<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/NCT03804606</url>
  </required_header>
  <id_info>
    <org_study_id>2018-0735</org_study_id>
    <nct_id>NCT03804606</nct_id>
  </id_info>
  <brief_title>Risk and Benefit Informed MTM Pharmacist Intervention in Heart Failure</brief_title>
  <official_title>Risk and Benefit Informed MTM Pharmacist Intervention in Heart Failure</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Geisinger Clinic</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Geisinger Clinic</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
    <is_us_export>Yes</is_us_export>
  </oversight_info>
  <brief_summary>
    <textblock>
      Out-of-hospital care of complex diseases, such as heart failure, is transitioning from an&#xD;
      individual patient-doctor relationship to population health management strategies. As an&#xD;
      example, at our institution, medication therapy management (MTM) pharmacists are being&#xD;
      deployed to patients with heart failure with the intent of improving patient outcomes&#xD;
      (through proper medication management and adherence) while reducing cost (e.g., keeping these&#xD;
      patients out of the hospital). The success of such strategies will be dependent on the&#xD;
      ability to effectively direct scarce resources to deliver appropriate/needed care to&#xD;
      patients. In this prospective, pragmatic randomized and matched controlled study, the&#xD;
      investigators hypothesize that the combination of accurate, data-driven benefit models and&#xD;
      MTM pharmacist intervention in patients with heart failure will result in reduced 1-year&#xD;
      mortality and hospital admissions. Using our extensive historical electronic health record&#xD;
      data, the investigators have developed a machine learning model that, for individual patients&#xD;
      with heart failure, predicts risk and benefit (that is, reduction in risk) associated with&#xD;
      closing specific &quot;care gaps&quot;. These care gaps represent standard evidence-based treatments&#xD;
      that may be missing for an individual patient, such as beta blockers or flu shots. The&#xD;
      investigators will use this model to define three cohorts to be studied: 1) a high risk/high&#xD;
      benefit group to be referred for MTM pharmacist intervention, 2) a high risk/high benefit&#xD;
      group to continue with existing standard of care (not necessarily involving MTM pharmacy),&#xD;
      and 3) a high risk/low benefit group to be referred for MTM pharmacist intervention.&#xD;
      Comparison of groups 1 and 2 (for which assignment is randomized) will evaluate the&#xD;
      effectiveness of the MTM pharmacy intervention, while comparison of groups 1 and 3 will&#xD;
      evaluate the accuracy of the benefit model prediction and importance of appropriate patient&#xD;
      selection for treatment. The primary study outcomes will be mortality and number of hospital&#xD;
      admissions during 1-year follow-up following study enrollment.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Heart failure is a highly prevalent, complex disease associated with significant morbidity&#xD;
      and cost. For example, Geisinger manages over 900 heart failure admissions per year, with&#xD;
      each admission costing an estimated $10,000-$12,000. As payment models continue to shift from&#xD;
      fee-for-service to value-based, significant investments are occurring in care team resources&#xD;
      to help manage populations of patients with heart failure. These care team resources have&#xD;
      demonstrated effectiveness. For example, internal Geisinger metrics indicate that&#xD;
      interventions led by clinical pharmacists aimed at poorly controlled type II diabetics have&#xD;
      resulted in a sustained median 1% (absolute) drop in hemoglobin hemoglobin a1C (glycated&#xD;
      hemoglobin). In this new environment, intelligent deployment of limited resources is critical&#xD;
      to drive quality and contain costs.&#xD;
&#xD;
      In heart failure, current risk prediction have demonstrated poor prognostic abilities and&#xD;
      present a barrier to &quot;precision delivery&quot; of care team resources. Currently approaches are&#xD;
      limited due to not fully utilizing rich, highly granular objective data such as imaging,&#xD;
      laboratory values, and vital signs, and therefore are not optimized to accurately predict&#xD;
      outcomes. The investigators have generated a machine learning model to predict both 1-year&#xD;
      survival and heart failure hospitalization within 6 months of echocardiography. This model&#xD;
      utilized 169 input variables including clinical data, imaging measures, and 18 care gap&#xD;
      variables. Our results showed not only that the machine learning model had far superior&#xD;
      accuracy to predict the morbidity endpoints compared to current approaches utilizing billing&#xD;
      code data, but also that care gap variables were important for predicting 1-year survival.&#xD;
      Moreover, the investigators showed that closing four of the care gap variables (flu&#xD;
      vaccination, evidence-based beta blocker treatment, ACE (angiotensin-converting-enzyme)&#xD;
      inhibitor/ARB (angiotensin receptor blockers) treatment, and control of diabetic a1C (i.e.,&#xD;
      values &quot;in goal)) resulted in a predicted improvement in 1-year survival of ~1200 (out of&#xD;
      ~11,000) patients. This study therefore aims to apply this machine learning approach to&#xD;
      direct care team resources in a clinical setting to evaluate its impact on patient survival&#xD;
      and healthcare utilization.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Enrolling by invitation</overall_status>
  <start_date type="Actual">February 28, 2019</start_date>
  <completion_date type="Anticipated">July 2023</completion_date>
  <primary_completion_date type="Anticipated">July 2023</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>
    <primary_purpose>Health Services Research</primary_purpose>
    <masking>Double (Participant, Care Provider)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>All-cause mortality</measure>
    <time_frame>1 year</time_frame>
    <description>Death following randomization</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Hospital admission</measure>
    <time_frame>1 year</time_frame>
    <description>Number of admissions to the hospital</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Healthcare utilization - Total cost of care</measure>
    <time_frame>1 year</time_frame>
    <description>Total cost of care (co-pays, claims paid, co-insurance, out-of-pocket costs) for the subset of patients in the study covered by the Geisinger Health Plan</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Incidence of flu vaccine care gap closure; relationship to mortality</measure>
    <time_frame>1 year</time_frame>
    <description>The investigators will compare rates of closure for the flu vaccine care gap among arms and compare predicted versus actual mortality as a function of the observed care gap closure.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Incidence of evidence-based beta blocker care gap closure; relationship to mortality</measure>
    <time_frame>1 year</time_frame>
    <description>The investigators will compare rates of closure for the evidence-based beta blocker care gap among arms and compare predicted versus actual hospitalization as a function of the observed care gap closure.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Incidence of ACE inhibitor/ARB care gap closure; relationship to mortality</measure>
    <time_frame>1 year</time_frame>
    <description>The investigators will compare rates of closure for the ACE inhibitor/ARB care gap among arms and compare predicted versus actual hospitalization as a function of the observed care gap closure.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Incidence of diabetic a1C &quot;in goal&quot; care gap closure; relationship to mortality</measure>
    <time_frame>1 year</time_frame>
    <description>The investigators will compare rates of closure for the diabetic a1C &quot;in goal&quot; care gap among arms and compare predicted versus actual hospitalization as a function of the observed care gap closure.</description>
  </secondary_outcome>
  <number_of_arms>3</number_of_arms>
  <enrollment type="Anticipated">600</enrollment>
  <condition>Heart Failure</condition>
  <arm_group>
    <arm_group_label>High benefit, MTM</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>This arm will comprise patients with heart failure who are predicted to receive high benefit (reduction in mortality risk) by addressing open care gaps. Following randomization, they will be referred to MTM pharmacy for review of treatments in an attempt to close appropriate care gaps.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>High benefit, no MTM</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>This arm will comprise patients with heart failure who are predicted to receive high benefit (reduction in mortality risk) by addressing open care gaps. Following randomization, they will continue to receive clinical standard-of-care: regular follow-ups with Community Medicine (every 3 months) and Cardiology (every six months). Importantly, these individuals are eligible for referral to MTM at the discretion of their physicians.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Low benefit, MTM</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>This arm will comprise patients with heart failure who are predicted to receive low benefit (reduction in mortality risk) by addressing open care gaps. They will be selected based on age, sex, and risk-matching to the High benefit, MTM arm. They will be referred to MTM pharmacy for review of treatments in an attempt to close appropriate care gaps.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Referral to MTM Pharmacist</intervention_name>
    <description>Patients will be referred for an encounter with a medication therapy management pharmacist.</description>
    <arm_group_label>High benefit, MTM</arm_group_label>
    <arm_group_label>Low benefit, MTM</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  All adult Geisinger patients with heart failure, as identified by a validated EHR&#xD;
             (Electonic Health Record)-based phenotype algorithm,&#xD;
&#xD;
          -  Patients with a Geisinger primary care provider (PCP)&#xD;
&#xD;
          -  Patients who follow with Geisinger Cardiology (at least 1 visit in past two years).&#xD;
&#xD;
          -  Fulfills the specifications for arm assignment based on the results of the care gap&#xD;
             benefit model.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Patients with a Geisinger PCP or Cardiologist in the South Central Region (part of the&#xD;
             Geisinger Holy Spirit footprint) as MTM availability is limited in this service area.&#xD;
&#xD;
          -  Patients who have indicated they do not wish to participate in research studies&#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>Christopher M Haggerty, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Geisinger Clinic</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Brandon K Fornwalt, MD, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Geisinger Clinic</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Geisinger Health System</name>
      <address>
        <city>Danville</city>
        <state>Pennsylvania</state>
        <zip>17822</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <reference>
    <citation>Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, Negahban SN, Krumholz HM. Analysis of Machine Learning Techniques for Heart Failure Readmissions. Circ Cardiovasc Qual Outcomes. 2016 Nov;9(6):629-640. doi: 10.1161/CIRCOUTCOMES.116.003039. Epub 2016 Nov 8.</citation>
    <PMID>28263938</PMID>
  </reference>
  <reference>
    <citation>Bhavnani SP, Parakh K, Atreja A, Druz R, Graham GN, Hayek SS, Krumholz HM, Maddox TM, Majmudar MD, Rumsfeld JS, Shah BR. 2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol. 2017 Nov 28;70(21):2696-2718. doi: 10.1016/j.jacc.2017.10.018. No abstract available.</citation>
    <PMID>29169478</PMID>
  </reference>
  <reference>
    <citation>Haga K, Murray S, Reid J, Ness A, O'Donnell M, Yellowlees D, Denvir MA. Identifying community based chronic heart failure patients in the last year of life: a comparison of the Gold Standards Framework Prognostic Indicator Guide and the Seattle Heart Failure Model. Heart. 2012 Apr;98(7):579-83. doi: 10.1136/heartjnl-2011-301021.</citation>
    <PMID>22422744</PMID>
  </reference>
  <verification_date>August 2022</verification_date>
  <study_first_submitted>January 11, 2019</study_first_submitted>
  <study_first_submitted_qc>January 14, 2019</study_first_submitted_qc>
  <study_first_posted type="Actual">January 15, 2019</study_first_posted>
  <last_update_submitted>August 3, 2022</last_update_submitted>
  <last_update_submitted_qc>August 3, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">August 5, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Heart Failure</keyword>
  <keyword>Machine Learning</keyword>
  <keyword>Medication Therapy Management</keyword>
  <keyword>Supervised Machine Learning</keyword>
  <keyword>Population Health Management</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>Yes</sharing_ipd>
    <ipd_description>Upon reasonable request to the PI, IPD (individual patient data) related to evaluation of the primary outcomes (group designation, vital status, number of hospital admissions, statuses of care gaps) will be made available to other researchers.</ipd_description>
  </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/NCT03804606
org study id: 2018-0735
nct id: NCT03804606
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
is us export: Yes
Out-of-hospital care of complex diseases, such as heart failure, is transitioning from an
      individual patient-doctor relationship to population health management strategies. As an
      example, at our institution, medication therapy management (MTM) pharmacists are being
      deployed to patients with heart failure with the intent of improving patient outcomes
      (through proper medication management and adherence) while reducing cost (e.g., keeping these
      patients out of the hospital). The success of such strategies will be dependent on the
      ability to effectively direct scarce resources to deliver appropriate/needed care to
      patients. In this prospective, pragmatic randomized and matched controlled study, the
      investigators hypothesize that the combination of accurate, data-driven benefit models and
      MTM pharmacist intervention in patients with heart failure will result in reduced 1-year
      mortality and hospital admissions. Using our extensive historical electronic health record
      data, the investigators have developed a machine learning model that, for individual patients
      with heart failure, predicts risk and benefit (that is, reduction in risk) associated with
      closing specific "care gaps". These care gaps represent standard evidence-based treatments
      that may be missing for an individual patient, such as beta blockers or flu shots. The
      investigators will use this model to define three cohorts to be studied: 1) a high risk/high
      benefit group to be referred for MTM pharmacist intervention, 2) a high risk/high benefit
      group to continue with existing standard of care (not necessarily involving MTM pharmacy),
      and 3) a high risk/low benefit group to be referred for MTM pharmacist intervention.
      Comparison of groups 1 and 2 (for which assignment is randomized) will evaluate the
      effectiveness of the MTM pharmacy intervention, while comparison of groups 1 and 3 will
      evaluate the accuracy of the benefit model prediction and importance of appropriate patient
      selection for treatment. The primary study outcomes will be mortality and number of hospital
      admissions during 1-year follow-up following study enrollment.
Heart failure is a highly prevalent, complex disease associated with significant morbidity
      and cost. For example, Geisinger manages over 900 heart failure admissions per year, with
      each admission costing an estimated $10,000-$12,000. As payment models continue to shift from
      fee-for-service to value-based, significant investments are occurring in care team resources
      to help manage populations of patients with heart failure. These care team resources have
      demonstrated effectiveness. For example, internal Geisinger metrics indicate that
      interventions led by clinical pharmacists aimed at poorly controlled type II diabetics have
      resulted in a sustained median 1% (absolute) drop in hemoglobin hemoglobin a1C (glycated
      hemoglobin). In this new environment, intelligent deployment of limited resources is critical
      to drive quality and contain costs.

      In heart failure, current risk prediction have demonstrated poor prognostic abilities and
      present a barrier to "precision delivery" of care team resources. Currently approaches are
      limited due to not fully utilizing rich, highly granular objective data such as imaging,
      laboratory values, and vital signs, and therefore are not optimized to accurately predict
      outcomes. The investigators have generated a machine learning model to predict both 1-year
      survival and heart failure hospitalization within 6 months of echocardiography. This model
      utilized 169 input variables including clinical data, imaging measures, and 18 care gap
      variables. Our results showed not only that the machine learning model had far superior
      accuracy to predict the morbidity endpoints compared to current approaches utilizing billing
      code data, but also that care gap variables were important for predicting 1-year survival.
      Moreover, the investigators showed that closing four of the care gap variables (flu
      vaccination, evidence-based beta blocker treatment, ACE (angiotensin-converting-enzyme)
      inhibitor/ARB (angiotensin receptor blockers) treatment, and control of diabetic a1C (i.e.,
      values "in goal)) resulted in a predicted improvement in 1-year survival of ~1200 (out of
      ~11,000) patients. This study therefore aims to apply this machine learning approach to
      direct care team resources in a clinical setting to evaluate its impact on patient survival
      and healthcare utilization.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Health Services Research
masking: Double (Participant, Care Provider)
measure: All-cause mortality
time frame: 1 year
description: Death following randomization
measure: Hospital admission
time frame: 1 year
description: Number of admissions to the hospital
measure: Healthcare utilization - Total cost of care
time frame: 1 year
description: Total cost of care (co-pays, claims paid, co-insurance, out-of-pocket costs) for the subset of patients in the study covered by the Geisinger Health Plan
measure: Incidence of flu vaccine care gap closure; relationship to mortality
time frame: 1 year
description: The investigators will compare rates of closure for the flu vaccine care gap among arms and compare predicted versus actual mortality as a function of the observed care gap closure.
measure: Incidence of evidence-based beta blocker care gap closure; relationship to mortality
time frame: 1 year
description: The investigators will compare rates of closure for the evidence-based beta blocker care gap among arms and compare predicted versus actual hospitalization as a function of the observed care gap closure.
measure: Incidence of ACE inhibitor/ARB care gap closure; relationship to mortality
time frame: 1 year
description: The investigators will compare rates of closure for the ACE inhibitor/ARB care gap among arms and compare predicted versus actual hospitalization as a function of the observed care gap closure.
measure: Incidence of diabetic a1C "in goal" care gap closure; relationship to mortality
time frame: 1 year
description: The investigators will compare rates of closure for the diabetic a1C "in goal" care gap among arms and compare predicted versus actual hospitalization as a function of the observed care gap closure.
arm group label: High benefit, MTM
arm group type: Experimental
description: This arm will comprise patients with heart failure who are predicted to receive high benefit (reduction in mortality risk) by addressing open care gaps. Following randomization, they will be referred to MTM pharmacy for review of treatments in an attempt to close appropriate care gaps.
arm group label: High benefit, no MTM
arm group type: No Intervention
description: This arm will comprise patients with heart failure who are predicted to receive high benefit (reduction in mortality risk) by addressing open care gaps. Following randomization, they will continue to receive clinical standard-of-care: regular follow-ups with Community Medicine (every 3 months) and Cardiology (every six months). Importantly, these individuals are eligible for referral to MTM at the discretion of their physicians.
arm group label: Low benefit, MTM
arm group type: Active Comparator
description: This arm will comprise patients with heart failure who are predicted to receive low benefit (reduction in mortality risk) by addressing open care gaps. They will be selected based on age, sex, and risk-matching to the High benefit, MTM arm. They will be referred to MTM pharmacy for review of treatments in an attempt to close appropriate care gaps.
intervention type: Other
intervention name: Referral to MTM Pharmacist
description: Patients will be referred for an encounter with a medication therapy management pharmacist.
arm group label: High benefit, MTM
arm group label: Low benefit, MTM
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Christopher M Haggerty, PhD
role: Principal Investigator
affiliation: Geisinger Clinic
last name: Brandon K Fornwalt, MD, PhD
role: Principal Investigator
affiliation: Geisinger Clinic
facility: 
country: United States
citation: Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, Negahban SN, Krumholz HM. Analysis of Machine Learning Techniques for Heart Failure Readmissions. Circ Cardiovasc Qual Outcomes. 2016 Nov;9(6):629-640. doi: 10.1161/CIRCOUTCOMES.116.003039. Epub 2016 Nov 8.
PMID: 28263938
citation: Bhavnani SP, Parakh K, Atreja A, Druz R, Graham GN, Hayek SS, Krumholz HM, Maddox TM, Majmudar MD, Rumsfeld JS, Shah BR. 2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol. 2017 Nov 28;70(21):2696-2718. doi: 10.1016/j.jacc.2017.10.018. No abstract available.
PMID: 29169478
citation: Haga K, Murray S, Reid J, Ness A, O'Donnell M, Yellowlees D, Denvir MA. Identifying community based chronic heart failure patients in the last year of life: a comparison of the Gold Standards Framework Prognostic Indicator Guide and the Seattle Heart Failure Model. Heart. 2012 Apr;98(7):579-83. doi: 10.1136/heartjnl-2011-301021.
PMID: 22422744
responsible party type: Sponsor
mesh term: Heart Failure
sharing ipd: Yes
ipd description: Upon reasonable request to the PI, IPD (individual patient data) related to evaluation of the primary outcomes (group designation, vital status, number of hospital admissions, statuses of care gaps) will be made available to other researchers.