<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03037710</url>
  </required_header>
  <id_info>
    <org_study_id>81833</org_study_id>
    <nct_id>NCT03037710</nct_id>
  </id_info>
  <brief_title>Link-HF: Multisensor Non-invasive Telemonitoring System for Prediction of Heart Failure Exacerbation</brief_title>
  <acronym>LINK-HF</acronym>
  <official_title>Link-HF: Multisensor Non-invasive Telemonitoring System for Prediction of Heart Failure Exacerbation</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Josef Stehlik</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>University of Utah</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      This is a multi-center, non-randomized, non-interventional study to evaluate the accuracy of&#xD;
      a remote monitoring and analytical platform for prediction of heart failure exacerbation. The&#xD;
      platform acquires continuous multivariate vital signs from HF patients using a new ambulatory&#xD;
      wearable (attached by an adhesive) multi-sensor device and analyzes the data using a novel&#xD;
      machine learning algorithm.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The analytics being investigated includes a Similarity-Based Modeling technique, that&#xD;
      empirically estimates the expected physiological behavior of a subject based on prior learned&#xD;
      dynamic data, for comparison to actual measured behavior from the subject, to reveal&#xD;
      discrepancies hidden by normal variation. The measurements are typically an ensemble of vital&#xD;
      signs that effectively characterizes the physiological &quot;control system&quot; of the subject. This&#xD;
      technique is multivariate: multiple variables are leveraged, because single variables in&#xD;
      isolation have little context - a high heart rate by itself could mean a person is exerting&#xD;
      himself, or it could mean his physiology is in distress even though he is not exerting&#xD;
      himself. With reference to several other variables, however, such as respiration rate,&#xD;
      oximetry and motion/activity, a high heart rate might be recognized as a normal state when&#xD;
      accompanied by the corroborating data showing a high respiration rate, a normal oximetry and&#xD;
      a high level of motion - the person is exercising.&#xD;
&#xD;
      A wearable adhesive multi-sensor device will be used to collect continuous vital sign and&#xD;
      other data from study subjects, including heart rate, respiration rate, bodily&#xD;
      motion/activity, skin temperature, pulse, electrocardiogram and peripheral capillary oxygen&#xD;
      saturation. Subjects are provided with a smartphone or cellular tablet that will be paired&#xD;
      with the multi-sensor device to receive data and upload it to the analytics server via&#xD;
      cellular network or WiFi internet. Study staff will interact with the subject during visits&#xD;
      scheduled for routine heart failure follow-up to capture pre-specified heart failure medical&#xD;
      events. All standard of care clinic and hospitalization notes and procedure reports including&#xD;
      echocardiograms, right heart catheterizations, pulmonary function tests, six minute walk&#xD;
      tests and radiology reports will be collected as they occur.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date>June 2015</start_date>
  <completion_date type="Actual">January 26, 2017</completion_date>
  <primary_completion_date type="Actual">January 26, 2017</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Other</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Detection of Heart Failure Exacerbation Event</measure>
    <time_frame>90 Days</time_frame>
    <description>Correlation of algorithmic alerts generated by a non-invasive telemonitoring system to a verified heart failure exacerbation event, measured in percent accuracy</description>
  </primary_outcome>
  <enrollment type="Actual">100</enrollment>
  <condition>Cardiac Failure</condition>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>HealthPatch</intervention_name>
    <description>A multi-sensor device to collect continuous vital signs</description>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Individuals with heart failure, NYHA Class II-IV&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Subject must be 18 years old or older&#xD;
&#xD;
          2. NYHA( New York Heart Association Functional Classification) Class II-IV, documented in&#xD;
             site's medical record system.&#xD;
&#xD;
          3. Subject able and willing to sign Informed Consent Document.&#xD;
&#xD;
          4. Subject willing and able to perform all study related procedures.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Expected LVAD (Left Ventricular Assist Device) implantation or heart transplantation&#xD;
             in the next 30 days.&#xD;
&#xD;
          2. Skin damage or significant arthritis, preventing wearing of device.&#xD;
&#xD;
          3. Uncontrolled seizures or other neurological disorders leading to excessive abnormal&#xD;
             movements or tremors in the upper body.&#xD;
&#xD;
          4. Pregnant women or those who are currently nursing.&#xD;
&#xD;
          5. Visual/cognitive impairment that as judged by the investigator does not allow the&#xD;
             subject to independently follow rules and procedures of the protocol.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Josef Stehlik, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>University of Utah</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Palo Alto VA Health Care System</name>
      <address>
        <city>Palo Alto</city>
        <state>California</state>
        <zip>94304</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Malcom Randall VA Medical Center</name>
      <address>
        <city>Gainesville</city>
        <state>Florida</state>
        <zip>32608</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Michael E. DeBakey VA Medical Center</name>
      <address>
        <city>Houston</city>
        <state>Texas</state>
        <zip>77030</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>George E Wahlen Medical Center</name>
      <address>
        <city>Salt Lake City</city>
        <state>Utah</state>
        <zip>84148</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <results_reference>
    <citation>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.</citation>
    <PMID>32093506</PMID>
  </results_reference>
  <verification_date>August 2020</verification_date>
  <study_first_submitted>January 27, 2017</study_first_submitted>
  <study_first_submitted_qc>January 27, 2017</study_first_submitted_qc>
  <study_first_posted type="Estimate">January 31, 2017</study_first_posted>
  <last_update_submitted>August 3, 2020</last_update_submitted>
  <last_update_submitted_qc>August 3, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">August 5, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor-Investigator</responsible_party_type>
    <investigator_affiliation>University of Utah</investigator_affiliation>
    <investigator_full_name>Josef Stehlik</investigator_full_name>
    <investigator_title>Principal Investigator</investigator_title>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Heart Failure</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03037710
org study id: 81833
nct id: NCT03037710
lead sponsor: 
has dmc: No
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.
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.
observational model: Other
time perspective: Prospective
measure: Detection of Heart Failure Exacerbation Event
time frame: 90 Days
description: Correlation of algorithmic alerts generated by a non-invasive telemonitoring system to a verified heart failure exacerbation event, measured in percent accuracy
intervention type: Device
intervention name: HealthPatch
description: A multi-sensor device to collect continuous vital signs
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Josef Stehlik, MD
role: Principal Investigator
affiliation: University of Utah
facility: 
facility: 
facility: 
facility: 
country: United States
citation: 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.
PMID: 32093506
responsible party type: Sponsor-Investigator
investigator affiliation: University of Utah
investigator full name: Josef Stehlik
investigator title: Principal Investigator
mesh term: Heart Failure
sharing ipd: No