<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/NCT02200419</url>
  </required_header>
  <id_info>
    <org_study_id>14-7495-A</org_study_id>
    <nct_id>NCT02200419</nct_id>
  </id_info>
  <brief_title>POC - Transfusion Algorithm Cardiac Study</brief_title>
  <acronym>TACS</acronym>
  <official_title>Blood Conservation in Cardiac Surgery Using a Novel Transfusion Algorithm Based on Point-of-care Testing-A Stepped-wedge Cluster Randomized Controlled Trial</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University Health Network, Toronto</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>University Health Network, Toronto</source>
  <oversight_info>
    <has_dmc>Yes</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      This multi-centre study will provide sound, generalizable data on the effectiveness of a&#xD;
      POC-based algorithm to determine to what extent this guideline can reduce blood product&#xD;
      transfusions. Investigators will study outcomes in 7000 patients undergoing heart surgery at&#xD;
      10 participating hospitals. The proposed trial addresses several important research and&#xD;
      clinical issues and has the potential to markedly improve the transfusion management and&#xD;
      surgical care in general of cardiac surgery patients.&#xD;
&#xD;
      The intervention will be a novel POC-based algorithm that has been shown in a pilot study by&#xD;
      us to be associated with a substantial reduction in blood product transfusions. The algorithm&#xD;
      will employ viscoelastic and aggregometric POC-tests and an objective measure of blood loss.&#xD;
      The primary outcome will be avoidance of red blood cell transfusion during hospitalization.&#xD;
      The study has a 90% power to detect a 12% increase in avoidance rate. Secondary outcomes will&#xD;
      include avoidance of red blood cell use and other blood products (plasma, platelets, and&#xD;
      cryoprecipitate), units of blood products transfused, and adverse clinical outcomes related&#xD;
      to transfusion (acute kidney injury, infections, and death). Data will also be collected for&#xD;
      future health-economics analyses.&#xD;
&#xD;
      Largely due to the limitations of existing evidence, however, such algorithms are rarely used&#xD;
      in clinical practice. The proposed trial will provide sound, generalizable data on the&#xD;
      effectiveness of a POC-based algorithm to guide their future use. An integrated blood&#xD;
      management algorithm that employs POC coagulation tests will reduce blood product&#xD;
      transfusions in cardiac surgery, thereby improving clinical outcomes.&#xD;
&#xD;
      Does an integrated blood transfusion algorithm that employs POC coagulation tests applied&#xD;
      across a network of hospitals reduce blood transfusions and associated adverse outcomes in&#xD;
      cardiac surgery?&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Despite major advances in cardiac surgery, coagulopathy continues to carry a heavy burden in&#xD;
      cases that require the use of cardiopulmonary bypass (CPB), occurring frequently and&#xD;
      resulting in excessive blood loss, blood product transfusions, and adverse clinical outcomes.&#xD;
      Current management of coagulopathy is hampered by the inability of conventional laboratory&#xD;
      tests to delineate its etiology in a timely manner, thereby precluding timely and targeted&#xD;
      transfusion therapy. With the advent of point-of-care (POC) coagulation tests that can&#xD;
      rapidly identify the etiology of coagulopathy, it may now be possible to reduce the burden of&#xD;
      coagulopathy and thereby reduce transfusions and adverse outcomes. Several single-centre&#xD;
      studies (including one by the investigator group) have found that the use of POC-based&#xD;
      algorithms in cardiac surgery can markedly reduce blood product transfusions and by that&#xD;
      means reduce morbidities and mortality.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date>October 2014</start_date>
  <completion_date type="Actual">September 2016</completion_date>
  <primary_completion_date type="Actual">September 2016</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>N/A</allocation>
    <intervention_model>Single Group Assignment</intervention_model>
    <primary_purpose>Diagnostic</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Point Of Care (POC) Transfusion Algorithm Cardiac Study</measure>
    <time_frame>2.5 years</time_frame>
    <description>The primary outcome measure will be the proportion of patients who receive any allogeneic red blood cell (RBC) transfusion from admission before surgery to end of follow-up.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Transfusion</measure>
    <time_frame>7 days</time_frame>
    <description>Allogeneic blood products (platelets, plasma, cryoprecipitate) or coagulation factors (e.g., fibrinogen concentrate, factor VIIa), or undergo re-exploration; units of blood products transfused, nadir hemoglobin concentration (to identify changes in transfusion practice that may be unrelated to treatment of coagulopathy)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Blood loss</measure>
    <time_frame>24 hours</time_frame>
    <description>Chest-tube drainage at 6 and 24 hours after surgery</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Ventilation and hospital stay</measure>
    <time_frame>24 hours to 7 days</time_frame>
    <description>Duration of mechanical ventilation and length of stay in the ICU and hospital</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Kidney injury</measure>
    <time_frame>48 hours</time_frame>
    <description>Incidence of acute kidney injury, defined as a ≥ 2-fold in increase in creatinine or new renal replacement therapy within 48 hours after surgery</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Post operative complications</measure>
    <time_frame>7 days</time_frame>
    <description>Arrhythmias, sternal infection, myocardial infarction, stroke, thromboembolic events, and death</description>
  </secondary_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Actual">7402</enrollment>
  <condition>Heart Diseases</condition>
  <arm_group>
    <arm_group_label>Transfusion Algorithm</arm_group_label>
    <arm_group_type>Other</arm_group_type>
    <description>Hospitals will be randomized to the intervention arm of the study in a stratified manner.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>POC-based transfusion algorithm</intervention_name>
    <description>A transfusion algorithm based on point-of-care coagulation testing.</description>
    <arm_group_label>Transfusion Algorithm</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Hospital Inclusion:&#xD;
&#xD;
          -  Participating hospitals must not currently be using a POC-based blood conservation&#xD;
             algorithm; must have an in-hospital RBC transfusion rate of &gt; 35% and platelet or&#xD;
             plasma transfusion rates of &gt; 20%; must conduct &gt; 600 cardiac surgeries employing CPB&#xD;
             per year; and must have commitments from departments of Anesthesia, Cardiac Surgery,&#xD;
             and Laboratory Medicine to support the study and to abide by the algorithm&#xD;
&#xD;
          -  Blood transfusion algorithm instituted as standard-of-care at participating hospitals&#xD;
&#xD;
          -  All adult (≥ 18 years) patients undergoing cardiac surgery with cardiopulmonary bypass&#xD;
             (CPB) will be included in the analysis.&#xD;
&#xD;
        Exclusion Criteria: Hospitals not meeting inclusion.&#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>Keyvan Karkouti, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>University Health Network, Toronto</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Dr. Charles McAdams</name>
      <address>
        <city>Calgary</city>
        <state>Alberta</state>
        <zip>T2N 2T9</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Blaine Achen</name>
      <address>
        <city>Edmonton</city>
        <state>Alberta</state>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Dr. Terry Waters</name>
      <address>
        <city>Vancouver</city>
        <state>British Columbia</state>
        <zip>V5Z 1M9</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Dr. Sukhpal Brar</name>
      <address>
        <city>Vancouver</city>
        <state>British Columbia</state>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Dr. H. Grocott</name>
      <address>
        <city>Winnipeg</city>
        <state>Manitoba</state>
        <zip>R2H 2A6</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Dr. Summer Syed</name>
      <address>
        <city>Hamilton</city>
        <state>Ontario</state>
        <zip>L8N 3Z5</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Dr. Christopher Harle</name>
      <address>
        <city>London</city>
        <state>Ontario</state>
        <zip>N6A 5A5</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Dr. Daniel Kim</name>
      <address>
        <city>Newmarket</city>
        <state>Ontario</state>
        <zip>L3Y 2P9</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Dr. D. Tran</name>
      <address>
        <city>Ottawa</city>
        <state>Ontario</state>
        <zip>K1Y 4W7</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Dr. F. Moussa</name>
      <address>
        <city>Toronto</city>
        <state>Ontario</state>
        <zip>M4N 3M5</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Dr. E. Medicis</name>
      <address>
        <city>Fleurimont</city>
        <state>Quebec</state>
        <zip>J1H 5N4</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Dr. J. Bussieres</name>
      <address>
        <city>Sainte -Foy</city>
        <state>Quebec</state>
        <zip>G1V 4G5</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Canada</country>
  </location_countries>
  <verification_date>December 2016</verification_date>
  <study_first_submitted>June 20, 2014</study_first_submitted>
  <study_first_submitted_qc>July 24, 2014</study_first_submitted_qc>
  <study_first_posted type="Estimate">July 25, 2014</study_first_posted>
  <last_update_submitted>December 1, 2016</last_update_submitted>
  <last_update_submitted_qc>December 1, 2016</last_update_submitted_qc>
  <last_update_posted type="Estimate">December 5, 2016</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>University Health Network, Toronto</investigator_affiliation>
    <investigator_full_name>Jo Carroll</investigator_full_name>
    <investigator_title>Research Manager</investigator_title>
  </responsible_party>
  <keyword>Rotem</keyword>
  <keyword>Point of care coagulation tests</keyword>
  <keyword>Cardiac surgery</keyword>
  <keyword>Vascular surgery</keyword>
  <keyword>Cardiopulmonary bypass</keyword>
  <keyword>Blood products</keyword>
  <keyword>Transfusion</keyword>
  <keyword>Viscoelastic coagulation</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Heart Diseases</mesh_term>
  </condition_browse>
  <!-- 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/NCT02200419
org study id: 14-7495-A
nct id: NCT02200419
lead sponsor: 
has dmc: Yes
This multi-centre study will provide sound, generalizable data on the effectiveness of a
      POC-based algorithm to determine to what extent this guideline can reduce blood product
      transfusions. Investigators will study outcomes in 7000 patients undergoing heart surgery at
      10 participating hospitals. The proposed trial addresses several important research and
      clinical issues and has the potential to markedly improve the transfusion management and
      surgical care in general of cardiac surgery patients.

      The intervention will be a novel POC-based algorithm that has been shown in a pilot study by
      us to be associated with a substantial reduction in blood product transfusions. The algorithm
      will employ viscoelastic and aggregometric POC-tests and an objective measure of blood loss.
      The primary outcome will be avoidance of red blood cell transfusion during hospitalization.
      The study has a 90% power to detect a 12% increase in avoidance rate. Secondary outcomes will
      include avoidance of red blood cell use and other blood products (plasma, platelets, and
      cryoprecipitate), units of blood products transfused, and adverse clinical outcomes related
      to transfusion (acute kidney injury, infections, and death). Data will also be collected for
      future health-economics analyses.

      Largely due to the limitations of existing evidence, however, such algorithms are rarely used
      in clinical practice. The proposed trial will provide sound, generalizable data on the
      effectiveness of a POC-based algorithm to guide their future use. An integrated blood
      management algorithm that employs POC coagulation tests will reduce blood product
      transfusions in cardiac surgery, thereby improving clinical outcomes.

      Does an integrated blood transfusion algorithm that employs POC coagulation tests applied
      across a network of hospitals reduce blood transfusions and associated adverse outcomes in
      cardiac surgery?
Despite major advances in cardiac surgery, coagulopathy continues to carry a heavy burden in
      cases that require the use of cardiopulmonary bypass (CPB), occurring frequently and
      resulting in excessive blood loss, blood product transfusions, and adverse clinical outcomes.
      Current management of coagulopathy is hampered by the inability of conventional laboratory
      tests to delineate its etiology in a timely manner, thereby precluding timely and targeted
      transfusion therapy. With the advent of point-of-care (POC) coagulation tests that can
      rapidly identify the etiology of coagulopathy, it may now be possible to reduce the burden of
      coagulopathy and thereby reduce transfusions and adverse outcomes. Several single-centre
      studies (including one by the investigator group) have found that the use of POC-based
      algorithms in cardiac surgery can markedly reduce blood product transfusions and by that
      means reduce morbidities and mortality.
allocation: N/A
intervention model: Single Group Assignment
primary purpose: Diagnostic
masking: None (Open Label)
measure: Point Of Care (POC) Transfusion Algorithm Cardiac Study
time frame: 2.5 years
description: The primary outcome measure will be the proportion of patients who receive any allogeneic red blood cell (RBC) transfusion from admission before surgery to end of follow-up.
measure: Transfusion
time frame: 7 days
description: Allogeneic blood products (platelets, plasma, cryoprecipitate) or coagulation factors (e.g., fibrinogen concentrate, factor VIIa), or undergo re-exploration; units of blood products transfused, nadir hemoglobin concentration (to identify changes in transfusion practice that may be unrelated to treatment of coagulopathy)
measure: Blood loss
time frame: 24 hours
description: Chest-tube drainage at 6 and 24 hours after surgery
measure: Ventilation and hospital stay
time frame: 24 hours to 7 days
description: Duration of mechanical ventilation and length of stay in the ICU and hospital
measure: Kidney injury
time frame: 48 hours
description: Incidence of acute kidney injury, defined as a ≥ 2-fold in increase in creatinine or new renal replacement therapy within 48 hours after surgery
measure: Post operative complications
time frame: 7 days
description: Arrhythmias, sternal infection, myocardial infarction, stroke, thromboembolic events, and death
arm group label: Transfusion Algorithm
arm group type: Other
description: Hospitals will be randomized to the intervention arm of the study in a stratified manner.
intervention type: Other
intervention name: POC-based transfusion algorithm
description: A transfusion algorithm based on point-of-care coagulation testing.
arm group label: Transfusion Algorithm
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Keyvan Karkouti, MD
role: Principal Investigator
affiliation: University Health Network, Toronto
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
country: Canada
responsible party type: Principal Investigator
investigator affiliation: University Health Network, Toronto
investigator full name: Jo Carroll
investigator title: Research Manager
mesh term: Heart Diseases