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Cited by 341Heart Failure Treatment and ManagementNon-Invasive Vital Sign MonitoringArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization: The LINK-HF Multicenter Study

Stehlik, Josef, Schmalfuss, Carsten, Bozkurt, Biykem, Nativi-Nicolau, Jose, Wohlfahrt, Peter, Wegerich, Stephan, Rose, Kevin, Ray, Ranjan, Schofield, Richard, Deswal, Anita, Sekaric, Jadranka, Anand, Sebastian, Richards, Dylan, Hanson, Heather, Pipke, Matthew, Pham, Michael·2020·Circulation Heart Failure
DOI 10.1161/circheartfailure.119.006513
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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.

Overview

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Crossref · 10.1161/circheartfailure.119.006513

Cites

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Research works printed but not linked: 1

  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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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
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2020-03
Volume
13
Issue
3
Pages
e006513-e006513
ISSN
  • 1941-3289 (ISSN-L)
  • 1941-3297
DOI
10.1161/circheartfailure.119.006513
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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