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.
“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
The platform was able to successfully predict hospitalization for HF exacerbation with 76%-88% sensitivity and 85% specificity.
“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]
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- 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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