Aims:Simplified detection of atrial arrhythmias via consumer-electronics would enable earlier therapy in at-risk populations. Whether this is feasible and effective in older populations is not known. Methods and results:The fully remote, investigator-initiated Smartphone and wearable detected atrial arrhythmia in Older Adults Case finding study (Smart in OAC-AFNET 9) digitally enrolled participants ≥65 years without known atrial fibrillation, not receiving oral anticoagulation in Germany, Poland, and Spain for 8 weeks. Participants were invited by media communications and direct contacts. Study procedures adhered to European data protection. Consenting participants received a wristband with a photoplethysmography sensor to be coupled to their smartphone. The primary outcome was the detection of atrial arrhythmias lasting 6 min or longer in the first 4 weeks of monitoring. Eight hundred and eighty-two older persons (age 71 ± 5 years, range 65-90, 500 (57%) women, 414 (47%) hypertension, and 97 (11%) diabetes) recorded signals. Most participants (72%) responded to adverts or word of mouth, leaflets (11%) or general practitioners (9%). Participation was completely remote in 469/882 persons (53%). During the first 4 weeks, participants transmitted PPG signals for 533/696 h (77% of the maximum possible time). Atrial arrhythmias were detected in 44 participants (5%) within 28 days, and in 53 (6%) within 8 weeks. Detection was highest in the first monitoring week [incidence rates: 1st week: 3.4% (95% confidence interval 2.4-4.9); 2nd-4th week: 0.55% (0.33-0.93)]. Conclusion:Remote, digitally supported consumer-electronics-based screening is feasible in older European adults and identifies atrial arrhythmias in 5% of participants within 4 weeks of monitoring (NCT04579159).

Introduction Screening for atrial fibrillation and timely initiation of oral anticoagulation, rhythm management, and treatment of concomitant cardiovascular conditions can improve outcomes in high-risk populations. Whether wearables can facilitate screening in older adults is not known. Methods and Analyses The multicenter, international, investigator-initiated, single-arm case-finding Smartphone and wearable detected atrial arrhythmia in older adults case finding study (Smart in OAC – AFNET 9) evaluates the diagnostic yield of a validated, cloud-based analysis algorithm detecting atrial arrhythmias via a signal acquired by a smartphone-coupled wristband monitoring system in older adults. Unselected participants aged ≥65 years without known atrial fibrillation and not receiving oral anticoagulation are enrolled in three European countries. Participants undergo continuous pulse monitoring using a wristband with a photo plethysmography (PPG) sensor and a telecare analytic service. Participants with PPG-detected atrial arrhythmias will be offered ECG loop monitoring. The study has a virtual design with digital consent and teleconsultations, whilst including hybrid solutions. Primary outcome is the proportion of older adults with newly detected atrial arrhythmias (NCT04579159). Discussion Smart in OAC – AFNET 9 will provide information on wearable-based screening for PPG-detected atrial arrhythmias in Europe and provide an estimate of the prevalence of atrial arrhythmias in an unselected population of older adults.

Abstract Background The dynamic changes and stability of blood biomarkers over time and after treatment are not well known. In this study, we describe changes in 12 centrally quantified known and novel cardiovascular biomarkers, prior to and 3 months after ablation for atrial fibrillation (AF). Purpose In patients enrolled in the AXAFA-AFNET5 trial, we 1) characterised changes in 12 biomarker levels pre and post-ablation, 2) ascertained if biomarker changes are consistent between males and females, and 3) identified biomarkers which predict recurrent AF post-ablation. Methods and results Of the 674 patients who were recruited and randomised, 633 received the study drug and underwent ablation. Peripheral blood samples were available for 488 patients at baseline and 434 at 3 months follow-up (median age [Q1, Q3] 64 [58, 70] years; 34% female). Between baseline (BL) and follow-up (FU), paired comparisons revealed that 3 biomarkers decreased, ANG2 (median [Q1, Q3] BL 2.185 [1.711, 3.115], FU 1.827 [1.457, 2.297] ng/mL, p<0.001), BMP10 (BL 2.056 [1.810, 2.380], FU 1.986 [1.757, 2.260] ng/mL, p<0.001), and NTproBNP (BL 2.219 [0.858, 5.731] per 100pg/mL, p<0.001), while 1 biomarker increased, FABP3 (BL 2.911 [2.425, 3.508], FU 2.911 [2.462, 3.521], p=0.005). The remaining 8 biomarkers remained unchanged. Significant differences in ANG2, BMP10, NTproBNP and FABP3 were driven by patients who remained arrhythmia free at follow-up whereas biomarker levels remained unchanged in 121 patients who experienced recurrent AF (39%; Figure). Change scores were mainly consistent between males and females, however, CRP decreased significantly more in females. Recurrent AF episodes were not different between males and females (p=0.319). Cox proportional hazards model assessed the relationship of individual biomarkers at baseline for predicting recurrent AF. Elevated ANG2 (hazard ratio, HR per ng/mL [95% confidence interval] 1.214 [1.113, 1.325]), BMP10 (HR per ng/mL 1.516 [1.039, 2.214]), and NTproBNP (HR per 100 pg/mL 1.050 [1.025, 1.076]) significantly predicted increased risk for recurrent AF, after adjustment for age, sex, body mass index, hypertension, diabetes, chronic obstructive pulmonary disease, stroke, heart failure, ablation type (PVI, PVI and other, other), ablation energy (radiofrequency, cryoablation, other), and treatment arm. Conclusion In this study, most cardiovascular biomarkers are unchanged after ablation for AF, however, ANG2, BMP10, and NTproBNP decreased at follow-up. These effects are driven by patients who remained arrhythmia free and could potentially reflect improvement in vascular (ANG2), endothelial (BMP10), and myocardial load (NTproBNP) parameters post-ablation. This outcome corresponds with the observation that elevated levels of these biomarkers at baseline predict recurrent AF at 3 months. Both males and females demonstrate similar changes in biomarker profiles and benefit equally from ablation for AF. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): DZHK (German Centre for Cardiovascular Research) and BMBF (German Ministry of Education and Research) to AFNET.Additional support from European Union [grant agreement No. 633196 (CATCH ME)]. Biomarker changes

Atrial fibrillation (AF) can be challenging to diagnose due to asymptomatic and paroxysmal presentation. Identifying prognostic factors of AF would elucidate potential mechanisms causing AF and refine screening for at risk patients. To identify the main predictors of AF and to develop a prognostic model for prevalent AF. Data of 120 potential predictors were harmonised in individual patient data from 4 independent European studies. A three stage Delphi expert consensus process identified predictors based on clinical knowledge. The predictors were further reduced using statistical selection (backward elimination), and a logistic regression model was fitted. We calculated odds ratios (OR) for each of the selected predictors and evaluated model performance using the C-statistic. Overall, 2420 patients (mean [standard deviation] age = 62.7 [14.5] years, 35.6% female, 43.1% with AF) were included in the analysis. Thirty-one potential predictors identified from the Delphi process which had sufficient data across all datasets were modelled. Of these 14 were deemed prognostic in predicting AF (age, sex, BMI, height, hypertension, diabetes, history of coronary artery disease, left atrial volume, left ventricular end systolic diameter, abnormality on echo, tricuspid valve disease of at least moderate intensity, aldosterone-antagonists, beta-blockers and P2Y12 blockers; see Figure 1). There was a clear interaction between age and sex indicating that males are at higher risk than females early in life, while females are at increased risk of AF at older age (Figure 1). The risk prediction model combining these prognostic factors performed well (C-statistic 0.79; 95% CI 0.77–0.81). Figure 1. (a) Forest plot; (b) Interaction Our preliminary analysis identified important prognostic factors and a complex relationship between age and sex, which predicts prevalent AF, highlighting the different potential causes of AF in different patients. There is a clear need to validate these factors in external datasets and for further investigation into the molecular mechanism underlying these factors. European Commission H2020 framework