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Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization: The LINK-HF Multicenter Study
Abstract
BACKGROUND: Implantable cardiac sensors have shown promise in reducing rehospitalization for heart failure (HF), but the efficacy of noninvasive approaches has not been determined. The objective of this study was to determine the accuracy of noninvasive remote monitoring in predicting HF rehospitalization. METHODS: The LINK-HF study (Multisensor Non-invasive Remote Monitoring for Prediction of Heart Failure Exacerbation) examined the performance of a personalized analytical platform using continuous data streams to predict rehospitalization after HF admission. Study subjects were monitored for up to 3 months using a disposable multisensor patch placed on the chest that recorded physiological data. Data were uploaded continuously via smartphone to a cloud analytics platform. Machine learning was used to design a prognostic algorithm to detect HF exacerbation. Clinical events were formally adjudicated. RESULTS: One hundred subjects aged 68.4±10.2 years (98% male) were enrolled. After discharge, the analytical platform derived a personalized baseline model of expected physiological values. Differences between baseline model estimated vital signs and actual monitored values were used to trigger a clinical alert. There were 35 unplanned nontrauma hospitalization events, including 24 worsening HF events. The platform was able to detect precursors of hospitalization for HF exacerbation with 76% to 88% sensitivity and 85% specificity. Median time between initial alert and readmission was 6.5 (4.2-13.7) days. CONCLUSIONS: Multivariate physiological telemetry from a wearable sensor can provide accurate early detection of impending rehospitalization with a predictive accuracy comparable to implanted devices. The clinical efficacy and generalizability of this low-cost noninvasive approach to rehospitalization mitigation should be further tested. Registration: URL: https://www.clinicaltrials.gov. Unique Identifier: NCT03037710.
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“Multiparametric monitoring may be enabled with a patch worn for up to 3 months (Stehlik 2020).”
From 2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society (2021) · cites this paper as Stehlik 2020
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“The platform was able to successfully predict hospitalization for HF exacerbation with 76%-88% sensitivity and 85% specificity.”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [36]
Citations
Cites
Forecasting the Impact of Heart Failure in the United States
Heidenreich, P. A., Albert, N. M., Allen, L. A., Bluemke, D. A., Butler, J., Fonarow, G. C., Ikonomidis, J. S., Khavjou, O., Konstam, M. A., Maddox, T. M., et al. (2013) · Circulation Heart Failure · doi:10.1161/hhf.0b013e318291329a · Cited by 2,884
Self-citationAbraham, W. T., Adamson, P. B., Bourge, R. C., Aaron, M. F., Costanzo, M. R., Stevenson, L. W., Strickland, W., Neelagaru, S., Raval, N., Krueger, S., et al. (2011) · The Lancet · doi:10.1016/s0140-6736(11)60101-3 · Cited by 1,744
Telemonitoring in patients with heart failure
Chaudhry, S. I., Mattera, J. A., Curtis, J. P., Spertus, J. A., Herrin, J., Lin, Z., Phillips, C. O., Hodshon, B. V., Cooper, L. S., & Krumholz, H. M. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1010029 · Cited by 1,232
Artificial intelligence in cardiology
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202
Intrathoracic Impedance Monitoring in Patients With Heart Failure
Yu, C., Wang, L., Chau, E. M. C., Chan, R. H.-W., Kong, S., TANG, M., Christensen, J., Stadler, R. W., & Lau, C. (2005) · Circulation · doi:10.1161/circulationaha.104.492207 · Cited by 708
Solomon, S. D., Dobson, J., Pocock, S., Skali, H., McMurray, J. J. V., Granger, C. B., Yusuf, S., Swedberg, K., Young, J. B., Michelson, E. L., et al. (2007) · Circulation · doi:10.1161/circulationaha.107.696906 · Cited by 681
Abraham, W. T., Stevenson, L. W., Bourge, R. C., Lindenfeld, J. A., Bauman, J. G., & Adamson, P. B. (2015) · The Lancet · doi:10.1016/s0140-6736(15)00723-0 · Cited by 621
van Veldhuisen, D. J., Braunschweig, F., Conraads, V. M., Ford, I., Cowie, M., Jondeau, G., Kautzner, J., Aguilera, R. M., Lunati, M., Yu, C., et al. (2011) · Circulation · doi:10.1161/circulationaha.111.043042 · Cited by 443
Gupta, A., Allen, L. A., Bhatt, D. L., Cox, M., DeVore, A. D., Heidenreich, P. A., Hernandez, A. F., Peterson, E. D., Matsouaka, R., Yancy, C. W., et al. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2017.4265 · Cited by 427
A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices
BOEHMER, J. P., Hariharan, R., Devecchi, F., Smith, A. L., Molon, G., Capucci, A., An, Q., Averina, V. A., Stolen, C., Thakur, P. H., et al. (2017) · JACC Heart Failure · doi:10.1016/j.jchf.2016.12.011 · Cited by 400
Research works printed but not linked: 1
- Mott J Pipke M. Similarity-based modeling of aircraft flight paths. Paper presented at 2004 IEEE Aerospace Conference Proceedings (IEEE Cat No04TH8720). March 6-13 2004 Big Sky MT USA. 2004;3:1580–1593.
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Self-citationArmoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., et al. (2024) · Circulation · doi:10.1161/cir.0000000000001201 · Cited by 347
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Krittanawong, C., Rogers, A. J., Johnson, K. W., Wang, Z., Turakhia, M. P., Halperin, J. L., & Narayan, S. M. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00445-9 · Cited by 248
Huang, J.-D., Wang, J., Ramsey, E., Leavey, G., Chico, T., & Condell, J. V. (2022) · Sensors · doi:10.3390/s22208002 · Cited by 203
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Yasmin, F., Shah, S. M. I., Naeem, A., Shujauddin, S. M., Jabeen, A., Kazmi, S., Siddiqui, S. A., Kumar, P., Salman, S., Hassan, S. A., et al. (2021) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2204121 · Cited by 178
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Leclercq, C., Witt, H., Hindricks, G., Katra, R. P., Albert, D., Belliger, A., Cowie, M. R., Deneke, T., Friedman, P., Haschemi, M., et al. (2022) · EP Europace · doi:10.1093/europace/euac052 · Cited by 109
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Romiti, S., Vinciguerra, M., Saade, W., Cortajarena, I. A., & Greco, E. (2020) · Cardiology Research and Practice · doi:10.1155/2020/4972346 · Cited by 109
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Alvarez, P. A., Sianis, A., Brown, J., Ali, A., & Briasoulis, Α. (2021) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2202046 · Cited by 106
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Khan, M. S., Arshad, M. S., Greene, S. J., Spall, H. G. C. V., Pandey, A., Vemulapalli, S., Perakslis, E. D., & Butler, J. (2023) · European Journal of Heart Failure · doi:10.1002/ejhf.2994 · Cited by 109
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Leroux, A., Lynn, R. R., Crainiceanu, C., & Sharma, T. (2021) · Digital Biomarkers · doi:10.1159/000515576 · Cited by 94
Gautam, N., Ghanta, S. N., Mueller, J. M., Mansour, M. M., Chen, Z., Puente, C., Ha, Y., Tarun, T., Dhar, G., Sivakumar, K., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12122964 · Cited by 88
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Singhal, A., & Cowie, M. R. (2020) · Current Heart Failure Reports · doi:10.1007/s11897-020-00467-x · Cited by 85
Kitsiou, S., Vatani, H., Paré, G., Gerber, B. S., Buchholz, S. W., Kansal, M. M., Leigh, J. W., & Creber, R. M. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.02.015 · Cited by 83
Lugogo, N. L., DePietro, M. R., Reich, M., Merchant, R. K., Chrystyn, H., Pleasants, R. A., Granovsky, L., Li, T., Hill, T. D., Brown, R. W., et al. (2022) · Journal of Asthma and Allergy · doi:10.2147/jaa.s377631 · Cited by 75
Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbuchel, H., Hu, Y.-F., Chen, L. Y., Couderc, J.-P., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Circulation: Arrhythmia and Electrophysiology · doi:10.1161/circep.120.009204 · Cited by 74
Sunny, J. S., Patro, C. P. K., Karnani, K., Pingle, S. C., Lin, F., Anekoji, M., Jones, L., Kesari, S., & Ashili, S. (2022) · Sensors · doi:10.3390/s22030756 · Cited by 77
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Lathauwer, I. L. J. D., Nieuwenhuys, W. W., Hafkamp, F. J., Regis, M., Brouwers, R. W. M., Funk, M., & Kemps, H. (2025) · European Journal of Heart Failure · doi:10.1002/ejhf.3568 · Cited by 65
Moradi, H., Al‐Hourani, A., Concilia, G., Khoshmanesh, F., Nezami, F. R., Needham, S., Baratchi, S., & Khoshmanesh, K. (2023) · Biophysical Reviews · doi:10.1007/s12551-022-01040-7 · Cited by 66
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Hirten, R. P., Danieletto, M., Scheel, R., Shervey, M., Ji, J., Hu, L., Sauk, J., Chang, L., Arnrich, B., Bӧttinger, E., et al. (2021) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa323 · Cited by 60
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Gautam, N., Ghanta, S. N., Clausen, A., Saluja, P., Sivakumar, K., Dhar, G., Qi, C., DeMazumder, D., Rabbat, M., Greene, S. J., et al. (2022) · JACC Heart Failure · doi:10.1016/j.jchf.2022.06.011 · Cited by 25
Use of a Cardiac Scale to Predict Heart Failure Events: Design of SCALE-HF 1
Fudim, M., Yazdi, D., Egolum, U. O., Haghighat, A., Kottam, A. R., Sauer, A. J., Shah, H., Kumar, P., Rakita, V., Centen, C., et al. (2023) · Circulation Heart Failure · doi:10.1161/circheartfailure.122.010012 · Cited by 25
Wearable devices and cardiovascular health: revolutionizing remote monitoring and disease prevention
Hughes, A. M., Taylor, D. J., Morris, P. D., & Brittain, E. L. (2026) · European Heart Journal · doi:10.1093/eurheartj/ehag189 · Cited by 23
Big Data Approaches in Heart Failure Research
Lanzer, J. D., Leuschner, F., Kramann, R., Levinson, R. T., & Sáez-Rodríguez, J. (2020) · Current Heart Failure Reports · doi:10.1007/s11897-020-00469-9 · Cited by 23
Scheid, M. R., Friedmann, B., Oppenheim, M. I., Hirsch, J. S., & Zanos, T. P. (2025) · Nature Communications · doi:10.1038/s41467-025-65219-8 · Cited by 22
Algorithm for Mobile Platform-Based Real-Time QRS Detection
Neri, L., Oberdier, M. T., Augello, A., Suzuki, M., Tumarkin, E., Jaipalli, S., Geminiani, G. A., Halperin, H. R., & Borghi, C. (2023) · Sensors · doi:10.3390/s23031625 · Cited by 22
Multiomics, Virtual Reality and Artificial Intelligence in Heart Failure
Gladding, P. A., Loader, S., Smith, K. K., Zarate, E., Green, S., Villas-Bôas, S. G., Shepherd, P., Kakadia, P. M., Hewitt, W., Thorstensen, E. B., et al. (2021) · Future Cardiology · doi:10.2217/fca-2020-0225 · Cited by 23
Telemedicine in Surgery—Beyond a Pandemic Adaptation
Ghomrawi, H. M. K., Holl, J. L., & Abdullah, F. (2021) · JAMA Surgery · doi:10.1001/jamasurg.2021.2052 · Cited by 21
Artificial intelligence in cardiology: fundamentals and applications
Watson, X., D'Souza, J., Cooper, D., & Markham, R. (2021) · Internal Medicine Journal · doi:10.1111/imj.15562 · Cited by 21
Artificial intelligence and digital health for volume maintenance in hemodialysis patients
Sandys, V. K., Sexton, D. J., & O’Seaghdha, C. M. (2022) · Hemodialysis International · doi:10.1111/hdi.13033 · Cited by 21
Brown, K., Mathenge, N., Crousillat, D. R., Pagliaro, J. A., Grady, C. B., Katz, N., Singh, J. P., & Bhatt, A. B. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab039 · Cited by 21
Continuous Remote Patient Monitoring: Evaluation of the Heart Failure Cascade Soft Launch
Chi, W. N., Reamer, C. B., Gordon, R. A., Sarswat, N., Gupta, C., VanGompel, E. W., Dayiantis, J., Morton-Jost, M., Ravichandran, U., Larimer, K. A., et al. (2021) · Applied Clinical Informatics · doi:10.1055/s-0041-1740480 · Cited by 20
Lala, A., Beavers, C. J., Blumer, V. L., Brewer, L. C., Oliveira-Gomes, D. D., Dunbar, S. B., Every, H., Ferraro, R., Ky, B., Januzzi, J. L., et al. (2025) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2025.06.013 · Cited by 26
Sideris, K., Weir, C. R., Schmalfuss, C. M., Hanson, H., Pipke, M., Tseng, P.-H., Lewis, N. P., Sallam, K., Bozkurt, B., Hanff, T. C., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae017 · Cited by 20
Self-citationTandon, A. A., Silva, J. N. A., Bhatt, A. B., Drummond, C. K., Hill, A. C., Paluch, A. E., Waits, S., Zablah, J. E., & Harris, K. C. (2024) · Circulation · doi:10.1161/cir.0000000000001225 · Cited by 20
Hemodynamic Determinants of Activity Measured by Accelerometer in Patients With Stable Heart Failure
Omar, M., Jensen, J., Frederiksen, P. H., Videbæk, L. M., Poulsen, M. K., Brønd, J. C., Gustafsson, F., Borlaug, B. A., Schou, M., & Møller, J. E. (2021) · JACC Heart Failure · doi:10.1016/j.jchf.2021.05.013 · Cited by 20
Lala, A., Beavers, C. J., Blumer, V. L., Brewer, L. C., Oliveira-Gomes, D. D., Dunbar, S. B., Every, H., Ferraro, R., Ky, B., Januzzi, J. L., et al. (2025) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2025.101069 · Cited by 19
Senarath, S., Fernie, G. R., & Fekr, A. R. (2021) · Sensors · doi:10.3390/s21113575 · Cited by 19
Wong, C., Un, K., Zhou, M., Cheng, Y., Lau, Y.-M., Shea, P. C., Lui, H. W., Ming Liang, Z. U. O., Yin, L. X., Chan, E. W., et al. (2022) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztac024 · Cited by 19
Kerexeta, J., Larburu, N., Escolar, V., Bahamonde, A. L., Macía, I., Beristain, A., & Graña, M. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10020048 · Cited by 19
Fudim, M., Egolum, U. O., Haghighat, A., Kottam, A. R., Sauer, A. J., Shah, H., Kumar, P., Rakita, V., Lópes, R. D., Centen, C., et al. (2024) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2024.08.050 · Cited by 18
Kanwar, M. K., Kilic, A., & Mehra, M. R. (2021) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2021.02.016 · Cited by 18
Jacobsen, M., Gholamipoor, R., Dembek, T. A., Rottmann, P., Verket, M., Brandts, J., Jäger, P. S., Baermann, B., Kondakci, M., Heinemann, L., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00847-2 · Cited by 21
Virtual healthcare solutions in heart failure: a literature review
Lee, K. C.-S., Breznen, B., Ukhova, A., Martin, S. S., & Koehler, F. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1231000 · Cited by 18
Smartphone-Based Recognition of Heart Failure by Means of Microelectromechanical Sensors
Haddad, F., Saraste, A., Santalahti, K., Pänkäälä, M., Kaisti, M., Kandolin, R. M., Simonen, P. P., Nammas, W., Dehkordi, K. J., Koivisto, T., et al. (2024) · JACC Heart Failure · doi:10.1016/j.jchf.2024.01.022 · Cited by 18
Krzesiński, P. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12062373 · Cited by 18
Dual-Chronoamperometry Drift Correction for Electrochemical Sensors
Riordan, K. T., Yang, K., Brazelton, E., Eslami, M., Copenhaver, A. E., Esmaeili, F., Flynn, C. D., Wu, Z., Isaacson, S. E., Chang, D., et al. (2025) · ACS Sensors · doi:10.1021/acssensors.5c02104 · Cited by 17
Metadata
- Authors
- Stehlik, J., Schmalfuss, C., Bozkurt, B., Nativi-Nicolau, J., Wohlfahrt, P., Wegerich, S., Rose, K., Ray, R., Schofield, R., Deswal, A., Sekaric, J., Anand, S., Richards, D., Hanson, H., Pipke, M., & Pham, M.
- Affiliations
- Michael E. DeBakey VA Medical Center (US)
- Malcom Randall VA Medical Center (US)
- University of Utah (US)
- PhysIQ Inc, Chicago, IL (S.W., K.R., J.S., S.A., D.R., M.P.).
- VA Palo Alto Health Care System (US)
- George E. Wahlen Department of VA Medical Center (US)
- Corresponding author
- Stehlik, Josef
- ORCID
- Venue
- Circulation Heart Failure
- Published
- 2020-03
- Volume
- 13
- Issue
- 3
- Pages
- e006513-e006513
- ISSN
- 1941-3289 (ISSN-L)
- 1941-3297
- OpenAlex
- W3007492756
- Cited by
- 333
- References
- 19
- Citations by year
- 2026: 59 · 2025: 70 · 2024: 58 · 2023: 52 · 2022: 38 · 2021: 41 · 2020: 15
- FWCI
- 22.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Circulation Heart Failure (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- heart failure, hospital readmission prediction, remote patient monitoring, wearable sensors, clinical alerts
- MeSH terms
- Cloud Computing, Smartphone, Machine Learning, Wearable Electronic Devices, Aged, Aged, 80 and over, Diagnosis, Computer-Assisted, Equipment Design, Female, Heart Failure, Humans, Male, Middle Aged, Patient Readmission, Predictive Value of Tests, Prognosis, Risk Factors, Telemetry, Time Factors, United States, Reproducibility of Results, Risk Assessment
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