Remote monitoring for the early detection of changes in patient status using the Home Monitoring technology
https://doi.org/10.35336/VA-2020-E-3-9
Abstract
Aims: To perform the analysis of adverse events (AE) rate and trends of physiologically meaningful parameters in patients with cardiac implantable electronic devices (CIEDs) with the mobile remote monitoring option.
Methods: In 9 clinical centers of the Russian Federation and 2 clinical centers of the Republic of Kazakhstan, 126 patients with an implantable cardioverter-defibrillator (ICD) or a pacemaker (PM) equipped with the Home Monitoring (HM) technology (BIOTRONIK, Berlin, Germany) were enrolled. Based on the daily data transmission, all alarm alerts, all HM options changes and all AE were recorded with dated alert content and undertaken measures.
Results: The study patients, followed up at least for one year, experienced 42 adverse events (AE), of which 26 were serious AE (SAE) and 3 SAE were defined as device-related (SAED). ICD patients (N=90) with concomitant coronary artery disease (CAD) had a statistically significantly higher SAE prevalence (p=0.0249). Patients with CRT-D had a lower SAE rate than patients with dual- or single-chamber ICD (р=0.046). Downloads of Home Monitoring parameters for retrospective mathematical analysis were available for 60 ICD patients, of which 47 had episodes of ventricular tachycardia (VT), ventricular fibrillation (VF) and/or atrial tachyarrhythmia (AT). Machine learning analysis of the trends of the physiologically meaningful parameters revealed correlations between changes and arrhythmia episodes, with the random forest and gradient boosting methods demonstrating the random effect of the results.
Conclusion: Home Monitoring of CIED patients enables the evaluation of different devices applications and their clinical advantages. This might implement the prevention of adverse events and iatrogenic effects of pacing. Based on daily transmission of physiologically meaningful Home Monitoring parameters, the study results demonstrate the feasibility of developing a prediction algorithm for adverse events.
About the Authors
A. Sh. Revishvili
Visnevsky National Medical Research Center of Surgery, Russian Ministry of Healthcare
Russian Federation
Moscow
Russian Federation
Moscow
N. N. Lomidze
Visnevsky National Medical Research Center of Surgery, Russian Ministry of Healthcare
Russian Federation
Moscow
Russian Federation
Moscow
A. S. Abdrakhmanov
National Scientific Cardiosurgical Center
Kazakhstan
Nur-Sultan
Kazakhstan
Nur-Sultan
A. A. Nechepurenko
Federal Center of Cardiovascular Surgery
Russian Federation
Astrakhan
Russian Federation
Astrakhan
E. A. Ivanitsky
Federal Center of Cardiovascular Surgery
Russian Federation
Krasnoyarsk
Russian Federation
Krasnoyarsk
O. V. Belyaev
Sverdlovsk Regional Clinical PsychoneurologicalHospital for War Veterans
Russian Federation
Ekaterinburg
Russian Federation
Ekaterinburg
S. V. Popov
Scientific Research Institute of Cardiology; National Research Medical Center of Russian Academy of Sciences
Russian Federation
Tomsk
Russian Federation
Tomsk
D. S. Lebedev
Almazov National Medical Research Center, Russian Ministry of Healthcare
Russian Federation
Saint Petersburg
Russian Federation
Saint Petersburg
V. K. Lebedeva
Almazov National Medical Research Center, Russian Ministry of Healthcare
Russian Federation
Saint Petersburg
Russian Federation
Saint Petersburg
S. P. Mikhailov
Sverdlovsk Regional Clinical Hospital #1
Russian Federation
Ekaterinburg
Russian Federation
Ekaterinburg
E. A. Pokushalov
Meshalkin National Medical Research Center, Russian Ministry of Healthcare
Russian Federation
Novosibirsk
Russian Federation
Novosibirsk
S. E. Mamchur
Scientific Research Institute for Complex Problems of Cardiovascular Diseases of Siberian Branch of Russian Academy of Sciences
Russian Federation
Kemerovo
Russian Federation
Kemerovo
P. L. Shugaev
Federal Center of Cardiovascular Surgery
Russian Federation
Chelyabinsk
Russian Federation
Chelyabinsk
R. R. Rekvava
Scientific Research Institute of Cardiology and Internal Diseases, Ministry of Healthcare
Kazakhstan
Almaty
Kazakhstan
Almaty
S. N. Vasilyev
Institute of Mathematics and Mechanics, Ural Branch of Russian Academy of Sciences
Russian Federation
Ekaterinburg
Russian Federation
Ekaterinburg
V. V. Kuptsov
Visnevsky National Medical Research Center of Surgery, Russian Ministry of Healthcare
Russian Federation
Moscow
Russian Federation
Moscow
V. I. Berdyshev
Institute of Mathematics and Mechanics, Ural Branch of Russian Academy of Sciences
Russian Federation
Ekaterinburg
Russian Federation
Ekaterinburg
R. Sh. Sungatov
Dicom Consulting LLC
Russian Federation
Kazan
Russian Federation
Kazan
I. Sh. Khassanov
BIOTRONIK; Max Schaldach-Stiftungsprofessur für Biomedizinische Technik, Friedrich-Alexander University Erlangen-Nuremberg
Germany
Germany
Berlin
Erlangen
Review
For citations:
Revishvili A.Sh., Lomidze N.N., Abdrakhmanov A.S., Nechepurenko A.A., Ivanitsky E.A., Belyaev O.V., Popov S.V., Lebedev D.S., Lebedeva V.K., Mikhailov S.P., Pokushalov E.A., Mamchur S.E., Shugaev P.L., Rekvava R.R., Vasilyev S.N., Kuptsov V.V., Berdyshev V.I., Sungatov R.Sh., Khassanov I.Sh. Remote monitoring for the early detection of changes in patient status using the Home Monitoring technology. Journal of Arrhythmology. 2020;27:3-9. https://doi.org/10.35336/VA-2020-E-3-9
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