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Data-driven monitoring in patients on left ventricular assist device support. | LitMetric

Data-driven monitoring in patients on left ventricular assist device support.

Expert Rev Med Devices

Department of Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands.

Published: September 2022

AI Article Synopsis

  • The increasing use of left ventricular assist devices (LVAD) among patients necessitates rigorous follow-up care, including regular hospital visits and self-management combined with telemonitoring.* -
  • The review discusses the operation of LVADs, the role of noninvasive and invasive monitoring technologies, and highlights the current challenges and future directions in telemonitoring for these patients.* -
  • Initial studies show promise for mobile applications to track clinical and pump data, potentially reducing hospital visits; however, more advanced algorithms are needed to detect complications early while ensuring data security and cost-effectiveness.*

Article Abstract

Introduction: Despite an increasing population of patients supported with a left ventricular assist device (LVAD), it remains a complex therapy, and patients are frequently admitted. Therefore, a strict follow-up including frequent hospital visits, patient self-management and telemonitoring is needed.

Areas Covered: The current review describes the principles of LVADs, the possibilities of (tele)monitoring using noninvasive and invasive devices. Furthermore, possibilities, challenges, and future perspectives in this emerging field are discussed.

Expert Opinion: Several studies described initial experiences on telemonitoring in LVAD patients, using mobile phone applications to collect clinical data and pump data. This may replace frequent hospital visits in near future. In addition, algorithms were developed aiming to early detect pump thrombosis or driveline infections. Since not all complications are reflected by pump parameters, data from different sources should be combined to detect a broader spectrum of complications in an early stage. We need to focus on the development of sophisticated but understandable algorithms and infrastructure combining different data sources, while addressing essential aspects such as data safety, privacy, and cost-effectiveness.

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Source
http://dx.doi.org/10.1080/17434440.2022.2132147DOI Listing

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