Citations

Abstract Aims Monitoring pulmonary congestion in chronic heart failure (HF) reduces decompensation and hospitalization, but conventional methods such as weight and symptom tracking are often unreliable. As fluid accumulation affects the lungs and vocal tract, subtle voice alterations may serve as a non-invasive signal for early detection of worsening HF. Methods and results The Voice Analysis for Monitoring Patients with HF trial (VAMP-HF, NCT06566911) prospectively enrolled 104 patients hospitalized with acute decompensated HF (ADHF) across two academic centres in the USA and Germany. Daily voice recordings were collected from admission to discharge, with breathing features extracted from speech and acoustic features from sustained vowels. A machine-learning model was trained to classify recordings as admission-phase vs. discharge-phase using leave-one-patient-out. Patients with clinical deterioration, insufficient audio quality, or short length of stay were excluded. Seventy-nine patients were included in the final dataset. The model classified admission and discharge with an F1-score of 0.83 (95% CI: 0.77–0.90; AUC = 0.90). In patients with higher audio volume (N = 54), performance reached 0.89 (95% CI: 0.82–0.94; AUC = 0.91). When applied to intermediate hospitalization days, model-predicted scores showed progressive increases from admission towards discharge. Performance remained robust irrespective of significant weight loss during hospitalization. Conclusion In this pilot study, structured voice and breathing analysis discriminated hospitalization phase from admission through discharge in patients with ADHF. This non-invasive approach captured progressive changes during the hospital course and warrants further investigation with concurrent objective congestion markers to establish physiological specificity.

Abstract Background Effective fluid status monitoring in chronic heart failure (HF) patients is critical for preventing decompensation and hospitalization. Conventional methods, such as daily weight tracking, often fail to detect early, pre-symptomatic fluid retention. As fluid accumulation affects the vocal tract, subtle voice alterations can serve as a biomarker for congestion. In this work, we present preliminary results from the first 55 patients enrolled at the German Heart Center at Charité (DHZC) within the multicenter VAMP-HF study, assessing the performance of AI-driven voice analysis for non-invasive fluid status monitoring. Methods The study design incorporated a run-in period for the first 20 patients to refine the recording setup and ensure sufficient audio quality for subsequent data collection. An additional three patients were excluded due to clinical deterioration or in-hospital mortality. Participants provided daily voice recordings (sustained vowels, standardized text, and varying sentences) alongside clinical measurements such as NT-proBNP, daily weight, and left-ventricular ejection fraction. A speaker-independent XGBoost classifier was trained on acoustic features to classify patient states (admission, discharge, and intermediate states). Performance was evaluated using a nested leave-one-patient-out approach, ensuring strict training and validation data separation. Results The final analysis included 32 patients (mean age 76.12 ± 12.65 years). At admission, patients had a mean NT-proBNP of 9575.55 ± 8202.87 pg/mL and a mean weight loss of 4.2 ± 4.29 kg until discharge. The trained machine learning model achieved an F1-score of 0.75 (Fig. 1b), demonstrating its ability to distinguish between admission and discharge states, based solely on voice samples. An exemplary prediction for a test patient is shown in Fig. 1a. Notably, the model was also able to detect subtle vocal changes even in patients with <2 kg weight loss. Conclusion Our findings suggest that AI-driven voice analysis is a feasible biomarker for detecting heart failure decompensation. While not directly compared with conventional early warning thresholds, these results support the hypothesis that voice-based biomarkers may serve as an early indicator of fluid congestion. Further validation in larger cohorts is needed to confirm these findings and enable integration into remote HF monitoring programs.

Abstract Atrial fibrillation (AF) is increasingly diagnosed early, close to its first occurrence due to: (i) increased public awareness with self-screening; (ii) health care initiatives including population screening and opportunistic case finding; and (iii) increased use and surveillance of implantable cardiac devices. At its onset, AF is often low burden, and cardiovascular co-morbidities may be absent or at an early stage. Thus, the management of recent-onset AF has become an issue of growing importance. Professional guidelines have traditionally focused on anticoagulant thromboprophylaxis, generally recommending a cautious approach to rhythm control, and priority has been given to rate control to alleviate symptoms. In recent guidelines, the importance of managing lifestyle and co-morbidities has increased. The AF-SCREEN collaboration proposes that a vigorous approach to active management of recent-onset AF may be warranted. This includes addressing co-morbidities and promoting healthy lifestyles to prevent the emergence or progression of AF and associated cardiovascular disease, as well as the initiation of active rhythm control ± anticoagulation to prevent AF-related morbidity and mortality, including stroke and heart failure (HF). Intuitively, intervention early after AF onset would be beneficial since lifestyle and co-morbidity management, plus rhythm control and anticoagulation, are important contributors to improved outcomes in patients with established AF, but robust evidence is lacking for recent-onset AF. There is a delicate balance between achieving favourable outcomes such as preventing strokes, HF and AF progression vs the complications and potential adverse effects of interventions. Given the serious long-term consequences, innovative approaches are necessary to determine the value and risks of initiating active therapy very early in the course of AF. More data are needed to guide the best management of recent-onset AF, bearing AF burden in mind. Long-term studies using large national databases linked to electronic medical records and rhythm monitoring devices offer excellent opportunities. Shorter-term studies focusing on reducing AF burden to slow AF progression and studies focusing on outcomes such as HF could be used in both randomized clinical trials and observational cohort studies.

Abstract Aims Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field. Methods and results An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (<20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP. Conclusion The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.

Background: Pulsed field ablation uses electrical pulses to cause nonthermal irreversible electroporation and induce cardiac cell death. Pulsed field ablation may have effectiveness comparable to traditional catheter ablation while preventing thermally mediated complications. Methods: The PULSED AF pivotal study (Pulsed Field Ablation to Irreversibly Electroporate Tissue and Treat AF) was a prospective, global, multicenter, nonrandomized, paired single-arm study in which patients with paroxysmal (n=150) or persistent (n=150) symptomatic atrial fibrillation (AF) refractory to class I or III antiarrhythmic drugs were treated with pulsed field ablation. All patients were monitored for 1 year using weekly and symptomatic transtelephonic monitoring; 3-, 6-, and 12-month ECGs; and 6- and 12-month 24-hour Holter monitoring. The primary effectiveness end point was freedom from a composite of acute procedural failure, arrhythmia recurrence, or antiarrhythmic escalation through 12 months, excluding a 3-month blanking period to allow recovery from the procedure. The primary safety end point was freedom from a composite of serious procedure- and device-related adverse events. Kaplan-Meier methods were used to evaluate the primary end points. Results: Pulsed field ablation was shown to be effective at 1 year in 66.2% (95% CI, 57.9 to 73.2) of patients with paroxysmal AF and 55.1% (95% CI, 46.7 to 62.7) of patients with persistent AF. The primary safety end point occurred in 1 patient (0.7%; 95% CI, 0.1 to 4.6) in both the paroxysmal and persistent AF cohorts. Conclusions: PULSED AF demonstrated a low rate of primary safety adverse events (0.7%) and provided effectiveness consistent with established ablation technologies using a novel irreversible electroporation energy to treat patients with AF. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04198701.

Abstract Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.

Background: Pulsed field ablation (PFA) is a novel form of ablation using electrical fields to ablate cardiac tissue. There are only limited data assessing the feasibility and safety of this type of ablation in humans. Methods: PULSED AF (Pulsed Field Ablation to Irreversibly Electroporate Tissue and Treat AF; https://www.clinicaltrials.gov ; unique identifier: NCT04198701) is a nonrandomized, prospective, multicenter, global, premarket clinical study. The first-in-human pilot phase evaluated the feasibility and efficacy of pulmonary vein isolation using a novel PFA system delivering bipolar, biphasic electrical fields through a circular multielectrode array catheter (PulseSelect; Medtronic, Inc). Thirty-eight patients with paroxysmal or persistent atrial fibrillation were treated in 6 centers in Australia, Canada, the United States, and the Netherlands. The primary outcomes were ability to achieve acute pulmonary vein isolation intraprocedurally and safety at 30 days. Results: Acute electrical isolation was achieved in 100% of pulmonary veins (n=152) in the 38 patients. Skin-to-skin procedure time was 160±91 minutes, left atrial dwell time was 82±35 minutes, and fluoroscopy time was 28±9 minutes. No serious adverse events related to the PFA system occurred in the 30-day follow-up including phrenic nerve injury, esophageal injury, stroke, or death. Conclusions: In this first-in-human clinical study, 100% pulmonary vein isolation was achieved using only PFA with no PFA system–related serious adverse events. Graphic Abstract: A graphic abstract is available for this article.

Abstract Aims To develop quality indicators (QIs) that may be used to evaluate the quality of care and outcomes for adults with atrial fibrillation (AF). Methods and results We followed the ESC methodology for QI development. This methodology involved (i) the identification of the domains of AF care for the diagnosis and management of AF (by a group of experts including members of the ESC Clinical Practice Guidelines Task Force for AF); (ii) the construction of candidate QIs (including a systematic review of the literature); and (iii) the selection of the final set of QIs (using a modified Delphi method). Six domains of care for the diagnosis and management of AF were identified: (i) Patient assessment (baseline and follow-up), (ii) Anticoagulation therapy, (iii) Rate control strategy, (iv) Rhythm control strategy, (v) Risk factor management, and (vi) Outcomes measures, including patient-reported outcome measures (PROMs). In total, 17 main and 17 secondary QIs, which covered all six domains of care for the diagnosis and management of AF, were selected. The outcome domain included measures on the consequences and treatment of AF, as well as PROMs. Conclusion This document defines six domains of AF care (patient assessment, anticoagulation, rate control, rhythm control, risk factor management, and outcomes), and provides 17 main and 17 secondary QIs for the diagnosis and management of AF. It is anticipated that implementation of these QIs will improve the quality of AF care.