COMparison of Physiological Algorithms for Real-time Evaluation of Atrial Fibrillation

Обзор

This is a cohort study to evaluate algorithms for mapping atrial fibrillation by mapping atrial structure and mapping electrical activity to detect physiologically important areas. A key innovation of the observation study is to assess tracking of physiological activity in AF, in relation to tissue activity (MAP recordings), and in relation to AF waves of activity. This observational work extends prior studies that focused on focal and rotational activity, dispersion of electrical activity, low voltage activity and others.

Подробное описание

In this study, the investigators will compare mapping approaches for atrial fibrillation ablation.

First, investigators will compare approaches to map atrial structure from collected computed tomography (CT) scans or magnetic resonance imaging (MRI) scans acquired routinely in patients undergoing ablation for atrial fibrillation. Imaging will be acquired from a variety of clinically approved

Second, investigators will compare approaches to analyze electrograms collected routinely at clinical electrophysiology study (EPS) in patients undergoing ablation for atrial fibrillation. Electrograms will be recorded from a variety of clinically approved catheters under standard of care conditions, and will be analyzed to better understand the mechanisms for atrial fibrillation and its relationship to outcomes. Electrograms include unipolar recordings, bipolar recordings and, monophasic action potential recordings using FDA-cleared catheters.

The investigators anticipate analyzing data from 1000 patients from inception of the study.

Первичные конечные точки

• Diagnostic Accuracy [Срок оценки: At the time of procedure]
• Diagnostic Accuracy [Срок оценки: At time of procedure.]

Вторичные конечные точки (1)

• Analysis Time [Срок оценки: At time of procedure.]

Критерии участия

Критерии включения

• aged 21-80 yrs who underwent electrophysiology study for ablation of Atrial Fibrillation

Критерии исключения

• Poor data quality of recordings of AF

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

США · 1 центр

• Stanford University — Stanford

Публикации

• Alhusseini M, Vidmar D, Meckler GL, Kowalewski CA, Shenasa F, Wang PJ, Narayan SM, Rappel WJ. Two Independent Mapping Techniques Identify Rotational Activity Patterns at Sites of Local Termination During Persistent Atrial Fibrillation. J Cardiovasc Electrophysiol. 2017 Jun;28(6):615-622. doi: 10.1111/jce.13177. Epub 2017 Mar 20. PMID 28185348
• Alhusseini MI, Abuzaid F, Rogers AJ, Zaman JAB, Baykaner T, Clopton P, Bailis P, Zaharia M, Wang PJ, Rappel WJ, Narayan SM. Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation: Machine Learning of Atrial Fibrillation. Circ Arrhythm Electrophysiol. 2020 Aug;13(8):e008160. doi: 10.1161/CIRCEP.119.008160. Epub 2020 Jul 6. PMID 32631100
• Tang S, Razeghi O, Kapoor R, Alhusseini MI, Fazal M, Rogers AJ, Rodrigo Bort M, Clopton P, Wang PJ, Rubin DL, Narayan SM, Baykaner T. Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes. Circ Arrhythm Electrophysiol. 2022 Aug;15(8):e010850. doi: 10.1161/CIRCEP.122.010850. Epub 2022 Jul 22. PMID 35867397
• Svennberg E, Han JK, Caiani EG, Engelhardt S, Ernst S, Friedman P, Garcia R, Ghanbari H, Hindricks G, Man SH, Millet J, Narayan SM, Ng GA, Noseworthy PA, Tjong FVY, Ramirez J, Singh JP, Trayanova N, Duncker D, Tfelt Hansen J, Barker J, Casado-Arroyo R, Chatterjee NA, Conte G, Diederichsen SZ, Linz D, Mahtani AU, Zorzi A. State of the Art of Artificial Intelligence in Clinical Electrophysiology in PMID 40163651
• Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS; American Heart Association Institute for Precision Cardiovascular Medicine; Council on Cardiovascular and Stroke Nursing; Council on Lifelong Congenital Heart Disease and Heart Health in the Young; Council on Cardiovascular Radiology an PMID 38415358
• Narayan SM, Wan EY, Andrade JG, Avari Silva JN, Bhatia NK, Deneke T, Deshmukh AJ, Chon KH, Erickson L, Ghanbari H, Noseworthy PA, Pathak RK, Roelle L, Seiler A, Singh JP, Srivatsa UN, Trela A, Tsiperfal A, Varma N, Yousuf OK. Visions for digital integrated cardiovascular care: HRS Digital Health Committee perspectives. Cardiovasc Digit Health J. 2024 Feb 24;5(2):37-49. doi: 10.1016/j.cvdhj.2024.02 PMID 38765620
• Armoundas AA, Ahmad FS, Bennett DA, Chung MK, Davis LL, Dunn J, Narayan SM, Slotwiner DJ, Wiley KK Jr, Khera R; American Heart Association Data Science and Precision Medicine Committee of the Council on Genomic and Precision Medicine and Council on Clinical Cardiology; Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation; Council on Cardiovascular and Stroke Nursing; Council PMID 38779844
• de Groot NMS, Kleber A, Narayan SM, Ciaccio EJ, Doessel O, Bernus O, Berenfeld O, Callans D, Fedorov V, Hummel J, Haissaguerre M, Natale A, Trayanova N, Spector P, Vigmond E, Anter E. Atrial fibrillation nomenclature, definitions, and mechanisms: Position paper from the international Working Group of the Signal Summit. Heart Rhythm. 2025 Jun;22(6):1480-1491. doi: 10.1016/j.hrthm.2024.11.012. Epub PMID 39561931

Идентификаторы

NCT: NCT02997254 · COMPARE_AF

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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