<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/NCT03015454</url>
  </required_header>
  <id_info>
    <org_study_id>16-19647</org_study_id>
    <nct_id>NCT03015454</nct_id>
  </id_info>
  <brief_title>An Algorithm Driven Sepsis Prediction Biomarker</brief_title>
  <official_title>A Randomized Controlled Clinical Trial of an Algorithm Driven Sepsis Prediction Biomarker</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Dascena</agency>
      <agency_class>Industry</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>University of California, San Francisco</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Dascena</source>
  <brief_summary>
    <textblock>
      A sepsis early warning predictive algorithm, InSight, has been developed and validated on a&#xD;
      large, diverse patient cohort. In this prospective study, the ability of InSight to predict&#xD;
      severe sepsis patients is investigated. Specifically, InSight is compared with a well&#xD;
      established severe sepsis detector in the UCSF electronic health record (EHR).&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date>December 2016</start_date>
  <primary_completion_date type="Actual">February 2017</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>Factorial Assignment</intervention_model>
    <primary_purpose>Screening</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Hospital length of stay</measure>
    <time_frame>Through study completion, an average of 45 days</time_frame>
  </primary_outcome>
  <secondary_outcome>
    <measure>In-hospital mortality</measure>
    <time_frame>Through study completion, an average of 45 days</time_frame>
  </secondary_outcome>
  <other_outcome>
    <measure>ICU length of stay</measure>
    <time_frame>Through study completion, an average of 45 days</time_frame>
  </other_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">142</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 UCSF 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 UCSF 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 UCSF 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 admitted to the participating units 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>Ritankar Das</last_name>
    <role>Principal Investigator</role>
    <affiliation>Dascena</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>UCSF Moffit-Long Hospital</name>
      <address>
        <city>San Francisco</city>
        <state>California</state>
        <zip>94143</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <reference>
    <citation>Calvert JS, Price DA, Barton CW, Chettipally UK, Das R. Discharge recommendation based on a novel technique of homeostatic analysis. J Am Med Inform Assoc. 2017 Jan;24(1):24-29. doi: 10.1093/jamia/ocw014. Epub 2016 Mar 28.</citation>
    <PMID>27026611</PMID>
  </reference>
  <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>December 31, 2016</study_first_submitted>
  <study_first_submitted_qc>January 6, 2017</study_first_submitted_qc>
  <study_first_posted type="Estimate">January 10, 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 23, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>InSight</keyword>
  <keyword>Dascena</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/NCT03015454
org study id: 16-19647
nct id: NCT03015454
lead sponsor: 
collaborator: 
A sepsis early warning predictive algorithm, InSight, has been developed and validated on a
      large, diverse patient cohort. In this prospective study, the ability of InSight to predict
      severe sepsis patients is investigated. Specifically, InSight is compared with a well
      established severe sepsis detector in the UCSF electronic health record (EHR).
allocation: Randomized
intervention model: Factorial Assignment
primary purpose: Screening
masking: None (Open Label)
measure: Hospital length of stay
time frame: Through study completion, an average of 45 days
measure: In-hospital mortality
time frame: Through study completion, an average of 45 days
measure: ICU length of stay
time frame: Through study completion, an average of 45 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 UCSF 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 UCSF 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 UCSF 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: Ritankar Das
role: Principal Investigator
affiliation: Dascena
facility: 
country: United States
citation: Calvert JS, Price DA, Barton CW, Chettipally UK, Das R. Discharge recommendation based on a novel technique of homeostatic analysis. J Am Med Inform Assoc. 2017 Jan;24(1):24-29. doi: 10.1093/jamia/ocw014. Epub 2016 Mar 28.
PMID: 27026611
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