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Lillo-Castellano JM, González-Ferrer JJ, Marina-Breysse M, Martínez-Ferrer JB, Pérez-Álvarez L, Alzueta J, Martínez JG, Rodríguez A, Rodríguez-Pérez JC, Anguera I, Viñolas X, García-Alberola A, Quintanilla JG, Alfonso-Almazán JM, García J, Borrego L, Cañadas-Godoy V, Pérez-Castellano N, Pérez-Villacastín J, Jiménez-Díaz J, Jalife J, Filgueiras-Rama D. Personalized monitoring of electrical remodelling during atrial fibrillation progression via remote transmissions from implantable devices. Europace 2021; 22:704-715. [PMID: 31840163 PMCID: PMC7203636 DOI: 10.1093/europace/euz331] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 11/12/2019] [Indexed: 11/29/2022] Open
Abstract
Aims Atrial electrical remodelling (AER) is a transitional period associated with the progression and long-term maintenance of atrial fibrillation (AF). We aimed to study the progression of AER in individual patients with implantable devices and AF episodes. Methods and results Observational multicentre study (51 centres) including 4618 patients with implantable cardioverter-defibrillator +/−resynchronization therapy (ICD/CRT-D) and 352 patients (2 centres) with pacemakers (median follow-up: 3.4 years). Atrial activation rate (AAR) was quantified as the frequency of the dominant peak in the signal spectrum of AF episodes with atrial bipolar electrograms. Patients with complete progression of AER, from paroxysmal AF episodes to electrically remodelled persistent AF, were used to depict patient-specific AER slopes. A total of 34 712 AF tracings from 830 patients (87 with pacemakers) were suitable for the study. Complete progression of AER was documented in 216 patients (16 with pacemakers). Patients with persistent AF after completion of AER showed ∼30% faster AAR than patients with paroxysmal AF. The slope of AAR changes during AF progression revealed patient-specific patterns that correlated with the time-to-completion of AER (R2 = 0.85). Pacemaker patients were older than patients with ICD/CRT-Ds (78.3 vs. 67.2 year olds, respectively, P < 0.001) and had a shorter median time-to-completion of AER (24.9 vs. 93.5 days, respectively, P = 0.016). Remote transmissions in patients with ICD/CRT-D devices enabled the estimation of the time-to-completion of AER using the predicted slope of AAR changes from initiation to completion of electrical remodelling (R2 = 0.45). Conclusion The AF progression shows patient-specific patterns of AER, which can be estimated using available remote-monitoring technology.
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Affiliation(s)
- José María Lillo-Castellano
- Advanced Development in Arrhythmia Mechanisms and Therapy Laboratory, Myocardial Pathophysiology Area, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.,Fundación Interhospitalaria para la Investigación Cardiovascular (FIC), Madrid, Spain
| | - Juan José González-Ferrer
- Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC). Cardiovascular Institute, Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Manuel Marina-Breysse
- Advanced Development in Arrhythmia Mechanisms and Therapy Laboratory, Myocardial Pathophysiology Area, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.,Agencia Española de Protección de la Salud en el Deporte (AEPSAD), Madrid. Spain
| | | | - Luisa Pérez-Álvarez
- Department of Cardiology, Hospital Hospital Universitario de A Coruña, La Coruña, Spain
| | - Javier Alzueta
- Department of Cardiology, Hospital Universitario Virgen de la Victoria, Málaga, Spain
| | - Juan Gabriel Martínez
- Department of Cardiology, Hospital General Universitario de Alicante, ISABIAL-FISABIO, Alicante, Spain
| | - Aníbal Rodríguez
- Department of Cardiology, Hospital Universitario de Canarias, Santa Cruz de Tenerife, Spain
| | | | - Ignasi Anguera
- Department of Cardiology, Hospital Universitario de Bellvitge, Barcelona, Spain
| | - Xavier Viñolas
- Department of Cardiology, Hospital Santa Creu i san Pau, Barcelona, Spain
| | | | - Jorge G Quintanilla
- Advanced Development in Arrhythmia Mechanisms and Therapy Laboratory, Myocardial Pathophysiology Area, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.,Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC). Cardiovascular Institute, Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - José Manuel Alfonso-Almazán
- Advanced Development in Arrhythmia Mechanisms and Therapy Laboratory, Myocardial Pathophysiology Area, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Javier García
- Department of Cardiology, Hospital Universitario de Getafe, Madrid, Spain
| | - Luis Borrego
- Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC). Cardiovascular Institute, Madrid, Spain
| | - Victoria Cañadas-Godoy
- Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC). Cardiovascular Institute, Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Nicasio Pérez-Castellano
- Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC). Cardiovascular Institute, Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Julián Pérez-Villacastín
- Fundación Interhospitalaria para la Investigación Cardiovascular (FIC), Madrid, Spain.,Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC). Cardiovascular Institute, Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Javier Jiménez-Díaz
- Department of Cardiology, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain
| | - José Jalife
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain.,Cardiac Arrhythmia Laboratory, Myocardial Pathophysiology Area, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - David Filgueiras-Rama
- Advanced Development in Arrhythmia Mechanisms and Therapy Laboratory, Myocardial Pathophysiology Area, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.,Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC). Cardiovascular Institute, Madrid, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
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Pérez-Villacastín J, Pérez Castellano N, Moreno Planas J. Epidemiology of atrial fibrillation in Spain in the past 20 years. REVISTA ESPANOLA DE CARDIOLOGIA (ENGLISH ED.) 2013; 66:561-5. [PMID: 24776206 DOI: 10.1016/j.rec.2013.02.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 02/04/2013] [Indexed: 11/30/2022]
Abstract
Atrial fibrillation is the most common sustained arrhythmia. Because of its potentially serious clinical consequences (heart failure, stroke, and cognitive impairment), atrial fibrillation has important socioeconomic and health implications. This article reviews the major studies on the epidemiology of atrial fibrillation in Spain. Recent data suggest that in people older than 40 years, the prevalence of atrial fibrillation may be more than 4%. Given the current Spanish demography, these data would imply that more than 1 million people in Spain have atrial fibrillation.
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Staniczenko PPA, Lee CF, Jones NS. Rapidly detecting disorder in rhythmic biological signals: a spectral entropy measure to identify cardiac arrhythmias. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2009; 79:011915. [PMID: 19257077 DOI: 10.1103/physreve.79.011915] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2008] [Revised: 11/14/2008] [Indexed: 05/27/2023]
Abstract
We consider the use of a running measure of power spectrum disorder to distinguish between the normal sinus rhythm of the heart and two forms of cardiac arrhythmia: atrial fibrillation and atrial flutter. This spectral entropy measure is motivated by characteristic differences in the power spectra of beat timings during the three rhythms. We plot patient data derived from ten-beat windows on a "disorder map" and identify rhythm-defining ranges in the level and variance of spectral entropy values. Employing the spectral entropy within an automatic arrhythmia detection algorithm enables the classification of periods of atrial fibrillation from the time series of patients' beats. When the algorithm is set to identify abnormal rhythms within 6 s, it agrees with 85.7% of the annotations of professional rhythm assessors; for a response time of 30 s, this becomes 89.5%, and with 60 s, it is 90.3%. The algorithm provides a rapid way to detect atrial fibrillation, demonstrating usable response times as low as 6s. Measures of disorder in the frequency domain have practical significance in a range of biological signals: the techniques described in this paper have potential application for the rapid identification of disorder in other rhythmic signals.
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Affiliation(s)
- Phillip P A Staniczenko
- Physics Department, Clarendon Laboratory, CABDyN Complexity Centre, Oxford University, Oxford OX1 1HP, United Kingdom
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