Mobile app-based symptom-rhythm correlation assessment in patients with persistent atrial fibrillation

Background: The assessment of symptom-rhythm correlation (SRC) in patients with persistent atrial fibrillation (AF) is challenging. Therefore, we performed a novel mobile app-based approach to assess SRC in persistent AF.Methods: Consecutive persistent AF patients planned for electrical cardioversion (ECV) used a mobile app to record a 60-s photoplethysmogram (PPG) and report symptoms once daily and in case of symptoms for four weeks prior and three weeks after ECV. Within each patient, SRC was quantified by the SRC-index defined as the sum of symptomatic AF recordings and asymptomatic non-AF recordings divided by the sum of all recordings.Results: Of 88 patients (33% women, age 68 +/- 9 years) included, 78% reported any symptoms during recordings. The overall SRC-index was 0.61 (0.44-0.79). The study population was divided into SRC-index tertiles: low (= 0.73). Patients within the low (vs high) SRC-index tertile had more often heart failure and diabetes mellitus (both 24.1% vs 6.9%). Extrasystoles occurred in 19% of all symptomatic non-AF PPG recordings. Within each patient, PPG recordings with the highest (vs lowest) tertile of pulse rates conferred an increased risk for symptomatic AF recordings (odds ratio [OR] 1.26, 95% coincidence interval [CI] 1.04-1.52) and symptomatic non-AF recordings (OR 2.93, 95% CI 2.16-3.97). Pulse variability was not associated with reported symptoms.Conclusions: In patients with persistent AF, SRC is relatively low. Pulse rate is the main determinant of reported symptoms. Further studies are required to verify whether integrating mobile app-based SRC assessment in current workflows can improve AF management

Mobile health solutions for atrial fibrillation detection and management: a systematic review

AimWe aimed to systematically review the available literature on mobile Health (mHealth) solutions, including handheld and wearable devices, implantable loop recorders (ILRs), as well as mobile platforms and support systems in atrial fibrillation (AF) detection and management.MethodsThis systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The electronic databases PubMed (NCBI), Embase (Ovid), and Cochrane were searched for articles published until 10 February 2021, inclusive. Given that the included studies varied widely in their design, interventions, comparators, and outcomes, no synthesis was undertaken, and we undertook a narrative review.ResultsWe found 208 studies, which were deemed potentially relevant. Of these studies included, 82, 46, and 49 studies aimed at validating handheld devices, wearables, and ILRs for AF detection and/or management, respectively, while 34 studies assessed mobile platforms/support systems. The diagnostic accuracy of mHealth solutions differs with respect to the type (handheld devices vs wearables vs ILRs) and technology used (electrocardiography vs photoplethysmography), as well as application setting (intermittent vs continuous, spot vs longitudinal assessment), and study population.ConclusionWhile the use of mHealth solutions in the detection and management of AF is becoming increasingly popular, its clinical implications merit further investigation and several barriers to widespread mHealth adaption in healthcare systems need to be overcome

Artificial intelligence for the detection, prediction, and management of atrial fibrillation

The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning

Photoplethysmography-documented atrial fibrillation in the first week after catheter ablation is associated with lower success rates

AimsTo test the feasibility of postprocedural photoplethysmography (PPG) rhythm telemonitoring during the first week after atrial fibrillation (AF) ablation and its predictive value for later AF recurrence.MethodsPPG rhythm telemonitoring during the first week after the ablation procedure was offered to a total of 382 consecutive patients undergoing AF ablation. Patients were instructed to perform 1 min PPG recordings by a mobile health application 3 times per day and in case of symptoms. Clinicians assessed the PPG tracings via a secured cloud and the information was remotely integrated into the therapeutic pathway via teleconsultation (TeleCheck-AF approach).Results119 patients (31%) agreed to perform PPG rhythm telemonitoring after ablation. Patients included in the TeleCheck-AF approach were younger compared to those who declined participation (58 & PLUSMN; 10 vs. 62 & PLUSMN; 10 years, p < 0.001). Median follow up duration was 544 (53-883) days. 27% of patients had PPG tracings suggestive of AF in the week following the ablation. In 24% of patients, the integration of PPG rhythm telemonitoring resulted in a remote clinical intervention during teleconsultation. During follow-up of one year, 33% of patients had ECG-documented AF recurrences. PPG recordings suggestive of AF in the week after ablation were predictive of late recurrences (p < 0.001).ConclusionPPG rhythm telemonitoring during the first week after AF ablation often triggered clinical interventions. Due to its high availability, PPG-based follow-up actively involving patients after AF ablation may close a diagnostic and prognostic gap in the blanking period and increase active patient-involvement

Frequency and Determinants of Spontaneous Conversion to Sinus Rhythm in Patients Presenting to the Emergency Department with Recent-onset Atrial Fibrillation: A Systematic Review

The exact frequency and clinical determinants of spontaneous conversion (SCV) in patients with symptomatic recent-onset AF are unclear. The aim of this systematic review is to provide an overview of the frequency and determinants of SCV of AF in patients presenting at the emergency department. A comprehensive literature search for studies about SCV in patients presenting to the emergency department with AF resulted in 25 articles - 12 randomised controlled trials and 13 observational studies. SCV rates range between 9-83% and determinants of SCV also varied between studies. The most important determinants of SCV included short duration of AF

Frequency and Determinants of Spontaneous Conversion to Sinus Rhythm in Patients Presenting to the Emergency Department with Recent-onset Atrial Fibrillation:A Systematic Review

The exact frequency and clinical determinants of spontaneous conversion (SCV) in patients with symptomatic recent-onset AF are unclear. The aim of this systematic review is to provide an overview of the frequency and determinants of SCV of AF in patients presenting at the emergency department. A comprehensive literature search for studies about SCV in patients presenting to the emergency department with AF resulted in 25 articles - 12 randomised controlled trials and 13 observational studies. SCV rates range between 9-83% and determinants of SCV also varied between studies. The most important determinants of SCV included short duration of AF (<24 or <48 hours), low number of episodes, normal atrial dimensions and absence of previous heart disease. The large variation in SCV rate and determinants of SCV was related to differences in duration of the observation period, inclusion and exclusion criteria and in variables used in the prediction models