The digital journey: 25 years of digital development in electrophysiology from an Europace perspective

Background and Aims Pulmonary Vein Isolation (PVI) is the cornerstone of ablation for atrial fibrillation. Confirmation of PVI can be challenging due to far field electrograms and sometimes requires additional pacing maneuvers or mapping. This prospective multicenter study assessed the agreement between a previously trained automated algorithm designed to determine vein isolation status with expert opinion in real-world clinical setting. Method Consecutive patients scheduled for PVI were recruited at four centers. The ECGenius electrophysiology recording system (CathVision ApS, Denmark) was connected in parallel with the lab’s existing system. Electrograms from a circular mapping catheter were annotated during sinus rhythm at baseline pre-ablation, time of isolation, and post-ablation. The ground truth for isolation status was based on operator opinion. The algorithm was applied to the collected PV signals off-line and compared to expert opinion. The primary endpoint was a sensitivity and specificity exceeding 80%. Results Overall, 498 electrograms (248 at baseline and 250 at PVI) with 5,473 individual PV beats from 89 patients (32 females, 62 ±12 years) were analyzed. The algorithm performance reached an area under curve (AUC) of 92% and met the primary study endpoint with a sensitivity and specificity of 86% and 87%, respectively (p = 0.005; p = 0.004). The algorithm had an accuracy of 87% in classifying the time of isolation. Conclusion This study validated an automated algorithm using machine learning (ML) to assess the isolation status of pulmonary veins in patients undergoing PVI with different ablation modalities. The algorithm reached an AUC of 92% with both sensitivity and specificity exceeding the primary study endpoints.

Section: Discussionmentioning

confidence: 99%

Validation of a machine learning algorithm to identify pulmonary vein isolation during ablation procedures for the treatment of atrial fibrillation: results of the PVISION study

De Pooter,

Timmers,

Boveda

et al. 2024

“… 1 Digitalization of AF management programs is appealing to make such integrated approach feasible. 29 While research on digitalization is experiencing rapid and extensive growth, 30 further studies are needed to inform clinicians on the performance and limitations of the digital tools and to clarify whether this will lead to better outcomes.…”

Section: Discussionmentioning

confidence: 99%

Real-world validation of smartphone-based photoplethysmography for rate and rhythm monitoring in atrial fibrillation

Gruwez,

Ezzat,

Van Puyvelde

et al. 2024

Aims Photoplethysmography- (PPG) based smartphone applications facilitate heart rate and rhythm monitoring in patients with paroxysmal and persistent atrial fibrillation (AF). Despite an endorsement from the European Heart Rhythm Association, validation studies in this setting are lacking. Therefore, we evaluated the accuracy of PPG-derived heart rate and rhythm classification in subjects with an established diagnosis of AF in unsupervised real-world conditions. Methods and results Fifty consecutive patients were enrolled, 4 weeks before undergoing AF ablation. Patients used a handheld single-lead electrocardiography (ECG) device and a fingertip PPG smartphone application to record 3907 heart rhythm measurements twice daily during 8 weeks. The ECG was performed immediately before and after each PPG recording and was given a diagnosis by the majority of three blinded cardiologists. A consistent ECG diagnosis was exhibited along with PPG data of sufficient quality in 3407 measurements. A single measurement exhibited good quality more often with ECG (93.2%) compared to PPG (89.5%; P < 0.001). However, PPG signal quality improved to 96.6% with repeated measurements. Photoplethysmography-based detection of AF demonstrated excellent sensitivity [98.3%; confidence interval (CI): 96.7–99.9%], specificity (99.9%; CI: 99.8–100.0%), positive predictive value (99.6%; CI: 99.1–100.0%), and negative predictive value (99.6%; CI: 99.0–100.0%). Photoplethysmography underestimated the heart rate in AF with 6.6 b.p.m. (95% CI: 5.8 b.p.m. to 7.4 b.p.m.). Bland–Altman analysis revealed increased underestimation in high heart rates. The root mean square error was 11.8 b.p.m. Conclusion Smartphone applications using PPG can be used to monitor patients with AF in unsupervised real-world conditions. The accuracy of AF detection algorithms in this setting is excellent, but PPG-derived heart rate may tend to underestimate higher heart rates.

“…Recent data from the RACE V registry suggest that paroxysmal AF has a higher burden that can be further differentiated into subgroups. 76 , 77 While continuous rhythm monitoring is the preferred method to evaluate AF burden, serial longer-term monitor 10 , 78 or even long-term intermittent monitoring by recording one to three short-term handheld ECGs per day provides effective, albeit less precise alternative methods. 79 So far, these rhythm monitoring methods have mainly been used in research settings.…”

Section: Atrial Fibrillation Burden In Patients With Electrocardiogra...mentioning

confidence: 99%

Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference

Linz,

Andrade,

Arbelo

et al. 2024

Self Cite

Aims Recent trial data demonstrate beneficial effects of active rhythm management in patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is associated with a low risk of AF-related complications. The aim of this document is to summarize the key outcomes of the 9th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). Methods and results Eighty-three international experts met in Münster for 2 days in September 2023. Key findings are as follows: (i) Active rhythm management should be part of the default initial treatment for all suitable patients with AF. (ii) Patients with device-detected AF have a low burden of AF and a low risk of stroke. Anticoagulation prevents some strokes and also increases major but non-lethal bleeding. (iii) More research is needed to improve stroke risk prediction in patients with AF, especially in those with a low AF burden. Biomolecules, genetics, and imaging can support this. (iv) The presence of AF should trigger systematic workup and comprehensive treatment of concomitant cardiovascular conditions. (v) Machine learning algorithms have been used to improve detection or likely development of AF. Cooperation between clinicians and data scientists is needed to leverage the potential of data science applications for patients with AF. Conclusions Patients with AF and a low arrhythmia burden have a lower risk of stroke and other cardiovascular events than those with a high arrhythmia burden. Combining active rhythm control, anticoagulation, rate control, and therapy of concomitant cardiovascular conditions can improve the lives of patients with AF.