<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/NCT04502563</url>
  </required_header>
  <id_info>
    <org_study_id>IIR 19-238</org_study_id>
    <nct_id>NCT04502563</nct_id>
  </id_info>
  <brief_title>LINK-HF2 - Remote Monitoring Analytics in Heart Failure</brief_title>
  <acronym>LINK-HF2</acronym>
  <official_title>Continuous Wearable Monitoring Analytics to Improve Outcomes in Heart Failure - LINK-HF2 Multicenter Implementation Study</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>VA Office of Research and Development</agency>
      <agency_class>U.S. Fed</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>George E. Wahlen Department of Veterans Affairs Medical Center</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Michael E. DeBakey VA Medical Center</agency>
      <agency_class>U.S. Fed</agency_class>
    </collaborator>
    <collaborator>
      <agency>VA Palo Alto Health Care System</agency>
      <agency_class>U.S. Fed</agency_class>
    </collaborator>
    <collaborator>
      <agency>Malcom Randall VA Medical Center</agency>
      <agency_class>U.S. Fed</agency_class>
    </collaborator>
    <collaborator>
      <agency>Hunter Holmes McGuire VA Medical Center</agency>
      <agency_class>U.S. Fed</agency_class>
    </collaborator>
    <collaborator>
      <agency>VHA Innovation Ecosystem</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>VA Office of Research and Development</source>
  <oversight_info>
    <has_dmc>Yes</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
    <is_us_export>No</is_us_export>
  </oversight_info>
  <brief_summary>
    <textblock>
      Heart failure (HF) is a type of heart disease that leads to need of admissions to the&#xD;
      hospital during worsening of symptoms. These admissions are expensive and very inconvenient&#xD;
      for patients. The investigators have previously shown that monitoring of patients with a&#xD;
      using a small wearable sensor combined with a mathematical model can detect worsening of HF&#xD;
      before the patient needs medical care.&#xD;
&#xD;
      In this study the investigators will test whether the remote monitoring and prediction of HF&#xD;
      worsening can be used to find out when patients are at risk, change their treatment and avoid&#xD;
      a hospitalization.&#xD;
&#xD;
      The study will enroll 240 Veterans with HF and randomly assign half of them to monitoring and&#xD;
      communication of the information on HF worsening to their medical teams. The investigators&#xD;
      hope to find our how to best use this approach in routine care of HF. The investigators also&#xD;
      plan to determine if this approach will indeed led to less admissions to the hospital among&#xD;
      these patients, shorter hospital stays and better quality of life.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Heart failure (HF) represents a major health burden, with 80% of the HF health care costs&#xD;
      attributable to hospitalizations. In a pilot multicenter study funded by the VA Center for&#xD;
      Innovation, the investigators demonstrated that multivariate physiological telemetry using a&#xD;
      small wearable sensor has a high compliance rate and provides accurate early detection of&#xD;
      impending readmission for HF. In this study the investigators will implement non-invasive&#xD;
      remote monitoring within the VA system and perform a feasibility evaluation of the&#xD;
      intervention and its programmatic effectiveness after implementation. Our hypothesis is that&#xD;
      the implementation of this program will be feasible and acceptable to clinicians working in&#xD;
      VA HF clinics. The investigators also hypothesize that algorithmic response to an alert&#xD;
      generated by the predictive algorithm using a continuous stream of remote monitoring data&#xD;
      will be feasible and provide the basis for further testing of this approach to decrease the&#xD;
      risk of hospitalization for HF and improve other key clinical outcomes. The specific aims of&#xD;
      our study are:&#xD;
&#xD;
      Aim 1. Implement remote monitoring into the clinical workflow of HF care. Aim 1a. Design&#xD;
      implementation strategies for non-invasive remote monitoring and algorithmic response to&#xD;
      clinical alerts generated by the predictive analytics platform. In HF programs at five VA&#xD;
      medical centers, eligible patients will be enrolled at the time of hospital discharge for HF&#xD;
      exacerbation and receive a wearable monitor and a smartphone with cellular service. Data&#xD;
      continuously uploaded to a secure server will be analyzed by the predictive analytics&#xD;
      algorithm and a clinical alert will be generated when physiological derangements correlated&#xD;
      with impending HF exacerbation are identified. A clinical response algorithm will provide&#xD;
      instructions for management response to the alert, to include medication changes and/or&#xD;
      urgent/non-urgent outpatient assessment. The intervention will include electronic health&#xD;
      record integration. The investigators will design implementation processes for this program&#xD;
      using the integrated Promoting Action on Research Implementation in Health Services&#xD;
      (i-PARiHS) framework, adapted for the VA QUERI. The investigators will design 3 phases of&#xD;
      implementation: 1) implementation intervention planning through workflow analysis, technology&#xD;
      assessments, and recipient/stakeholder interviews; 2) formative evaluation of pilot&#xD;
      implementation at two vanguard sites to test initial acceptability, reliability, and&#xD;
      equipment performance; and 3) implementation fidelity monitoring by assessing consistency,&#xD;
      safety and satisfaction.&#xD;
&#xD;
      Aim 1b. Evaluate implementation outcomes, including clinician and patient perceptions and&#xD;
      adoption of the use of ambulatory remote monitoring data. The investigators will use both&#xD;
      quantitative and qualitative research methods to examine the eight core dimensions of&#xD;
      implementation outcomes. Focus groups and semi-structured interviews will be done to assess&#xD;
      clinician and patient perceptions of acceptability and feasibility. Adoption behaviors will&#xD;
      be tracked including alert response rates and appropriateness of decisions. Fidelity of&#xD;
      implementation will be monitored by assessing compliance with all aspects of the study&#xD;
      protocol. Penetration and sustainability will be evaluated by assessing variation in&#xD;
      implementation outcomes across the five study sites as well as participant perceptions from&#xD;
      the qualitative work at the end of the study.&#xD;
&#xD;
      Aim 2. Conduct a feasibility study of non-invasive remote monitoring in chronic HF.&#xD;
&#xD;
      Aim 2a. Define key characteristics that will inform design of a pivotal trial of non-invasive&#xD;
      remote monitoring aimed at reducing rehospitalization and improving quality of life in HF.&#xD;
      The investigators will enroll 240 patients hospitalized for HF exacerbation. At enrollment,&#xD;
      subjects will undergo 1:1 randomization to intervention or control arm. While all study&#xD;
      subjects will use the monitoring device for 90 days after discharge, in the intervention arm,&#xD;
      clinicians will be notified of clinical alerts and will follow the response algorithm to&#xD;
      modify HF treatment and/or recommend urgent clinic visit/emergency room visit. In the control&#xD;
      arm, information from the sensor will be collected, but clinical alerts will not be generated&#xD;
      or communicated to providers. The main study outcomes will include the proportion of&#xD;
      randomized patients who meet the algorithm's criteria for at least one alert, the proportion&#xD;
      of time the remote monitor is in use and functioning properly, HF hospitalization rate,&#xD;
      length of hospital stay, and health-related quality of life. Implementation factors&#xD;
      identified in Aim 1 will help clarify the results of this aim.&#xD;
&#xD;
      Aim 2b. Identify costs associated with implementation and clinical use of non-invasive remote&#xD;
      monitoring in HF. Correct classification of costs associated with implementation of&#xD;
      non-invasive remote monitoring will set the stage for cost-effectiveness analyses in a future&#xD;
      pivotal trial.&#xD;
&#xD;
      Recent advances in technology and in machine learning provide an opportunity for processing&#xD;
      of new sources of real-time patient-level data to generate clinically actionable information.&#xD;
      An important knowledge gap is how to best implement this technology-based approach into&#xD;
      clinical practice. Our study addresses this critical question of clinical implementation, and&#xD;
      will generate feasibility data for a design of a pivotal clinical trial of non-invasive&#xD;
      remote monitoring with predictive analytics during the high-risk period after hospital&#xD;
      discharge. This work has potential to result in changes to care of Veterans with HF and other&#xD;
      chronic health conditions.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">April 19, 2021</start_date>
  <completion_date type="Anticipated">October 30, 2024</completion_date>
  <primary_completion_date type="Anticipated">October 30, 2024</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <intervention_model_description>Prospective randomized study</intervention_model_description>
    <primary_purpose>Health Services Research</primary_purpose>
    <masking>Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)</masking>
    <masking_description>All subjects will wear non-invasive sensors. The subjects and the investigators will not know whether subjects are randomized to active arm (remote monitoring data shared with treatment team and used for clinical decisions per algorithm) or to control arm (data collected but not shared with treatment team).</masking_description>
  </study_design_info>
  <primary_outcome>
    <measure>Heart failure hospitalization rate</measure>
    <time_frame>90 days</time_frame>
    <description>90-day hospitalization rate in subjects in active arm vs control arm</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Kansas City Cardiomyopathy Questionaire Score</measure>
    <time_frame>90 days</time_frame>
    <description>Scale range 0-100. Higher result is better.</description>
  </secondary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Anticipated">240</enrollment>
  <condition>Heart Failure</condition>
  <arm_group>
    <arm_group_label>Active arm</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Subjects will undergo remote monitoring, remote monitoring data will be analyzed on a predictive platform, alerts indicating HF worsening shared with treating team, and algorithmic response to alerts implements.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Control</arm_group_label>
    <arm_group_type>Sham Comparator</arm_group_type>
    <description>Subjects will wear a sensor, but data from the sensor will not generate alerts and will not be shared with the treating team.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Remote monitoring and predictive analytics</intervention_name>
    <description>Subjects will undergo remote monitoring, remote monitoring data will be analyzed on a predictive platform, alerts indicating HF worsening shared with treating team, and algorithmic response to alerts implements.</description>
    <arm_group_label>Active arm</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Sham comparator</intervention_name>
    <description>Subjects will wear a sensor, but data from the sensor will not generate alerts and will not be shared with the treating team.</description>
    <arm_group_label>Control</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Subject must be 18 years old or older&#xD;
&#xD;
          -  NYHA( New York Heart Association Functional Classification) Class II-IV, documented in&#xD;
             site's medical record system.&#xD;
&#xD;
          -  Subject able and willing to sign Informed Consent Document, and if participating in a&#xD;
             patient interview, able to comprehend and agree with items listed in the VA Consent&#xD;
             Cover Letter.&#xD;
&#xD;
          -  Subject willing and able to perform all study related procedures.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Expected LVAD (Left Ventricular Assist Device) implantation or heart transplantation&#xD;
             in the next 30 days.&#xD;
&#xD;
          -  Skin damage or significant arthritis, preventing wearing of device.&#xD;
&#xD;
          -  Uncontrolled seizures or other neurological disorders leading to excessive abnormal&#xD;
             movements or tremors in the upper body.&#xD;
&#xD;
          -  Pregnant women or those who are currently nursing.&#xD;
&#xD;
          -  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 MPH</last_name>
    <role>Principal Investigator</role>
    <affiliation>VA Salt Lake City Health Care System, Salt Lake City, UT</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Heather Hanson, AAS BS</last_name>
    <phone>(801) 582-1565</phone>
    <phone_ext>4543</phone_ext>
    <email>Heather.Hanson@va.gov</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Josef Stehlik, MD MPH</last_name>
    <phone>(801) 582-1565</phone>
    <phone_ext>4543</phone_ext>
    <email>josef.stehlik@hsc.utah.edu</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>VA Palo Alto Health Care System, Palo Alto, CA</name>
      <address>
        <city>Palo Alto</city>
        <state>California</state>
        <zip>94304-1207</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Not yet recruiting</status>
    <contact>
      <last_name>Karim Sallam</last_name>
      <email>Karim.Sallam@va.gov</email>
    </contact>
    <investigator>
      <last_name>Karim Sallam</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location>
    <facility>
      <name>North Florida/South Georgia Veterans Health System, Gainesville, FL</name>
      <address>
        <city>Gainesville</city>
        <state>Florida</state>
        <zip>32608-1135</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Carsten Schmalfuss, MD</last_name>
      <phone>352-376-1611</phone>
      <email>Carsten.Schmalfuss@va.gov</email>
    </contact>
    <investigator>
      <last_name>Carsten Schmalfuss, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location>
    <facility>
      <name>Michael E. DeBakey VA Medical Center, Houston, TX</name>
      <address>
        <city>Houston</city>
        <state>Texas</state>
        <zip>77030-4211</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Not yet recruiting</status>
    <contact>
      <last_name>Biykem Bozkurt, MD</last_name>
      <phone>713-440-4400</phone>
      <email>BOZKURT.BIYKEM@va.gov</email>
    </contact>
    <investigator>
      <last_name>Biykem Bozkurt, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location>
    <facility>
      <name>VA Salt Lake City Health Care System, Salt Lake City, UT</name>
      <address>
        <city>Salt Lake City</city>
        <state>Utah</state>
        <zip>84148-0001</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Amyanne Wuthrich, MS BS</last_name>
      <phone>(801) 582-1565</phone>
      <phone_ext>4169</phone_ext>
      <email>Amyanne.Wuthrich@va.gov</email>
    </contact>
    <investigator>
      <last_name>Charlene Raye Weir, PhD RN</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Josef Stehlik, MD MPH</last_name>
      <role>Principal Investigator</role>
    </investigator>
    <investigator>
      <last_name>Richard E Nelson, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Susan L. Zickmund, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Thomas C Hanff, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location>
    <facility>
      <name>Hunter Holmes McGuire VA Medical Center, Richmond, VA</name>
      <address>
        <city>Richmond</city>
        <state>Virginia</state>
        <zip>23249</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Not yet recruiting</status>
    <contact>
      <last_name>Neil P Lewis</last_name>
      <email>Neil.Lewis@va.gov</email>
    </contact>
    <investigator>
      <last_name>Neil P. Lewis</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <results_reference>
    <citation>Nelson RE, Hyun D, Jezek A, Samore MH. Mortality, Length of Stay, and Healthcare Costs Associated With Multidrug-Resistant Bacterial Infections Among Elderly Hospitalized Patients in the United States. Clin Infect Dis. 2022 Mar 23;74(6):1070-1080. doi: 10.1093/cid/ciab696.</citation>
    <PMID>34617118</PMID>
  </results_reference>
  <verification_date>March 2023</verification_date>
  <study_first_submitted>August 4, 2020</study_first_submitted>
  <study_first_submitted_qc>August 4, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">August 6, 2020</study_first_posted>
  <last_update_submitted>March 9, 2023</last_update_submitted>
  <last_update_submitted_qc>March 9, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">March 13, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>heart failure</keyword>
  <keyword>remote monitoring</keyword>
  <keyword>predictive analytics</keyword>
  <keyword>artificial intelligence</keyword>
  <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/NCT04502563
org study id: IIR 19-238
nct id: NCT04502563
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
is us export: No
Heart failure (HF) is a type of heart disease that leads to need of admissions to the
      hospital during worsening of symptoms. These admissions are expensive and very inconvenient
      for patients. The investigators have previously shown that monitoring of patients with a
      using a small wearable sensor combined with a mathematical model can detect worsening of HF
      before the patient needs medical care.

      In this study the investigators will test whether the remote monitoring and prediction of HF
      worsening can be used to find out when patients are at risk, change their treatment and avoid
      a hospitalization.

      The study will enroll 240 Veterans with HF and randomly assign half of them to monitoring and
      communication of the information on HF worsening to their medical teams. The investigators
      hope to find our how to best use this approach in routine care of HF. The investigators also
      plan to determine if this approach will indeed led to less admissions to the hospital among
      these patients, shorter hospital stays and better quality of life.
Heart failure (HF) represents a major health burden, with 80% of the HF health care costs
      attributable to hospitalizations. In a pilot multicenter study funded by the VA Center for
      Innovation, the investigators demonstrated that multivariate physiological telemetry using a
      small wearable sensor has a high compliance rate and provides accurate early detection of
      impending readmission for HF. In this study the investigators will implement non-invasive
      remote monitoring within the VA system and perform a feasibility evaluation of the
      intervention and its programmatic effectiveness after implementation. Our hypothesis is that
      the implementation of this program will be feasible and acceptable to clinicians working in
      VA HF clinics. The investigators also hypothesize that algorithmic response to an alert
      generated by the predictive algorithm using a continuous stream of remote monitoring data
      will be feasible and provide the basis for further testing of this approach to decrease the
      risk of hospitalization for HF and improve other key clinical outcomes. The specific aims of
      our study are:

      Aim 1. Implement remote monitoring into the clinical workflow of HF care. Aim 1a. Design
      implementation strategies for non-invasive remote monitoring and algorithmic response to
      clinical alerts generated by the predictive analytics platform. In HF programs at five VA
      medical centers, eligible patients will be enrolled at the time of hospital discharge for HF
      exacerbation and receive a wearable monitor and a smartphone with cellular service. Data
      continuously uploaded to a secure server will be analyzed by the predictive analytics
      algorithm and a clinical alert will be generated when physiological derangements correlated
      with impending HF exacerbation are identified. A clinical response algorithm will provide
      instructions for management response to the alert, to include medication changes and/or
      urgent/non-urgent outpatient assessment. The intervention will include electronic health
      record integration. The investigators will design implementation processes for this program
      using the integrated Promoting Action on Research Implementation in Health Services
      (i-PARiHS) framework, adapted for the VA QUERI. The investigators will design 3 phases of
      implementation: 1) implementation intervention planning through workflow analysis, technology
      assessments, and recipient/stakeholder interviews; 2) formative evaluation of pilot
      implementation at two vanguard sites to test initial acceptability, reliability, and
      equipment performance; and 3) implementation fidelity monitoring by assessing consistency,
      safety and satisfaction.

      Aim 1b. Evaluate implementation outcomes, including clinician and patient perceptions and
      adoption of the use of ambulatory remote monitoring data. The investigators will use both
      quantitative and qualitative research methods to examine the eight core dimensions of
      implementation outcomes. Focus groups and semi-structured interviews will be done to assess
      clinician and patient perceptions of acceptability and feasibility. Adoption behaviors will
      be tracked including alert response rates and appropriateness of decisions. Fidelity of
      implementation will be monitored by assessing compliance with all aspects of the study
      protocol. Penetration and sustainability will be evaluated by assessing variation in
      implementation outcomes across the five study sites as well as participant perceptions from
      the qualitative work at the end of the study.

      Aim 2. Conduct a feasibility study of non-invasive remote monitoring in chronic HF.

      Aim 2a. Define key characteristics that will inform design of a pivotal trial of non-invasive
      remote monitoring aimed at reducing rehospitalization and improving quality of life in HF.
      The investigators will enroll 240 patients hospitalized for HF exacerbation. At enrollment,
      subjects will undergo 1:1 randomization to intervention or control arm. While all study
      subjects will use the monitoring device for 90 days after discharge, in the intervention arm,
      clinicians will be notified of clinical alerts and will follow the response algorithm to
      modify HF treatment and/or recommend urgent clinic visit/emergency room visit. In the control
      arm, information from the sensor will be collected, but clinical alerts will not be generated
      or communicated to providers. The main study outcomes will include the proportion of
      randomized patients who meet the algorithm's criteria for at least one alert, the proportion
      of time the remote monitor is in use and functioning properly, HF hospitalization rate,
      length of hospital stay, and health-related quality of life. Implementation factors
      identified in Aim 1 will help clarify the results of this aim.

      Aim 2b. Identify costs associated with implementation and clinical use of non-invasive remote
      monitoring in HF. Correct classification of costs associated with implementation of
      non-invasive remote monitoring will set the stage for cost-effectiveness analyses in a future
      pivotal trial.

      Recent advances in technology and in machine learning provide an opportunity for processing
      of new sources of real-time patient-level data to generate clinically actionable information.
      An important knowledge gap is how to best implement this technology-based approach into
      clinical practice. Our study addresses this critical question of clinical implementation, and
      will generate feasibility data for a design of a pivotal clinical trial of non-invasive
      remote monitoring with predictive analytics during the high-risk period after hospital
      discharge. This work has potential to result in changes to care of Veterans with HF and other
      chronic health conditions.
allocation: Randomized
intervention model: Parallel Assignment
intervention model description: Prospective randomized study
primary purpose: Health Services Research
masking: Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)
masking description: All subjects will wear non-invasive sensors. The subjects and the investigators will not know whether subjects are randomized to active arm (remote monitoring data shared with treatment team and used for clinical decisions per algorithm) or to control arm (data collected but not shared with treatment team).
measure: Heart failure hospitalization rate
time frame: 90 days
description: 90-day hospitalization rate in subjects in active arm vs control arm
measure: Kansas City Cardiomyopathy Questionaire Score
time frame: 90 days
description: Scale range 0-100. Higher result is better.
arm group label: Active arm
arm group type: Experimental
description: Subjects will undergo remote monitoring, remote monitoring data will be analyzed on a predictive platform, alerts indicating HF worsening shared with treating team, and algorithmic response to alerts implements.
arm group label: Control
arm group type: Sham Comparator
description: Subjects will wear a sensor, but data from the sensor will not generate alerts and will not be shared with the treating team.
intervention type: Other
intervention name: Remote monitoring and predictive analytics
description: Subjects will undergo remote monitoring, remote monitoring data will be analyzed on a predictive platform, alerts indicating HF worsening shared with treating team, and algorithmic response to alerts implements.
arm group label: Active arm
intervention type: Other
intervention name: Sham comparator
description: Subjects will wear a sensor, but data from the sensor will not generate alerts and will not be shared with the treating team.
arm group label: Control
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Josef Stehlik, MD MPH
role: Principal Investigator
affiliation: VA Salt Lake City Health Care System, Salt Lake City, UT
last name: Heather Hanson, AAS BS
phone: (801) 582-1565
phone ext: 4543
email: Heather.Hanson@va.gov
last name: Josef Stehlik, MD MPH
phone: (801) 582-1565
phone ext: 4543
email: josef.stehlik@hsc.utah.edu
facility: 
status: Not yet recruiting
contact: 
investigator: 
facility: 
status: Recruiting
contact: 
investigator: 
facility: 
status: Not yet recruiting
contact: 
investigator: 
facility: 
status: Recruiting
contact: 
investigator: 
investigator: 
investigator: 
investigator: 
investigator: 
facility: 
status: Not yet recruiting
contact: 
investigator: 
country: United States
citation: Nelson RE, Hyun D, Jezek A, Samore MH. Mortality, Length of Stay, and Healthcare Costs Associated With Multidrug-Resistant Bacterial Infections Among Elderly Hospitalized Patients in the United States. Clin Infect Dis. 2022 Mar 23;74(6):1070-1080. doi: 10.1093/cid/ciab696.
PMID: 34617118
responsible party type: Sponsor
mesh term: Heart Failure
sharing ipd: No