Link-HF: Multisensor Non-invasive Telemonitoring System for Prediction of Heart Failure Exacerbation (LINK-HF)
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ClinicalTrials.gov Identifier: NCT03037710 |
Recruitment Status :
Completed
First Posted : January 31, 2017
Last Update Posted : August 5, 2020
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Tracking Information | ||||
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First Submitted Date | January 27, 2017 | |||
First Posted Date | January 31, 2017 | |||
Last Update Posted Date | August 5, 2020 | |||
Study Start Date | June 2015 | |||
Actual Primary Completion Date | January 26, 2017 (Final data collection date for primary outcome measure) | |||
Current Primary Outcome Measures |
Detection of Heart Failure Exacerbation Event [ Time Frame: 90 Days ] Correlation of algorithmic alerts generated by a non-invasive telemonitoring system to a verified heart failure exacerbation event, measured in percent accuracy
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Original Primary Outcome Measures | Same as current | |||
Change History | ||||
Current Secondary Outcome Measures | Not Provided | |||
Original Secondary Outcome Measures | Not Provided | |||
Current Other Pre-specified Outcome Measures | Not Provided | |||
Original Other Pre-specified Outcome Measures | Not Provided | |||
Descriptive Information | ||||
Brief Title | Link-HF: Multisensor Non-invasive Telemonitoring System for Prediction of Heart Failure Exacerbation | |||
Official Title | Link-HF: Multisensor Non-invasive Telemonitoring System for Prediction of Heart Failure Exacerbation | |||
Brief Summary | This is a multi-center, non-randomized, non-interventional study to evaluate the accuracy of a remote monitoring and analytical platform for prediction of heart failure exacerbation. The platform acquires continuous multivariate vital signs from HF patients using a new ambulatory wearable (attached by an adhesive) multi-sensor device and analyzes the data using a novel machine learning algorithm. | |||
Detailed Description | The analytics being investigated includes a Similarity-Based Modeling technique, that empirically estimates the expected physiological behavior of a subject based on prior learned dynamic data, for comparison to actual measured behavior from the subject, to reveal discrepancies hidden by normal variation. The measurements are typically an ensemble of vital signs that effectively characterizes the physiological "control system" of the subject. This technique is multivariate: multiple variables are leveraged, because single variables in isolation have little context - a high heart rate by itself could mean a person is exerting himself, or it could mean his physiology is in distress even though he is not exerting himself. With reference to several other variables, however, such as respiration rate, oximetry and motion/activity, a high heart rate might be recognized as a normal state when accompanied by the corroborating data showing a high respiration rate, a normal oximetry and a high level of motion - the person is exercising. A wearable adhesive multi-sensor device will be used to collect continuous vital sign and other data from study subjects, including heart rate, respiration rate, bodily motion/activity, skin temperature, pulse, electrocardiogram and peripheral capillary oxygen saturation. Subjects are provided with a smartphone or cellular tablet that will be paired with the multi-sensor device to receive data and upload it to the analytics server via cellular network or WiFi internet. Study staff will interact with the subject during visits scheduled for routine heart failure follow-up to capture pre-specified heart failure medical events. All standard of care clinic and hospitalization notes and procedure reports including echocardiograms, right heart catheterizations, pulmonary function tests, six minute walk tests and radiology reports will be collected as they occur. |
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Study Type | Observational | |||
Study Design | Observational Model: Other Time Perspective: Prospective |
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Target Follow-Up Duration | Not Provided | |||
Biospecimen | Not Provided | |||
Sampling Method | Non-Probability Sample | |||
Study Population | Individuals with heart failure, NYHA Class II-IV | |||
Condition | Cardiac Failure | |||
Intervention | Device: HealthPatch
A multi-sensor device to collect continuous vital signs
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Study Groups/Cohorts | Not Provided | |||
Publications * | 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. Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization: The LINK-HF Multicenter Study. Circ Heart Fail. 2020 Mar;13(3):e006513. doi: 10.1161/CIRCHEARTFAILURE.119.006513. Epub 2020 Feb 25. | |||
* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline. |
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Recruitment Information | ||||
Recruitment Status | Completed | |||
Actual Enrollment |
100 | |||
Original Actual Enrollment | Same as current | |||
Actual Study Completion Date | January 26, 2017 | |||
Actual Primary Completion Date | January 26, 2017 (Final data collection date for primary outcome measure) | |||
Eligibility Criteria | Inclusion Criteria:
Exclusion Criteria:
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Sex/Gender |
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Ages | 18 Years and older (Adult, Older Adult) | |||
Accepts Healthy Volunteers | No | |||
Contacts | Contact information is only displayed when the study is recruiting subjects | |||
Listed Location Countries | United States | |||
Removed Location Countries | ||||
Administrative Information | ||||
NCT Number | NCT03037710 | |||
Other Study ID Numbers | 81833 | |||
Has Data Monitoring Committee | No | |||
U.S. FDA-regulated Product | Not Provided | |||
IPD Sharing Statement |
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Responsible Party | Josef Stehlik, University of Utah | |||
Study Sponsor | Josef Stehlik | |||
Collaborators | Not Provided | |||
Investigators |
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PRS Account | University of Utah | |||
Verification Date | August 2020 |