<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/NCT03752489</url>
  </required_header>
  <id_info>
    <org_study_id>19-426246</org_study_id>
    <nct_id>NCT03752489</nct_id>
  </id_info>
  <brief_title>Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment</brief_title>
  <official_title>Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Dascena</agency>
      <agency_class>Industry</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Dascena</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>
  </oversight_info>
  <brief_summary>
    <textblock>
      The focus of this study will be to conduct a prospective, randomized controlled trial (RCT)&#xD;
      at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center&#xD;
      (UCSF) in which a fluid treatment-specific algorithm will be applied to EHR data for the&#xD;
      detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the&#xD;
      algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH,&#xD;
      and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint,&#xD;
      reductions in in-hospital mortality.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Not yet recruiting</overall_status>
  <start_date type="Anticipated">April 1, 2022</start_date>
  <completion_date type="Anticipated">March 31, 2024</completion_date>
  <primary_completion_date type="Anticipated">March 31, 2024</primary_completion_date>
  <phase>Phase 2</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>Diagnostic</primary_purpose>
    <masking>Triple (Participant, Care Provider, Investigator)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>In-hospital SIRS-based mortality</measure>
    <time_frame>Through study completion, an average of 8 months</time_frame>
    <description>Mortality attributed to patients meeting two or more SIRS criteria at some point during their stay</description>
  </primary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Anticipated">51645</enrollment>
  <condition>Sepsis</condition>
  <condition>Severe Sepsis</condition>
  <condition>Septic Shock</condition>
  <arm_group>
    <arm_group_label>Fluid treatment-specific algorithm</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>The experimental arm will involve patients monitored by the fluid treatment-customized version of InSight.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Standard InSight</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>The control arm will involve patients monitored with the standard, non-treatment specific version of InSight.</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Treatment-specific InSight</intervention_name>
    <description>The InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis, and in this study will be customized to differentiate between clusters of patients who respond similarly to fluids treatment according to the nature of their disease progression.</description>
    <arm_group_label>Fluid treatment-specific algorithm</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>InSight</intervention_name>
    <description>The non-customized InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis.</description>
    <arm_group_label>Standard InSight</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  All adults above age 18 who are a member of one of the clinical subpopulations studied&#xD;
             in this trial are eligible to participate in the study.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Under age 18&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Qingqing Mao, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Dascena, Inc.</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Qingqing Mao, PhD</last_name>
    <phone>5108269508</phone>
    <email>qmao@dascena.com</email>
  </overall_contact>
  <reference>
    <citation>Calvert J, Mao Q, Hoffman JL, Jay M, Desautels T, Mohamadlou H, Chettipally U, Das R. Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Ann Med Surg (Lond). 2016 Sep 6;11:52-57. doi: 10.1016/j.amsu.2016.09.002. eCollection 2016 Nov.</citation>
    <PMID>27699003</PMID>
  </reference>
  <reference>
    <citation>Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017 Nov 9;4(1):e000234. doi: 10.1136/bmjresp-2017-000234. eCollection 2017.</citation>
    <PMID>29435343</PMID>
  </reference>
  <reference>
    <citation>Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018 Jan 26;8(1):e017833. doi: 10.1136/bmjopen-2017-017833.</citation>
    <PMID>29374661</PMID>
  </reference>
  <verification_date>September 2021</verification_date>
  <study_first_submitted>November 21, 2018</study_first_submitted>
  <study_first_submitted_qc>November 21, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">November 26, 2018</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 23, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Dascena</keyword>
  <keyword>machine learning</keyword>
  <keyword>fluid administration</keyword>
  <keyword>clustering algorithm</keyword>
  <keyword>mortality</keyword>
  <keyword>diagnostic</keyword>
  <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/NCT03752489
org study id: 19-426246
nct id: NCT03752489
lead sponsor: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
The focus of this study will be to conduct a prospective, randomized controlled trial (RCT)
      at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center
      (UCSF) in which a fluid treatment-specific algorithm will be applied to EHR data for the
      detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the
      algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH,
      and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint,
      reductions in in-hospital mortality.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Diagnostic
masking: Triple (Participant, Care Provider, Investigator)
measure: In-hospital SIRS-based mortality
time frame: Through study completion, an average of 8 months
description: Mortality attributed to patients meeting two or more SIRS criteria at some point during their stay
arm group label: Fluid treatment-specific algorithm
arm group type: Experimental
description: The experimental arm will involve patients monitored by the fluid treatment-customized version of InSight.
arm group label: Standard InSight
arm group type: Active Comparator
description: The control arm will involve patients monitored with the standard, non-treatment specific version of InSight.
intervention type: Diagnostic Test
intervention name: Treatment-specific InSight
description: The InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis, and in this study will be customized to differentiate between clusters of patients who respond similarly to fluids treatment according to the nature of their disease progression.
arm group label: Fluid treatment-specific algorithm
intervention type: Diagnostic Test
intervention name: InSight
description: The non-customized InSight algorithm which draws information from a patient's electronic health record (EHR) to predict the onset of severe sepsis.
arm group label: Standard InSight
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Qingqing Mao, PhD
role: Principal Investigator
affiliation: Dascena, Inc.
last name: Qingqing Mao, PhD
phone: 5108269508
email: qmao@dascena.com
citation: Calvert J, Mao Q, Hoffman JL, Jay M, Desautels T, Mohamadlou H, Chettipally U, Das R. Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Ann Med Surg (Lond). 2016 Sep 6;11:52-57. doi: 10.1016/j.amsu.2016.09.002. eCollection 2016 Nov.
PMID: 27699003
citation: Shimabukuro DW, Barton CW, Feldman MD, Mataraso SJ, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017 Nov 9;4(1):e000234. doi: 10.1136/bmjresp-2017-000234. eCollection 2017.
PMID: 29435343
citation: Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018 Jan 26;8(1):e017833. doi: 10.1136/bmjopen-2017-017833.
PMID: 29374661
responsible party type: Sponsor
mesh term: Sepsis
mesh term: Toxemia