier curves were employed to assess the cumulative incidence of true device-detected AF through 12 months. Results In our study, 117 patients were enrolled across 19 global sites from September 2021 to August 31, 2023. Kaplan-Meier analysis revealed a 21.3% probability (confidence interval (CI): 14.5%, 30.7%) of true AF episodes lasting more than 30 seconds at 6 months, increasing to 31.0% (CI: 22.0%, 42.5%) at 12 months. Similar results were observed in episodes lasting over 120 seconds at both 6 months (20.7%) and 12 months (30.5%). In average, 14.4 true AF episodes per patient were detected per year. The median time from implantation to AF detection (&amp;gt; 30 seconds) was 31.5 days (interquartile range, 7 to 191). Conclusion Our ongoing study illustrates the effectiveness of the Confirm Rx ICM in detecting atrial fibrillation after cryptogenic stroke. These results confirm the diagnostics value of long-term continuous AF monitoring in cryptogenic stroke patients.

Abstract 18368: Management of Incident AF in Hospitalized Patients - Deficient, Delayed, Detrimental

Introduction: Atrial fibrillation (AF) is potentially associated with significant complications including stroke and cardiac decompensation. Early appropriate AF management mitigates this risk. However, intervention is frequently delayed - a median of 1.5 days from AF onset in the ICU to clinical response. We sought to better understand delays in detection and management of AF in patients admitted to a cardiology bed and the consequences of delays. Methods: We undertook a retrospective review of 498 patients admitted to cardiology beds at the University of Virginia Medical Center who had automated algorithmic detection of AF episodes lasting ≥6 min. Time from algorithmic detection to clinical identification, AF burden, AF interventions and complications, medical history, and demographics were identified in each patient’s chart. Results: Of the 498 patients, 171 had an algorithmically detected episode of AF that was not acknowledged medically. These undetected episodes were generally shorter relative to episodes that were acknowledged (median 8 min vs. 52 min). 96 of the 498 patients had initial clinical detection of AF following algorithm detection; of the 96, 21 received early intervention (within 60 min), 51 received late intervention (after 60 min), and 24 received no intervention at all. Patients with early intervention experienced less AF burden compared to late and no intervention groups (median 8.6 hrs vs 21.75 hrs vs 29.35 hrs). Late intervention patients had more AF related complications compared to early intervention patients (9.8% vs. 4.8%). 20.8% of patients with clinical acknowledgement of AF but no intervention suffered a complication. Patients with a history of AF were more likely to have delays in intervention and over twice as likely to suffer an AF complication compared to those with no history (15.6% vs. 7.4%). Patients of lower socioeconomic status (SES) were also more likely to experience a complication. Conclusions: In summary, cardiology inpatients at our hospital commonly experience delays in management of AF that are considerable in duration, resulting in greater AF burden and higher complication rates. Differences in management, and subsequent outcomes, following AF appear to be impacted by prior history of AF and patient SES.

Electrocardiographic Morphology-Voltage-P-Wave-Duration (MVP) Score to Select Patients for Continuous Atrial Fibrillation Screening to Prevent Stroke

Morphology-voltage-P-wave-duration (MVP) score combining P-wave duration (PWD), P-wave voltage in lead I (PWVI), and interatrial block (IAB) has been demonstrated to predict atrial fibrillation (AF). Therefore, this study aimed to examine MVP score and its P-wave components as potential predictors of AF screening effects on stroke prevention. This was a secondary analysis of the LOOP Study (Atrial Fibrillation detected by Continuous ECG Monitoring using Implantable Loop Recorder to prevent Stroke in High-risk Individuals) which randomized older persons (aged 70 to 90years) with additional stroke risk factors to either continuous monitoring with implantable loop recorder and anticoagulation upon detection of AF episodes ≥6minutes (the intervention group), or usual care. A total of 5,759 participants were included in the present analysis, where PWD, PWVI, and IAB were determined through a computerized analysis of 12-lead electrocardiogram and further employed to calculate baseline MVP score (0 to 6) for each participant. In total, 305 (5.3%) had stroke or systemic embolism during follow-up, with a higher risk in the group with MVP score 5 to 6 than those having score 0 to 2 (hazard ratio (HR) 1.54 [95% confidence interval (CI) 1.01 to 2.35]). This risk increase was mainly upheld by participants with IAB (HR 1.62 [95% CI 1.11 to 2.36] for IAB vs no IAB) and with longer PWD (HR 1.37 [95% CI 1.07 to 1.75] for >110 vs ≤110ms). Compared with usual care, implantable loop recorder screening did not significantly reduce the risk of stroke or systemic embolism in any MVP risk categories (HR 0.80 [95% CI 0.60 to 1.08] for MVP score 0 to 2, 0.54 [95% CI 0.16 to 1.85] for MVP score 3 to 4, and 0.89 [95% CI 0.35 to 2.25] for MVP score 5 to 6; pinteraction=0.78). In conclusion, a higher MVP score was associated with an increased stroke risk, but it did not demonstrate an association with effects of AF screening on stroke prevention. These findings should be considered hypothesis-generating and warrant further study.

Cardiac Magnetic Resonance Imaging-Derived Left Atrial Characteristics in Relation to Atrial Fibrillation Detection in Patients With an Implantable Cardioverter-Defibrillator.

Background Among patients with an implantable cardioverter-defibrillator, a high prevalence of atrial fibrillation (AF) is present. Identification of AF predictors in this patient group is of clinical importance to initiate appropriate preventive therapeutic measures to reduce the risk of AF-related complications. This study assesses whether cardiac magnetic resonance imaging-derived atrial characteristics are associated with AF development in patients with a dual-chamber implantable cardioverter-defibrillator or cardiac resynchronization therapy defibrillator, as detected by the cardiac implantable electronic device. Methods and Results This single-center retrospective study included 233 patients without documented AF history at the moment of device implantation (dual-chamber implantable cardioverter-defibrillator [63.5%] or cardiac resynchronization therapy defibrillator [36.5%]). All patients underwent cardiac magnetic resonance imaging before device implantation. Cardiac magnetic resonance-derived features of left atrial (LA) remodeling were evaluated in all patients. Detection of AF episodes was based on cardiac implantable electronic device interrogation. During a median follow-up of 6.1 years, a newly diagnosed AF episode was detected in 88 of the 233 (37.8%) patients with an ICD. In these patients, increased LA volumes and impaired LA function (LA emptying fraction and LA strain) were found as compared with patients without AF during follow-up. However, a significant association was only found in patients with dilated cardiomyopathy and not in patients with ischemic cardiomyopathy. Conclusions LA remodeling characteristics were associated with development of AF in patients with dilated cardiomyopathy but not patients with ischemic cardiomyopathy, suggesting different mechanisms of AF development in ischemic cardiomyopathy and dilated cardiomyopathy. Assessment of LA remodeling before device implantation might identify high-risk patients for AF.

Predictive value of left and right atrial strain for the detection of atrial fibrillation in patients with cryptogenic stroke and implant of loop recorder

Abstract Funding Acknowledgements Type of funding sources: None. Background Structural and functional remodeling of left and right atrium (LA-RA) are predictors of the risk of hospitalization and mortality. These are chronic and complex processes, dependent on an adaptive response implemented by the atrial myocytes against stressors of an electrical, mechanical and metabolic nature. Atrial fibrillation (AF) is the most common condition associated with remodeling of both atria, of which it can be cause and effect at the same time: atrial enlargement, interstitial fibrosis and myocardial distension. Often the first manifestation of AF is cryptogenic stroke (CS) which is associated with a high rate of recurrence and adverse long-term follow-up outcomes, mainly due to its unknown etiology often leading to secondary prevention ineffective. Asymptomatic (AF) may play an important pathophysiological role to detect CS. Evidence has demonstrated the efficacy of AF ablation in restoring the structure of the LA and, consequently, in favoring the maintenance of sinus rhythm. However, little is known regarding the evaluation of RA remodeling and its variation over time in patients with FA. Aim of the study Evaluate the relationship between echocardiographic parameters of LA and RA function, and the occurrence of AF revealed by continuous ECG monitoring through insertable cardiac monitors (ICM) implant in a cohort of patients with CS Methods Single-center retrospective study. A total of 204 patients who suffered from cryptogenic stroke underwent ICM between May 2013 and July 2022. All detected AF episodes lasting more than 30 seconds were considered, according to Guidelines. All patients underwent to Transthoracic Echocardiography (TTE) to study on LA and RA function, including both standard and longitudinal strain-derived parameters. Transesophageal Echocardiography (TEE) was also performed to exclude embolic sources. Results During a median follow-up period of 15.3 months (interquartile range, 7.4 / 23.5), continuous ECG monitoring revealed AF in 96 patients (47.0%). Many echocardiographic parameters, such as LA maximum and minimal volume were significantly associated with the occurrence of AF, suggesting the worst atrial function in the AF group. Furthermore, multivariable regression analysis revealed that peak atrial contraction strain of both atria were independently associated with AF (p &amp;lt; 0.001,respectively). Conclusion In patients with cryptogenic CS, LA and RA strain analysis are strong and independent predictors of the occurrence of AF despite clinical and morpho-functional echocardiographic parameters.

Effects of Atrial Fibrillation Screening According to N-Terminal Pro-B-Type Natriuretic Peptide: A Secondary Analysis of the Randomized LOOP Study.

Research suggests NT-proBNP (N-terminal pro-B-type natriuretic peptide) to be a strong predictor of incident atrial fibrillation (AF) and stroke. However, its utility in AF screening remains unknown. The aim of this study was to investigate NT-proBNP as a potential marker for screening efficacy with respect to AF yield and stroke prevention. In the LOOP Study (Atrial Fibrillation Detected by Continuous ECG Monitoring Using Implantable Loop Recorder to Prevent Stroke in High-Risk Individuals), 6004 AF-naïve individuals at least 70 years old and with additional stroke risk factors were randomized 1:3 to either screening with an implantable loop recorder (ILR) and initiation of anticoagulation upon detection of AF episodes lasting ≥6 minutes or usual care (control). This post hoc analysis included study participants with available NT-proBNP measurement at baseline. A total of 5819 participants (96.9% of the trial population) were included. The mean age was 74.7 years (SD, 4.1 years) and 47.5% were female. The median NT-proBNP level was 15 pmol/L (interquartile range, 9-28 pmol/L) corresponding to 125 pg/mL (interquartile range, 76-233 pg/mL). NT-proBNP above median was associated with an increased risk of AF diagnosis both in the ILR group (hazard ratio, 1.84 [95% CI, 1.51-2.25]) and the control group (hazard ratio, 2.79 [95% CI, 2.30-3.40]). Participants with NT-proBNP above the median were also at higher risk of clinical events compared with those having lower levels (hazard ratio, 1.21 [95% CI, 0.96-1.54] for stroke or systemic embolism [SE], 1.60 [95% CI, 1.32-1.95] for stroke/SE/cardiovascular death, and 1.91 [95% CI, 1.61-2.26] for all-cause death). Compared with usual care, ILR screening was associated with significant reductions in stroke/SE and stroke/SE/cardiovascular death among participants with NT-proBNP above median (hazard ratio, 0.60 [95% CI, 0.40-0.90] and 0.70 [95% CI, 0.53-0.94], respectively) but not among those with lower levels (Pinteraction=0.029 for stroke/SE and 0.045 for stroke/SE/cardiovascular death). No risk reduction in all-cause death was observed in either NT-proBNP subgroup for ILR versus control (Pinteraction=0.68). Analyzing NT-proBNP as a continuous variable yielded similar findings. In an older population with additional stroke risk factors, ILR screening for AF was associated with a significant reduction in stroke risk among individuals with higher NT-proBNP levels but not among those with lower levels. These findings should be considered hypothesis generating and warrant further study before clinical implementation. URL: https://www. gov; Unique identifier: NCT02036450.

Automatic screening of patients with atrial fibrillation from 24-h Holter recording using deep learning.

As the demand for atrial fibrillation (AF) screening increases, clinicians spend a significant amount of time identifying AF signals from massive amounts of data obtained during long-term dynamic electrocardiogram (ECG) monitoring. The identification of AF signals is subjective and depends on the experience of clinicians. However, experienced cardiologists are scarce. This study aimed to apply a deep learning-based algorithm to fully automate primary screening of patients with AF using 24-h Holter monitoring. A deep learning model was developed to automatically detect AF episodes using RR intervals and was trained and evaluated on 23 621 (2297 AF and 21 324 non-AF) 24-h Holter recordings from 23 452 patients. Based on the AF episode detection results, patients with AF were automatically identified using the criterion of at least one AF episode lasting 6 min or longer. Performance was assessed on an independent real-world hospital-scenario test set (19 227 recordings) and a community-scenario test set (1299 recordings). For the two test sets, the model obtained high performance for the identification of patients with AF (sensitivity: 0.995 and 1.000; specificity: 0.985 and 0.997, respectively). Moreover, it obtained good and consistent performance (sensitivity: 1.000; specificity: 0.972) for an external public data set. Using the criterion of at least one AF episode of 6 min or longer, the deep learning model can fully automatically screen patients for AF with high accuracy from long-term Holter monitoring data. This method may serve as a powerful and cost-effective tool for primary screening for AF.

Prevalence of atrial fibrillation in the 65 or over Polish population. Report of cross-sectional NOMED-AF study.

Atrial fibrillation (AF) is the most common cardiac arrhythmia, characterized by an increased risk of thromboembolic complications that can be markedly reduced with anticoagulation. There is a paucity of studies assessing the total prevalence of AF in national populations. To assess the nationwide prevalence of AF in a population of adults ≥65 old and to determine the impact of duration of electrocardiogram (ECG) monitoring on the number of newly detected AF episodes. The NOMED-AF study (ClinicalTrials.gov; NCT: 0324347) was a cross-sectional study performed on a nationally representative random sample of 3014 Polish citizens 65 years or older. Final estimates were adjusted to the national population. All participants underwent up to 30 days of continuous ECG monitoring. Total AF prevalence was diagnosed based on the patient's medical records or the presence of AF in ECG monitoring. The prevalence of AF in the Polish population ≥65 years was estimated as 19.2% (95% confidence interval [CI], 17.9%-20.6%). This included 4.1% (95% CI, 3.5%-4.8%) newly diagnosed cases and 15.1% (95% CI, 13.9%-16.3%) previously diagnosed cases and consisted of 10.8% (95% CI, 9.8%-11.9%) paroxysmal AF and 8.4% (95% CI, 7.5%-9.4%) persistent/permanent AF. The incidence of all paroxysmal AF events as a function of ECG monitoring duration increased from 1.9% (95% CI, 1.4%-2.6%) at 24 hours to 6.2% (95% CI, 5.3%-7.2%) at 4 weeks. The prevalence of AF in elderly adults is higher than estimated based on medical records only. Four weeks of monitoring compared to 24-hour ECG Holter allow detection of 7-fold more cases of previously undiagnosed paroxysmal AF.

MHealth based patient-centered self-management reduces the burden and episodes of atrial fibrillation

Abstract Background There is a surging development of digital technologies for screening and diagnosis of atrial fibrillation (AF), nonetheless, how these digital technologies impact AF care remains unclear. Objectives The present study aimed to investigate the impact on AF episodes and arrhythmia burden of patient-centered self-management with using smart technologies, incorporating artificial intelligence (AI), wearable and mobile health (mHealth) technology for patient-centred self-management. Methods In this cohort study, we applied AI machine-learning (ML) model predicting AF prior to 4 hours of AF occurrence, while the timely monitoring of AF episodes with photoplethysmography (PPG) technology, and further confirmation of the diagnosis of AF with single-lead ECG, was based on wearable devices. These have been developed in AF screening study (stage 1 pre-mAFA) of Mobile Health technology for improved screening and optimizing integrated care in atrial fibrillation (mAFA II programme), while mHealth supported integrated care of AF was validated to reduce clinical events (stage 2 mAFA II cluster randomized trial). In this observational cohort, the subjects were in 2 groups: (i) subjects with monitored AF but without using mAFA (Group 1); (ii) subjects with monitored AF and using smart devices and mAFA for patient-centered self-management (Group 2). Adult subjects freely downloaded the mAFA AF App, with compatible devices, and were included into the study from across China between October 26, 2018 and Dec 1, 2021. Results From 3499461 subjects involving in AF population screening, there were 5904 subjects in Group 1 (mean age 57 years, SD,15 years; 80.7%, male), while 2667 subjects in Group 2 (51, 15; 89.7% male). The diagnosis of AF episodes was confirmed by single-lead ECG recordings, and decreased among those using mAFA in Group 2 (Confirmed AF: 602 in 1st quarter, 224 in 2nd quarter, 81 in 3rd quarter, 52 in 4th quarter) (all p value for trend&amp;lt;0.001). AF burden was significantly reduced over time (PPG: 26% on 1st month, 19% on 12th month; ECG: 3%, 0.7%, all p&amp;lt;0.001), while the average probability of AF occurrences by AI ML model prediction was decreased among those using mAFA in Group 2 (65%, 57%, p&amp;lt;0.001). The decreasing trend of AF burden over time was not seen in subjects with detected AF without mAFA (Group 1). On multivariate analysis, AI ML model driven mAFA-based upstream risk factor control significantly reduced the changes in AF burden of both Δ 6 month (adjusted OR, 95% CI, 0.51,0.43–0.59) and Δ 12 month (0.39,0.32–0.47, all p&amp;lt;0.001). Conclusion mHealth-AF care increased the adherence of using smart devices for AF detection. The incorporation of smart devices and AI tools into an AF clinical care pathway, effectively reduced AF burden and detected AF episodes, through appropriate rhythm control management and upstream risk factor control. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Natural Science Foundation of China

Detection of Atrial Fibrillation Episodes based on 3D Algebraic Relationships between Cardiac Intervals.

In this study, the notion of perfect matrices of Lagrange differences is employed to detect atrial fibrillation episodes based on three ECG parameters (JT interval, QRS interval, RR interval). The case study comprised 8 healthy individuals and 7 unhealthy individuals, and the mean and standard deviation of age was 65.84 ± 1.4 years, height was 1.75 ± 0.12 m, and weight was 79.4 ± 0.9 kg. Initially, it was demonstrated that the sensitivity of algebraic relationships between cardiac intervals increases when the dimension of the perfect matrices of Lagrange differences is extended from two to three. The baseline dataset was established using statistical algorithms for classification by means of the developed decision support system. The classification helps to determine whether the new incoming candidate has indications of atrial fibrillation or not. The application of probability distribution graphs and semi-gauge indicator techniques aided in visualizing the categorization of the new candidates. Though the study's data are limited, this work provides a strong foundation for (1) validating the sensitivity of the perfect matrices of Lagrange differences, (2) establishing a robust baseline dataset for supervised classification, and (3) classifying new incoming candidates within the classification framework. From a clinical standpoint, the developed approach assists in the early detection of atrial fibrillation in an individual.

A two-staged classifier to reduce false positives: On device detection of atrial fibrillation using phase-based distribution of poincaré plots and deep learning

BackgroundMobile Cardiac Outpatient Telemetry (MCOT) can be used to screen high risk patients for atrial fibrillation (AF). These devices rely primarily on algorithmic detection of AF events, which are then stored and transmitted to a clinician for review. It is critical the positive predictive value (PPV) of MCOT detected AF is high, and this often leads to reduced sensitivity, as device manufacturers try to limit false positives. ObjectiveThe purpose of this study was to design a two stage classifier using artificial intelligence (AI) to improve the PPV of MCOT detected atrial fibrillation episodes whilst maintaining high levels of detection sensitivity. MethodsA low complexity, RR-interval based, AF classifier was paired with a deep convolutional neural network (DCNN) to create a two-stage classifier. The DCNN was limited in size to allow it to be embedded on MCOT devices. The DCNN was trained on 491,727 ECGs from a proprietary database and contained 128,612 parameters requiring only 158 KB of storage. The performance of the two-stage classifier was then assessed using publicly available datasets. ResultsThe sensitivity of AF detected by the low complexity classifier was high across all datasets (>93%) however the PPV was poor (<76%). Subsequent analysis by the DCNN increased episode PPV across all datasets substantially (>11%), with only a minor loss in sensitivity (<5%). This increase in PPV was due to a decrease in the number of false positive detections. Further analysis showed that DCNN processing was only required on around half of analysis windows, offering a significant computational saving against using the DCNN as a one-stage classifier. ConclusionDCNNs can be combined with existing MCOT classifiers to increase the PPV of detected AF episodes. This reduces the review burden for physicians and can be achieved with only a modest decrease in sensitivity.

Consumer-Led Screening for AtrialFibrillation: A Report From the mAFA-II Trial Long-Term Extension Cohort.

There are limited data on mobile health detection of prevalent atrial fibrillation (AF) and its related risk factors over time. This study aimed to report the trends on prevalent AF detection over time and risk factors, with a consumer-led photoplethysmography screening approach. 3,499,461 subjects aged over 18 years, who use smart devices (Huawei Technologies Co.) were enrolled between October 26, 2018, and December 1,2021. Among 2,852,217 subjects for AF screening, 12,244 subjects (0.43%; 83.2% male, mean age 57 ± 15 years) detected AF episodes. When compared with 2018, the risk (adjusted HRs, 95%CI) for monitored prevalent AF increased significantly for subjects when monitoring started in 2020 (adjusted HR: 1.34; 95%CI: 1.27-1.40; P< .001) or in 2021 (adjusted HR: 1.67; 95%CI: 1.59-1.76; P< 0.001). Of the 961,931 subjects who screening for both AF and OSA, 18,032 (1.9%, 97.8% male, mean age 44 ±17 years) were identified as high risk for OSA, which resulted in a 1.5-fold increase (95%CI: 1.30-fold to 1.75-fold) in the prevalent AF. A total of 5,227 (53.3%, 5,227/9,797) subjects were effectively followed up, from which 4,903 (93.8%, 4,903/5,227) subjects were confirmed with the diagnosis of AF, by the mAFA Telecare Team health providers. Photoplethysmography-based smart devices can facilitate screening for AF with >93% confirmation of detected AF episodes even for the low-risk general population, highlighting the increased risk for detecting prevalent AF and the need for modification of OSA that increase AF susceptibility. (Mobile Health [mHealth] Technology for Improved Screening, Patient Involvement and Optimizing Integrated Care in Atrial Fibrillation [mAFA (mAF-App) II study]; ChiCTR-OOC-17014138).

Screening for atrial fibrillation to prevent stroke in elderly individuals with or without preexisting cardiovascular disease

Abstract Background Previous research has showed that various cardiovascular diseases (CVD) are associated with increased risks of atrial fibrillation (AF) and stroke. However, data on the interaction between CVD and AF screening efficacy are lacking. Purpose To evaluate the influence of preexisting CVD on the effects of AF screening with long-term continuous monitoring. Methods The LOOP Study (Atrial Fibrillation detected by Continuous ECG Monitoring using Implantable Loop Recorder to prevent Stroke in High-risk Individuals) randomized AF-naïve individuals aged ≥70 years and with additional stroke risk factors to either long-term screening with implantable loop recorder (ILR) and subsequent anticoagulation initiation upon detection of AF episodes ≥6 minutes, or usual care (the control group). In the current study, all participants from the LOOP Study were divided into two risk groups according to the presence of CVD (defined as ischemic heart disease, heart failure, previous stroke, valvular heart disease, or peripheral artery disease). The relative risks of outcomes in groupwise comparisons, as indicated by hazard ratio (HR), were assessed in the cause-specific Cox proportional-hazards model with death as competing risk. Results Of 6004 participants included, 1997 (33.3%) had ≥1 CVD at baseline. Compared with no CVD, the presence of CVD was associated with increased risks of AF diagnosis in the ILR group (adjusted HR 1.32 [1.09–1.59]) and of stroke or systemic arterial embolism in the entire study cohort (adjusted HR 1.34 [1.06–1.69]). For ILR screening versus usual care, there was no decrease in stroke or systemic arterial embolism among participants with preexisting CVD (adjusted HR 1.13 [0.76–1.68]), whereas a significant risk reduction was obtained by screening among those without CVD (adjusted HR 0.64 [0.44–0.93]). The interaction was significant (adjusted p-value for interaction 0.041). Conclusions In an elderly, high-risk population, ILR screening did not prevent stroke significantly in individuals with preexisting CVD, but it was associated with an approximately 40% risk reduction among those without CVD. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): The LOOP Study was funded by Innovation Fund Denmark [grant number 12-1352259], The Research Foundation for the Capital Region of Denmark, The Danish Heart Foundation [grant number 11-04-R83-A3363-22625], Aalborg University Talent Management Program, Arvid Nilssons Fond, Skibsreder Per Henriksen, R og Hustrus Fond, the European Union's Horizon 2020 program [grant number 847770], Læge Sophus Carl Emil Friis og hustru Olga Doris Friis' Legat, and an unrestricted grant from Medtronic.

MHealth based patient-centered self-management reduces the burden and episodes of atrial fibrillation

Abstract Background There is a surging development of digital technologies for screening and diagnosis of atrial fibrillation (AF), nonetheless, how these digital technologies impact AF care remains unclear. Objectives The present study aimed to investigate the impact on AF episodes and arrhythmia burden of patient-centered self-management with using smart technologies, incorporating artificial intelligence (AI), wearable and mobile health (mHealth) technology for patient-centred self-management. Methods In this cohort study, we applied AI machine-learning (ML) model predicting AF prior to 4 hours of AF occurrence, while the timely monitoring of AF episodes with photoplethysmography (PPG) technology, and further confirmation of the diagnosis of AF with single-lead ECG, was based on wearable devices. These have been developed in AF screening study (stage 1 pre-mAFA) of Mobile Health technology for improved screening and optimizing integrated care in atrial fibrillation (mAFA II programme), while mHealth supported integrated care of AF was validated to reduce clinical events (stage 2 mAFA II cluster randomized trial). In this observational cohort, the subjects were in 2 groups: (i) subjects with monitored AF but without using mAFA (Group 1); (ii) subjects with monitored AF and using smart devices and mAFA for patient-centered self-management (Group 2). Adult subjects freely downloaded the mAFA AF App, with compatible devices, and were included into the study from across China between October 26, 2018 and Dec 1, 2021. Results From 3499461 subjects involving in AF population screening, there were 5904 subjects in Group 1 (mean age 57 years, SD,15 years; 80.7%, male), while 2667 subjects in Group 2 (51, 15; 89.7% male). The diagnosis of AF episodes was confirmed by single-lead ECG recordings, and decreased among those using mAFA in Group 2 (Confirmed AF: 602 in 1st quarter, 224 in 2nd quarter, 81 in 3rd quarter, 52 in 4th quarter) (all p value for trend&amp;lt;0.001). AF burden was significantly reduced over time (PPG: 26% on 1st month, 19% on 12th month; ECG: 3%, 0.7%, all p&amp;lt;0.001), while the average probability of AF occurrences by AI ML model prediction was decreased among those using mAFA in Group 2 (65%, 57%, p&amp;lt;0.001). The decreasing trend of AF burden over time was not seen in subjects with detected AF without mAFA (Group 1). On multivariate analysis, AI ML model driven mAFA-based upstream risk factor control significantly reduced the changes in AF burden of both Δ 6 month (adjusted OR, 95% CI, 0.51,0.43–0.59) and Δ 12 month (0.39,0.32–0.47, all p&amp;lt;0.001). Conclusion mHealth-AF care increased the adherence of using smart devices for AF detection. The incorporation of smart devices and AI tools into an AF clinical care pathway, effectively reduced AF burden and detected AF episodes, through appropriate rhythm control management and upstream risk factor control. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Natural Science Foundation of China

Systolic Blood Pressure and Effects of Screening for Atrial Fibrillation With Long-Term Continuous Monitoring (a LOOP Substudy).

Background:Hypertension is a well-known risk factor for atrial fibrillation (AF) and stoke, but data on the interaction between systolic blood pressure (SBP) and effects of AF screening are lacking.Methods:The LOOP Study randomized AF-naïve individuals aged 70 to 90 years with additional stroke risk factors to either screening with implantable loop recorder (ILR) and anticoagulation initiation upon detection of AF episodes ≥6 minutes, or usual care. In total, 5997 participants with available baseline SBP measurements were included in this substudy. Outcomes were analyzed according to the time-to-first-event principle using cause-specific Cox models.Results:The hazard ratio of stroke or systemic arterial embolism for ILR versus control decreased with increasing SBP. ILR screening yielded a 44% risk reduction of stroke or systemic arterial embolism among participants with SBP ≥150 mm Hg (adjusted hazard ratio, 0.56 [0.37–0.83]). Within the ILR group, SBP≥150 mm Hg was associated with a higher incidence of AF episodes ≥24 hours than lower SBP (adjusted hazard ratio, 1.70 [1.08–2.69]) but not with the overall occurrence of AF (adjusted P>0.05).Conclusions:The impact of AF screening on thromboembolic events increased with increasing blood pressure. SBP≥150 mm Hg was associated with a >1.5-fold increased risk of AF episodes ≥24 hours, along with an almost 50% risk reduction of stroke or systemic arterial embolism by ILR screening compared to lower blood pressure. These findings should be considered hypothesis-generating and warrant further study.Registration:URL: https://www.clinicaltrials.gov; Unique Identifier: NCT02036450.