Atrial Fibrillation Population Screening

A High-Protein Diet Promotes Atrial Arrhythmogenesis via Absent-in-Melanoma 2 Inflammasome

High-protein diets (HPDs) offer health benefits, such as weight management and improved metabolic profiles. The effects of HPD on cardiac arrhythmogenesis remain unclear. Atrial fibrillation (AF), the most common arrhythmia, is associated with inflammasome activation. The role of the Absent-in-Melanoma 2 (AIM2) inflammasome in AF pathogenesis remains unexplored. In this study, we discovered that HPD increased susceptibility to AF. To demonstrate the involvement of AIM2 signaling in the pathogenesis of HPD-induced AF, wildtype (WT) and Aim2-/- mice were fed normal-chow (NC) and HPD, respectively. Four weeks later, inflammasome activity was upregulated in the atria of WT-HPD mice, but not in the Aim2-/--HPD mice. The increased AF vulnerability in WT-HPD mice was associated with abnormal sarcoplasmic reticulum (SR) Ca2+-release events in atrial myocytes. HPD increased the cytoplasmic double-strand (ds) DNA level, causing AIM2 activation. Genetic inhibition of AIM2 in Aim2-/- mice reduced susceptibility to AF, cytoplasmic dsDNA level, mitochondrial ROS production, and abnormal SR Ca2+-release in atrial myocytes. These data suggest that HPD creates a substrate conducive to AF development by activating the AIM2-inflammasome, which is associated with mitochondrial oxidative stress along with proarrhythmic SR Ca2+-release. Our data imply that targeting the AIM2 inflammasome might constitute a novel anti-AF strategy in certain patient subpopulations.

Smartphone-based atrial fibrillation screening in the general population: feasibility and impact on medical treatment

Aims

The aim of this study is to determine the feasibility, detection rate, and therapeutic implications of large-scale smartphone-based screening for atrial fibrillation (AF).

Methods and results

Subjects from the general population in Belgium were recruited through a media campaign to perform AF screening during 8 consecutive days with a smartphone application. The application analyses photoplethysmography traces with artificial intelligence and offline validation of suspected signals to detect AF. The impact of AF screening on medical therapy was measured through questionnaires. Atrial fibrillation was detected in the screened population (n = 60.629) in 791 subjects (1.3%). From this group, 55% responded to the questionnaire. Clinical AF [AF confirmed on a surface electrocardiogram (ECG)] was newly diagnosed in 60 individuals and triggered the initiation of anti-thrombotic therapy in 45%, adjustment of rate or rhythm controlling strategies in 62%, and risk factor management in 17%. In subjects diagnosed with known AF before screening, a positive screening result led to these therapy adjustments in 9%, 39%, and 11%, respectively. In all subjects with clinical AF and an indication for oral anti-coagulation (OAC), OAC uptake increased from 56% to 74% with AF screening. Subjects with clinical AF were older with more co-morbidities compared with subclinical AF (no surface ECG confirmation of AF) (P < 0.001). In subjects with subclinical AF (n = 202), therapy adjustments were performed in only 7%.

Conclusion

Smartphone-based AF screening is feasible at large scale. Screening increased OAC uptake and impacted therapy of both new and previously diagnosed clinical AF but failed to impact risk factor management in subjects with subclinical AF.

Detecting Paroxysmal Atrial Fibrillation From an Electrocardiogram in Sinus Rhythm: External Validation of the AI Approach

BACKGROUND

Atrial fibrillation (AF) may occur asymptomatically and can be diagnosed only with electrocardiography (ECG) while the arrhythmia is present.

OBJECTIVES

The aim of this study was to independently validate the approach of using artificial intelligence (AI) to identify underlying paroxysmal AF from a 12-lead ECG in sinus rhythm (SR).

METHODS

An AI algorithm was trained to identify patients with underlying paroxysmal AF, using electrocardiographic data from all in- and outpatients from a single center with at least 1 ECG in SR. For patients without AF, all ECGs in SR were included. For patients with AF, all ECGs in SR starting 31 days before the first AF event were included. The patients were randomly allocated to training, internal validation, and testing datasets in a 7:1:2 ratio. In a secondary analysis, the AF prevalence of the testing group was modified. Additionally, the performance of the algorithm was validated at an external hospital.

RESULTS

The dataset consisted of 494,042 ECGs in SR from 142,310 patients. Testing the model on the first ECG of each patient (AF prevalence 9.0%) resulted in accuracy of 78.1% (95% CI: 77.6%-78.5%), area under the receiver-operating characteristic curve of 0.87 (95% CI: 0.86-0.87), and area under the precision recall curve (AUPRC) of 0.48 (95% CI: 0.46-0.50). In a low-risk group (AF prevalence 3%), the AUPRC decreased to 0.21 (95% CI: 0.18-0.24). In a high-risk group (AF prevalence 30%), the AUPRC increased to 0.76 (95% CI: 0.75-0.78). This performance was robust when validated in an external hospital.

CONCLUSIONS

The approach of using an AI-enabled electrocardiographic algorithm for the identification of patients with underlying paroxysmal AF from ECGs in SR was independently validated.