Federica Censi1, Giovanni Calcagnini2, Eugenio Mattei3, Leonardo Calò4, Antonio Curnis5, Antonio D'Onofrio6, Diego Vaccari7, Gabriele Zanotto8, Loredana Morichelli9, Nicola Rovai10, Alessio Gargaro11, Renato Pietro Ricci12. 1. Department of Cardiovascular, Dysmetabolic and Aging-associated Diseases, Rome, Italy. Electronic address: federica.censi@iss.it. 2. Department of Cardiovascular, Dysmetabolic and Aging-associated Diseases, Rome, Italy. Electronic address: giovanni.calcagnini@iss.it. 3. Department of Cardiovascular, Dysmetabolic and Aging-associated Diseases, Rome, Italy. Electronic address: eugenio.mattei@iss.it. 4. Department of Cardiology, Casilino Hospital, Via Casilina 1049, 00169 Rome, Italy. Electronic address: leonardo.calo@tin.it. 5. Electrophysiology, Spedali Civili, P.le Spedali Civili 1, 25123 Brescia, Italy. Electronic address: antonio.curnis@libero.it. 6. UOSD Electrophysiology, Vincenzo Monaldi Hospital, Via L. Bianchi, 80131 Naples, Italy. Electronic address: donofrioant1@gmail.com. 7. Department of Cardiology, Civil Hospital, Via Togliatti 1, 31044 Montebelluna, Italy. Electronic address: vaccaridiego@gmail.com. 8. UOC Cardiology, Mater Salutis Hospital, Via Gianella 1, 37045 Legnago, Italy. Electronic address: gabzanot@tin.it. 9. Department of Cardiovascular Diseases, San Filippo Neri Hospital, Rome, Italy. Electronic address: lmorichelli@yahoo.it. 10. Clinical Office, Biotronik Italia S.p.a., V.le delle Industrie 11, 20900 Vimodrone, MI, Italy. Electronic address: nicola.rovai@biotronik.com. 11. Clinical Office, Biotronik Italia S.p.a., V.le delle Industrie 11, 20900 Vimodrone, MI, Italy. Electronic address: alessio.gargaro@biotronik.com. 12. Department of Cardiovascular Diseases, San Filippo Neri Hospital, Rome, Italy. Electronic address: renatopietroricci@tin.it.
Abstract
BACKGROUND: Remote monitoring (RM) of cardiac implantable electronic devices is an ideal experimental model to evaluate long-term trends of physiological and clinical data automatically collected from large patient cohorts. OBJECTIVES: We studied data of atrial fibrillation (AF) and physical activity (PA) transmitted daily during 3.5years from a subgroup of patients enrolled in the HomeGuide trial, a previously conducted study on patients routinely followed with a RM system transmitting clinical and diagnostic data daily. METHODS: We selected 988 patients (80% male, mean age 68±11) implanted with a pacemaker (16%) or an implantable defibrillator and provided with atrial sensing and movement sensors. Remotely transmitted data were processed in order to obtain AF incidence and time of PA in the form of collective time series daily sampled. RESULTS: We found that both PA and AF incidence clearly showed seasonal trends with an annual period and inverse correlation. In a first-order autoregressive model the regression coefficient of daily activity to AF incidence was -0.64 (standard error, 0.18, p<0.0001), while the cross-correlation coefficient reached its maximum values at ±180day lags. AF incidence was 14.4% higher and PA was 14.7% lower in winters than in summers (p<0.0001 for both comparisons). Power spectral analysis revealed weekly periodicity in the PA series (corresponding to festivity rest) but not in the AF incidence. CONCLUSIONS: RM data collected daily from a relatively large patient cohort revealed marked seasonal trends in AF incidence and PA with opposite behavior in winters and summers.
BACKGROUND: Remote monitoring (RM) of cardiac implantable electronic devices is an ideal experimental model to evaluate long-term trends of physiological and clinical data automatically collected from large patient cohorts. OBJECTIVES: We studied data of atrial fibrillation (AF) and physical activity (PA) transmitted daily during 3.5years from a subgroup of patients enrolled in the HomeGuide trial, a previously conducted study on patients routinely followed with a RM system transmitting clinical and diagnostic data daily. METHODS: We selected 988 patients (80% male, mean age 68±11) implanted with a pacemaker (16%) or an implantable defibrillator and provided with atrial sensing and movement sensors. Remotely transmitted data were processed in order to obtain AF incidence and time of PA in the form of collective time series daily sampled. RESULTS: We found that both PA and AF incidence clearly showed seasonal trends with an annual period and inverse correlation. In a first-order autoregressive model the regression coefficient of daily activity to AF incidence was -0.64 (standard error, 0.18, p<0.0001), while the cross-correlation coefficient reached its maximum values at ±180day lags. AF incidence was 14.4% higher and PA was 14.7% lower in winters than in summers (p<0.0001 for both comparisons). Power spectral analysis revealed weekly periodicity in the PA series (corresponding to festivity rest) but not in the AF incidence. CONCLUSIONS: RM data collected daily from a relatively large patient cohort revealed marked seasonal trends in AF incidence and PA with opposite behavior in winters and summers.
Authors: Lucas Marzec; Sridharan Raghavan; Farnoush Banaei-Kashani; Seth Creasy; Edward L Melanson; Leslie Lange; Debashis Ghosh; Michael A Rosenberg Journal: PLoS One Date: 2018-10-29 Impact factor: 3.240
Authors: Philipp Bücke; Hans Henkes; Guy Arnold; Birgit Herting; Eric Jüttler; Christof Klötzsch; Alfred Lindner; Uwe Mauz; Ludwig Niehaus; Matthias Reinhard; Stefan Waibel; Thomas Horvath; Hansjörg Bäzner; Marta Aguilar Pérez Journal: Eur J Neurol Date: 2021-05-05 Impact factor: 6.288