This paper is in the following e-collection/theme issue:

Tools, Programs and Algorithms (275) Digital Health, Telehealth and e-Innovation in Clinical Settings (421) Telehealth and Telemonitoring (1713) Clinical Communication, Electronic Consultation and Telehealth (615)

Published on in Vol 4, No 1 (2016): Jan-Mar

Early Indication of Decompensated Heart Failure in Patients on Home-Telemonitoring: A Comparison of Prediction Algorithms Based on Daily Weight and Noninvasive Transthoracic Bio-impedance

Early Indication of Decompensated Heart Failure in Patients on Home-Telemonitoring: A Comparison of Prediction Algorithms Based on Daily Weight and Noninvasive Transthoracic Bio-impedance

Early Indication of Decompensated Heart Failure in Patients on Home-Telemonitoring: A Comparison of Prediction Algorithms Based on Daily Weight and Noninvasive Transthoracic Bio-impedance

Authors of this article:

Illapha Cuba Gyllensten1, 2 Author Orcid Image ;   Alberto G Bonomi1 Author Orcid Image ;   Kevin M Goode3 Author Orcid Image ;   Harald Reiter1 Author Orcid Image ;   Joerg Habetha1 Author Orcid Image ;   Oliver Amft2, 4 Author Orcid Image ;   John GF Cleland5 Author Orcid Image
Article Authors Cited by (33) Tweetations (1) Metrics

Journals

  1. Daluwatte C, Yaghouby F, Scully C. A framework to characterize the performance of early warning index alarm systems for patient monitoring. MethodsX 2019;6:1660 View
  2. Cuba Gyllensten I, Crundall-Goode A, Aarts R, Goode K. Simulated case management of home telemonitoring to assess the impact of different alert algorithms on work-load and clinical decisions. BMC Medical Informatics and Decision Making 2017;17(1) View
  3. Darling C, Dovancescu S, Saczynski J, Riistama J, Sert Kuniyoshi F, Rock J, Meyer T, McManus D. Bioimpedance-Based Heart Failure Deterioration Prediction Using a Prototype Fluid Accumulation Vest-Mobile Phone Dyad: An Observational Study. JMIR Cardio 2017;1(1):e1 View
  4. Cuba-Gyllensten I, Gastelurrutia P, Bonomi A, Riistama J, Bayes-Genis A, Aarts R. A method to adapt thoracic impedance based on chest geometry and composition to assess congestion in heart failure patients. Medical Engineering & Physics 2016;38(6):538 View
  5. Larburu N, Artetxe A, Escolar V, Lozano A, Kerexeta J. Artificial Intelligence to Prevent Mobile Heart Failure Patients Decompensation in Real Time: Monitoring-Based Predictive Model. Mobile Information Systems 2018;2018:1 View
  6. Papavasileiou L, Santini L, Forleo G, Ammirati F, Santini M. Novel devices to monitor heart failure and minimize hospitalizations. Expert Review of Cardiovascular Therapy 2016;14(8):905 View
  7. Saporito S, Dovancescu S, Herold I, van den Bosch H, van Assen H, Aarts R, Korsten H, Mischi M. Comparison of cardiac magnetic resonance imaging and bio-impedance spectroscopy for the assessment of fluid displacement induced by external leg compression. Physiological Measurement 2017;38(1):15 View
  8. Pevnick J, Birkeland K, Zimmer R, Elad Y, Kedan I. Wearable technology for cardiology: An update and framework for the future. Trends in Cardiovascular Medicine 2018;28(2):144 View
  9. Singhal A, Cowie M. The Role of Wearables in Heart Failure. Current Heart Failure Reports 2020;17(4):125 View
  10. Bhatia A, Maddox T. Remote Patient Monitoring in Heart Failure: Factors for Clinical Efficacy. International Journal of Heart Failure 2021;3(1):31 View
  11. Iqbal S, Mahgoub I, Du E, Leavitt M, Asghar W. Advances in healthcare wearable devices. npj Flexible Electronics 2021;5(1) View
  12. Groenendaal W, Lee S, van Hoof C. Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions. JMIR Biomedical Engineering 2021;6(2):e22911 View
  13. Senarath S, Fernie G, Roshan Fekr A. Influential Factors in Remote Monitoring of Heart Failure Patients: A Review of the Literature and Direction for Future Research. Sensors 2021;21(11):3575 View
  14. Albuquerque de Almeida F, Corro Ramos I, Rutten-van Mölken M, Al M. Modeling Early Warning Systems: Construction and Validation of a Discrete Event Simulation Model for Heart Failure. Value in Health 2021;24(10):1435 View
  15. Albuquerque de Almeida F, Corro Ramos I, Al M, Rutten-van Mölken M. Home Telemonitoring and a Diagnostic Algorithm in the Management of Heart Failure in the Netherlands: Cost-effectiveness Analysis. JMIR Cardio 2022;6(2):e31302 View
  16. Sandys V, Sexton D, O'Seaghdha C. Artificial intelligence and digital health for volume maintenance in hemodialysis patients. Hemodialysis International 2022;26(4):480 View
  17. La Porta E, Lanino L, Calatroni M, Caramella E, Avella A, Quinn C, Faragli A, Estienne L, Alogna A, Esposito P. Volume Balance in Chronic Kidney Disease: Evaluation Methodologies and Innovation Opportunities. Kidney and Blood Pressure Research 2021;46(4):396 View
  18. Amadou Boubacar H, Rahim M, Al-Hamoud G, Montesantos S, Delval C, Bothorel S, Ramirez-Gil J. HeartPredict algorithm: Machine intelligence for the early detection of heart failure. Intelligence-Based Medicine 2021;5:100044 View
  19. Bhatia A, Ewald G, Maddox T. The Novel Data Collection and Analytics Tools for Remote Patient Monitoring in Heart Failure (Nov-RPM-HF) Trial: Protocol for a Single-Center Prospective Trial. JMIR Research Protocols 2022;11(6):e32873 View
  20. Kumar S, Victoria-Castro A, Melchinger H, O’Connor K, Psotka M, Desai N, Ahmad T, Wilson F. Wearables in Cardiovascular Disease. Journal of Cardiovascular Translational Research 2023;16(3):557 View
  21. Iqbal S, Mahgoub I, Du E, Leavitt M, Asghar W. Development of a wearable belt with integrated sensors for measuring multiple physiological parameters related to heart failure. Scientific Reports 2022;12(1) View
  22. Joshi R, Gyllensten I. Changes in Daily Measures of Blood Pressure and Heart Rate Improve Weight-Based Detection of Heart Failure Deterioration in Patients on Telemonitoring. IEEE Journal of Biomedical and Health Informatics 2019;23(3):1041 View
  23. Williams G, Al-Baraikan A, Rademakers F, Ciravegna F, van de Vosse F, Lawrie A, Rothman A, Ashley E, Wilkins M, Lawford P, Omholt S, Wisløff U, Hose D, Chico T, Gunn J, Morris P. Wearable technology and the cardiovascular system: the future of patient assessment. The Lancet Digital Health 2023;5(7):e467 View
  24. Iqbal S, Leavitt M, Mahgoub I, Asghar W. A Wearable Internet of Things Device for Noninvasive Remote Monitoring of Vital Signs Related to Heart Failure. IoT 2024;5(1):155 View
  25. Curtain J, Talebi A, McIntosh A, McConnachie A, O'Donnell J, Welsh P, Osmanska J, Lee M, Sonecki P, Akl T, Seo J, Gopinathan V, Hurwitz J, Thiagarajan S, Pettit S, Kalra P, Patel R, Mark P, Lang N, McMurray J, Petrie M, Gardner R, Jhund P. Measuring congestion with a non‐invasive monitoring device in heart failure and haemodialysis: CONGEST‐HF. European Journal of Heart Failure 2024;26(6):1383 View
  26. Giménez-Miranda L, Scagliusi S, Pérez-García P, Olmo-Fernández A, Huertas G, Yúfera A, Medrano F. Wearable Devices Based on Bioimpedance Test in Heart Failure: Clinical Relevance: Systematic Review. Reviews in Cardiovascular Medicine 2024;25(9) View
  27. Odeh V, Chen Y, Wang W, Ding X. Recent Advances in the Wearable Devices for Monitoring and Management of Heart Failure. Reviews in Cardiovascular Medicine 2024;25(10) View
  28. Faragli A, Herrmann A, Cvetkovic M, Perna S, Khorsheed E, Lo Muzio F, La Porta E, Fassina L, Günther A, Oetvoes J, Düngen H, Alogna A. In-hospital bioimpedance-derived total body water predicts short-term cardiovascular mortality and re-hospitalizations in acute decompensated heart failure patients. Clinical Research in Cardiology 2024 View

Books/Policy Documents

  1. Bertemes-Filho P, Morcelles K. Medicine-Based Informatics and Engineering. View
  2. Mühlsteff J, ten Kate W, Bonomi A, Gyllensten I, de Carvalho P, Pielmus A, Orglmeister R. Personalized Health Systems for Cardiovascular Disease. View