xml: <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/NCT05988658</url>
    </required_header>
    <id_info>
      <org_study_id>IRB23-0343</org_study_id>
      <secondary_id>R01DK126933</secondary_id>
      <nct_id>NCT05988658</nct_id>
    </id_info>
    <brief_title>Combining Biomarkers and Electronic Risk Scores to Predict AKI in Hospitalized Patients</brief_title>
    <official_title>Combining Biomarkers and Electronic Risk Scores to Predict AKI in Hospitalized Patients</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>University of Chicago</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)</agency>
        <agency_class>NIH</agency_class>
      </collaborator>
      <collaborator>
        <agency>University of Wisconsin, Madison</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>University of Chicago</source>
    <oversight_info>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
      <is_us_export>No</is_us_export>
    </oversight_info>
    <brief_summary>
      <textblock>
        The study's objective is to evaluate the additive value of renal biomarkers (from blood and&#xD;
        urine) for identifying individuals at high risk for severe acute kidney injury (AKI) above&#xD;
        that of a novel natural language processing(NLP)-based AKI risk algorithm. The risk algorithm&#xD;
        is based on electronic health records (EHR) data (labs, vitals, clinical notes, and test&#xD;
        reports). Patients will be enrolled at the University of Chicago Medical Center and the&#xD;
        University of Wisconsin Hospital, where the risk score will be run in real time. The risk&#xD;
        score will identify those patients with the highest risk for the future development of Stage&#xD;
        2 AKI and collect blood and urine for biomarker measurement over the subsequent 3 days.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        The investigators hypothesize that combining the biomarkers with electronic health risk score&#xD;
        will impact improvement in AKI risk stratification. Using a real time, externally validated&#xD;
        electronic health record based AKI risk score, we will enroll patients who are at high risk&#xD;
        for the impending development of KDIGO Stage 2 AKI (top 10% of risk). Once identified and&#xD;
        enrolled, patients will have blood and urine samples collected over the next 3 days. We will&#xD;
        recruit 2 cohorts of 400 patients across the 2 institutions. In the development cohort we&#xD;
        will see if adding urinary or serum biomarkers of AKI can improve the ability of EHR-risk&#xD;
        score to predict the development of Stage 2 AKI and other outcomes. We will compare the area&#xD;
        under the receiver operator characteristic curve (AUC) for the risk score alone versus the&#xD;
        risk score plus biomarkers. We will then seek to validate our findings in a separate cohort&#xD;
        of 400 patients.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">August 1, 2023</start_date>
    <completion_date type="Anticipated">August 1, 2027</completion_date>
    <primary_completion_date type="Anticipated">August 1, 2026</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Developing KDIGO stage 2 AKI</measure>
      <time_frame>With in 7 days of enrollment</time_frame>
      <description>Number of patients developing KDIGO Stage 2 AKI.&#xD;
  KDIGO Stage 2 AKI is defined as:&#xD;
  A double of baseline serum creatinine from baseline&#xD;
  OR&#xD;
  12 hours of urine output of less than 0.5ml/kg/hr in those with bladder catheters.&#xD;
  If no catheter in place than urine output based AKI can not be diagnosed</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Development of KDIGO stage 3 AKI</measure>
      <time_frame>with in 12 hour of each observation, with in 7 days of enrollment and 90 day MAKE outcome</time_frame>
      <description>Number of patients developing KDIGO Stage 3 AKI&#xD;
  KDIGO Stage 3 AKI can be defined as:&#xD;
  Increase in Serum creatinine by 3.0 times baseline&#xD;
  OR&#xD;
  Increase serum creatinine to &gt; 4.0 mg/dL&#xD;
  OR&#xD;
  Need for Renal Replacement Therapy (RRT)</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Recipient of renal replacement therapy(RRT)</measure>
      <time_frame>with in 12 hour of each observation, with in 7 days of enrollment and 90 day make outcome</time_frame>
      <description>The number of patients who receive RRT due to following indications (in the setting of Stage 2/3 AKI) :&#xD;
  Hyperkalemia (≥ 6 mmol/L)&#xD;
  Diuretic-resistant hypervolemia (difficult to define)&#xD;
  BUN urea serum levels greater than or equal to 150mg/ dL&#xD;
  Severe metabolic acidosis (pH ≤ 7.15)&#xD;
  Oliguria (urinary output &lt; 200mL/12hr), or anuria.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Change in Mortality Status during hospitalization</measure>
      <time_frame>with in 12 hour of each observation, with in 7 days of enrollment and during current hospitalization</time_frame>
      <description>Patients' mortality status during current hospitalization</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Major Adverse Kidney Events (MAKE) Outcomes</measure>
      <time_frame>3 months (90 days)</time_frame>
      <description>Number of Participants developing Major Adverse Kidney Events (MAKE) :&#xD;
  Recurrent Hospitalization&#xD;
  Kidney Function Status:&#xD;
  Recurrent AKI&#xD;
  New chronic kidney disease (CKD)&#xD;
  Need or continued need for RRT&#xD;
  Mortality</description>
    </secondary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">800</enrollment>
    <condition>Acute Kidney Injury</condition>
    <condition>Biomarkers</condition>
    <arm_group>
      <arm_group_label>Study cohort</arm_group_label>
      <description>Patients will be identified as high risk based on their AKI risk score (ESTOP- AKI 2.0) being in the top 10% of all hospitalized patients</description>
    </arm_group>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>ESTOP - AKI 2.0</intervention_name>
      <description>Medical software as a Noninvasive medical device, which at the time of the project will not implement directly into subject/clinical care.</description>
      <arm_group_label>Study cohort</arm_group_label>
    </intervention>
    <biospec_retention>Samples Without DNA</biospec_retention>
    <biospec_descr>
      <textblock>
        1. Blood&#xD;
  &#xD;
          2. Urine&#xD;
      </textblock>
    </biospec_descr>
    <eligibility>
      <study_pop>
        <textblock>
          Adult patients admitted in inpatient ward, intermediate or ICU care at UCMC or UWHealth&#xD;
          with E-STOP AKI 2.0 score in the top 10% of risk (historically from all hospitalized&#xD;
          patients) within the last 12 hours. (First time across this 10% risk threshold during this&#xD;
          hospital stay)&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            1. Age ≥ 18 years&#xD;
  &#xD;
            2. E-STOP AKI 2.0 score in the top 10% of risk (historically from all hospitalized&#xD;
               patients) within the last 12 hours. (First time across this 10% risk threshold during&#xD;
               this hospital stay).&#xD;
  &#xD;
            3. Admitted to an inpatient ward, intermediate, or ICU care at the University of Chicago&#xD;
               Medical Center (UCMC) or University of Wisconsin Health (UWHealth. (No Emergency&#xD;
               Department patients)&#xD;
  &#xD;
            4. Patient or their legally authorized representative must be able to read, speak, and&#xD;
               understand English, for the purposes of consenting. Otherwise, inclusion in this&#xD;
               protocol will be done without regard to race, ethnic origin or gender&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            1. Voluntary refusal or missing written consent of the patient / legal representative.&#xD;
  &#xD;
            2. Patients with a known history of end-stage renal disease on dialysis (including renal&#xD;
               transplantation).&#xD;
  &#xD;
            3. Patients without a measured serum creatinine value during their inpatient stay.&#xD;
  &#xD;
            4. Patients with a creatinine &gt;4.0 mg/dl at the time of admission or available in the EHR&#xD;
               from the last 6 months&#xD;
  &#xD;
            5. Patients with prior episode of KDIGO defined AKI during this same hospitalization-&#xD;
               regardless of E-STOP AKI 2.0 score&#xD;
  &#xD;
            6. Patients with prior renal consultation during their admission.&#xD;
  &#xD;
            7. Patient with an E-STOP AKI 2.0 above the top 10% risk threshold more than 12 hours ago&#xD;
               during this same hospital stay.&#xD;
  &#xD;
            8. Incarcerated patients&#xD;
  &#xD;
            9. Pregnant patients&#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>Jay Koyner, MD</last_name>
      <role>Principal Investigator</role>
      <affiliation>University of Chicago</affiliation>
    </overall_official>
    <overall_official>
      <last_name>Matthew Churpek, MD,MPH,PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>University of Wisconsin, Madison</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Jay Koyner, MD</last_name>
      <phone>773-702-4842</phone>
      <email>jkoyner@uchicago.edu</email>
    </overall_contact>
    <location>
      <facility>
        <name>University of Chicago Medical Center</name>
        <address>
          <city>Chicago</city>
          <state>Illinois</state>
          <zip>60637</zip>
          <country>United States</country>
        </address>
      </facility>
      <contact>
        <last_name>Aiman Fatima, MBBS</last_name>
        <phone>773-702-6201</phone>
        <email>aimanfatima@uchicagomedicine.org</email>
      </contact>
      <contact_backup>
        <last_name>Ola Anjorin, MBBS,DA,MPH</last_name>
        <phone>773-704-3168</phone>
        <email>oanjorin@bsd.uchicago.edu</email>
      </contact_backup>
      <investigator>
        <last_name>Jay Koyner, MD</last_name>
        <role>Principal Investigator</role>
      </investigator>
    </location>
    <location>
      <facility>
        <name>University of Wisconsin Hospital</name>
        <address>
          <city>Madison</city>
          <state>Wisconsin</state>
          <zip>53792</zip>
          <country>United States</country>
        </address>
      </facility>
      <contact>
        <last_name>Madeline Ogus, MS</last_name>
        <phone>608-265-2878</phone>
        <email>mkoguss@medicine.wisc.edu</email>
      </contact>
      <contact_backup>
        <last_name>Michael Weber</last_name>
        <phone>608-263-3369</phone>
        <email>mjweber@medicine.wisc.edu</email>
      </contact_backup>
      <investigator>
        <last_name>Matthew Churpek, MD,MPH,PhD</last_name>
        <role>Principal Investigator</role>
      </investigator>
    </location>
    <location_countries>
      <country>United States</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 4, 2023</study_first_submitted>
    <study_first_submitted_qc>August 4, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 14, 2023</study_first_posted>
    <last_update_submitted>August 4, 2023</last_update_submitted>
    <last_update_submitted_qc>August 4, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 14, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Acute Kidney Injury</keyword>
    <keyword>Biomarkers</keyword>
    <keyword>Renal Replacement Therapy</keyword>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Risk Assessment</keyword>
    <keyword>Clinical Nephrology</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Acute Kidney Injury</mesh_term>
    </condition_browse>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT05988658
  org study id: IRB23-0343
  secondary id: R01DK126933
  nct id: NCT05988658
  lead sponsor: 
  collaborator: 
  collaborator: 
  is fda regulated drug: No
  is fda regulated device: No
  is us export: No
  The study's objective is to evaluate the additive value of renal biomarkers (from blood and
        urine) for identifying individuals at high risk for severe acute kidney injury (AKI) above
        that of a novel natural language processing(NLP)-based AKI risk algorithm. The risk algorithm
        is based on electronic health records (EHR) data (labs, vitals, clinical notes, and test
        reports). Patients will be enrolled at the University of Chicago Medical Center and the
        University of Wisconsin Hospital, where the risk score will be run in real time. The risk
        score will identify those patients with the highest risk for the future development of Stage
        2 AKI and collect blood and urine for biomarker measurement over the subsequent 3 days.
  The investigators hypothesize that combining the biomarkers with electronic health risk score
        will impact improvement in AKI risk stratification. Using a real time, externally validated
        electronic health record based AKI risk score, we will enroll patients who are at high risk
        for the impending development of KDIGO Stage 2 AKI (top 10% of risk). Once identified and
        enrolled, patients will have blood and urine samples collected over the next 3 days. We will
        recruit 2 cohorts of 400 patients across the 2 institutions. In the development cohort we
        will see if adding urinary or serum biomarkers of AKI can improve the ability of EHR-risk
        score to predict the development of Stage 2 AKI and other outcomes. We will compare the area
        under the receiver operator characteristic curve (AUC) for the risk score alone versus the
        risk score plus biomarkers. We will then seek to validate our findings in a separate cohort
        of 400 patients.
  observational model: Cohort
  time perspective: Prospective
  measure: Developing KDIGO stage 2 AKI
  time frame: With in 7 days of enrollment
  description: Number of patients developing KDIGO Stage 2 AKI.
  KDIGO Stage 2 AKI is defined as:
  A double of baseline serum creatinine from baseline
  OR
  12 hours of urine output of less than 0.5ml/kg/hr in those with bladder catheters.
  If no catheter in place than urine output based AKI can not be diagnosed
  measure: Development of KDIGO stage 3 AKI
  time frame: with in 12 hour of each observation, with in 7 days of enrollment and 90 day MAKE outcome
  description: Number of patients developing KDIGO Stage 3 AKI
  KDIGO Stage 3 AKI can be defined as:
  Increase in Serum creatinine by 3.0 times baseline
  OR
  Increase serum creatinine to > 4.0 mg/dL
  OR
  Need for Renal Replacement Therapy (RRT)
  measure: Recipient of renal replacement therapy(RRT)
  time frame: with in 12 hour of each observation, with in 7 days of enrollment and 90 day make outcome
  description: The number of patients who receive RRT due to following indications (in the setting of Stage 2/3 AKI) :
  Hyperkalemia (≥ 6 mmol/L)
  Diuretic-resistant hypervolemia (difficult to define)
  BUN urea serum levels greater than or equal to 150mg/ dL
  Severe metabolic acidosis (pH ≤ 7.15)
  Oliguria (urinary output < 200mL/12hr), or anuria.
  measure: Change in Mortality Status during hospitalization
  time frame: with in 12 hour of each observation, with in 7 days of enrollment and during current hospitalization
  description: Patients' mortality status during current hospitalization
  measure: Major Adverse Kidney Events (MAKE) Outcomes
  time frame: 3 months (90 days)
  description: Number of Participants developing Major Adverse Kidney Events (MAKE) :
  Recurrent Hospitalization
  Kidney Function Status:
  Recurrent AKI
  New chronic kidney disease (CKD)
  Need or continued need for RRT
  Mortality
  arm group label: Study cohort
  description: Patients will be identified as high risk based on their AKI risk score (ESTOP- AKI 2.0) being in the top 10% of all hospitalized patients
  intervention type: Device
  intervention name: ESTOP - AKI 2.0
  description: Medical software as a Noninvasive medical device, which at the time of the project will not implement directly into subject/clinical care.
  arm group label: Study cohort
  1. Blood
  
          2. Urine
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Jay Koyner, MD
  role: Principal Investigator
  affiliation: University of Chicago
  last name: Matthew Churpek, MD,MPH,PhD
  role: Principal Investigator
  affiliation: University of Wisconsin, Madison
  last name: Jay Koyner, MD
  phone: 773-702-4842
  email: jkoyner@uchicago.edu
  facility: 
  contact: 
  contact backup: 
  investigator: 
  facility: 
  contact: 
  contact backup: 
  investigator: 
  country: United States
  responsible party type: Sponsor
  mesh term: Acute Kidney Injury