<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/NCT03235193</url>
  </required_header>
  <id_info>
    <org_study_id>1097090-1</org_study_id>
    <nct_id>NCT03235193</nct_id>
  </id_info>
  <brief_title>Predictive algoRithm for EValuation and Intervention in SEpsis</brief_title>
  <acronym>PREVISE</acronym>
  <official_title>Prediction of Severe Sepsis Using a Machine Learning Algorithm</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Dascena</agency>
      <agency_class>Industry</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Cabell Huntington Hospital</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Dascena</source>
  <oversight_info>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      In this prospective study, the ability of a machine learning algorithm to predict sepsis and&#xD;
      influence clinical outcomes, will be investigated at Cabell Huntington Hospital (CHH).&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">July 1, 2017</start_date>
  <completion_date type="Actual">August 30, 2017</completion_date>
  <primary_completion_date type="Actual">August 30, 2017</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Non-Randomized</allocation>
    <intervention_model>Factorial Assignment</intervention_model>
    <primary_purpose>Diagnostic</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>In-hospital mortality</measure>
    <time_frame>Through study completion, an average of 30 days</time_frame>
  </primary_outcome>
  <secondary_outcome>
    <measure>Hospital length of stay</measure>
    <time_frame>Through study completion, an average of 30 days</time_frame>
  </secondary_outcome>
  <other_outcome>
    <measure>Hospital readmission</measure>
    <time_frame>Through study completion, an average of 30 days</time_frame>
  </other_outcome>
  <other_outcome>
    <measure>ICU length of stay</measure>
    <time_frame>Through study completion, an average of 30 days</time_frame>
  </other_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">2296</enrollment>
  <condition>Sepsis</condition>
  <condition>Septic Shock</condition>
  <condition>Severe Sepsis</condition>
  <arm_group>
    <arm_group_label>With InSight</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Healthcare provider receives an alert from InSight for patients trending towards severe sepsis. Healthcare provider also receives information from the severe sepsis detector in the CHH electronic health record.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Without Insight</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Healthcare provider does not receive any alerts from InSight. Healthcare provider receives information from the severe sepsis detector in the CHH electronic health record.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Severe Sepsis Prediction</intervention_name>
    <description>Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.</description>
    <arm_group_label>With InSight</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Severe Sepsis Detection</intervention_name>
    <description>Upon receiving information from the severe sepsis detector in the CHH electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.</description>
    <arm_group_label>With InSight</arm_group_label>
    <arm_group_label>Without Insight</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  All adult patients visiting the emergency department, or admitted to the participating&#xD;
             intensive care unit (ICU) wards of Cabell Huntington Hospital will be eligible.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  All patients younger than 18 years of age will be excluded.&#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>Hoyt Burdick</last_name>
    <role>Principal Investigator</role>
    <affiliation>Cabell Huntington Hospital</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Cabell Huntington Hospital</name>
      <address>
        <city>Huntington</city>
        <state>West Virginia</state>
        <zip>25701</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <reference>
    <citation>Calvert J, Desautels T, Chettipally U, Barton C, Hoffman J, Jay M, Mao Q, Mohamadlou H, Das R. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg (Lond). 2016 May 10;8:50-5. doi: 10.1016/j.amsu.2016.04.023. eCollection 2016 Jun.</citation>
    <PMID>27489621</PMID>
  </reference>
  <reference>
    <citation>Calvert JS, Price DA, Chettipally UK, Barton CW, Feldman MD, Hoffman JL, Jay M, Das R. A computational approach to early sepsis detection. Comput Biol Med. 2016 Jul 1;74:69-73. doi: 10.1016/j.compbiomed.2016.05.003. Epub 2016 May 12.</citation>
    <PMID>27208704</PMID>
  </reference>
  <reference>
    <citation>Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman MD, Barton C, Wales DJ, Das R. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Med Inform. 2016 Sep 30;4(3):e28. doi: 10.2196/medinform.5909.</citation>
    <PMID>27694098</PMID>
  </reference>
  <verification_date>September 2021</verification_date>
  <study_first_submitted>July 27, 2017</study_first_submitted>
  <study_first_submitted_qc>July 27, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 1, 2017</study_first_posted>
  <last_update_submitted>September 17, 2021</last_update_submitted>
  <last_update_submitted_qc>September 17, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">September 21, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Sepsis</mesh_term>
    <mesh_term>Toxemia</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/NCT03235193
org study id: 1097090-1
nct id: NCT03235193
lead sponsor: 
collaborator: 
is fda regulated drug: No
is fda regulated device: No
In this prospective study, the ability of a machine learning algorithm to predict sepsis and
      influence clinical outcomes, will be investigated at Cabell Huntington Hospital (CHH).
allocation: Non-Randomized
intervention model: Factorial Assignment
primary purpose: Diagnostic
masking: None (Open Label)
measure: In-hospital mortality
time frame: Through study completion, an average of 30 days
measure: Hospital length of stay
time frame: Through study completion, an average of 30 days
measure: Hospital readmission
time frame: Through study completion, an average of 30 days
measure: ICU length of stay
time frame: Through study completion, an average of 30 days
arm group label: With InSight
arm group type: Experimental
description: Healthcare provider receives an alert from InSight for patients trending towards severe sepsis. Healthcare provider also receives information from the severe sepsis detector in the CHH electronic health record.
arm group label: Without Insight
arm group type: Active Comparator
description: Healthcare provider does not receive any alerts from InSight. Healthcare provider receives information from the severe sepsis detector in the CHH electronic health record.
intervention type: Other
intervention name: Severe Sepsis Prediction
description: Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
arm group label: With InSight
intervention type: Other
intervention name: Severe Sepsis Detection
description: Upon receiving information from the severe sepsis detector in the CHH electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.
arm group label: With InSight
arm group label: Without Insight
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Hoyt Burdick
role: Principal Investigator
affiliation: Cabell Huntington Hospital
facility: 
country: United States
citation: Calvert J, Desautels T, Chettipally U, Barton C, Hoffman J, Jay M, Mao Q, Mohamadlou H, Das R. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg (Lond). 2016 May 10;8:50-5. doi: 10.1016/j.amsu.2016.04.023. eCollection 2016 Jun.
PMID: 27489621
citation: Calvert JS, Price DA, Chettipally UK, Barton CW, Feldman MD, Hoffman JL, Jay M, Das R. A computational approach to early sepsis detection. Comput Biol Med. 2016 Jul 1;74:69-73. doi: 10.1016/j.compbiomed.2016.05.003. Epub 2016 May 12.
PMID: 27208704
citation: Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman MD, Barton C, Wales DJ, Das R. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Med Inform. 2016 Sep 30;4(3):e28. doi: 10.2196/medinform.5909.
PMID: 27694098
responsible party type: Sponsor
mesh term: Sepsis
mesh term: Toxemia