<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/NCT04462380</url>
  </required_header>
  <id_info>
    <org_study_id>20reamed01</org_study_id>
    <nct_id>NCT04462380</nct_id>
  </id_info>
  <brief_title>AiCR : Artificial Intelligence in Cardiac aRrest</brief_title>
  <acronym>AiCR</acronym>
  <official_title>AiCR : Artificial Intelligence in Cardiac aRrest Application of an Algorithm in the Prognosis of Recovered Cardiorespiratory Arrests</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Centre Hospitalier Universitaire de Nice</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Institut National de Recherche en Informatique et en Automatique</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>AIINTENSE</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Centre Hospitalier Universitaire de Nice</source>
  <oversight_info>
    <has_dmc>No</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 overall incidence of cardiorespiratory arrest in Europe is estimated at 350,000 to&#xD;
      700,000 cases per year. Survival rate is estimated at 10.7% for all rhythm disorders&#xD;
      combined.&#xD;
&#xD;
      Several examples of AI application in the medical field exist. Ting et al have developed a&#xD;
      computer tool capable of diagnosing the presence of diabetic retinopathy with excellent&#xD;
      power. In resuscitation, Celi et al proposed a tool capable of predicting the need for&#xD;
      crystalloid vascular filling during a systemic inflammatory state. In Nature in 2018,&#xD;
      Komorowski demonstrated the efficacy of AI in the hemodynamic management of sepsis. In a&#xD;
      study of the renal response to fluid challenge, Zhang et al. demonstrate the effectiveness of&#xD;
      the learning machine.&#xD;
&#xD;
      Objectives: Determination of an algorithm capable of predicting the mortality of patients&#xD;
      admitted to intensive care units (ICU) for ACR from hospitalization reports (CRH). Also use&#xD;
      of the algorithm to predict the risk of recurrence of the arrest, the duration of mechanical&#xD;
      ventilation, the appearance of sepsis, the development of organ failure, prediction of the&#xD;
      CPC (Cerebral Performance Category), time to obtain catecholamine withdrawal, the appearance&#xD;
      of acute renal failure with or without the need for extra-renal purification (EER) and&#xD;
      duration under EER, the average length of stay.&#xD;
&#xD;
      This project is part of a larger, nationwide project with greater power, and includes all the&#xD;
      data generated during hospitalization in intensive care.&#xD;
&#xD;
      Method: an estimated total number of patients included in this study to be between 300 and&#xD;
      500. The population will come from the intensive care units of Nice, Antibes, Cannes, Grasse.&#xD;
&#xD;
      Inclusion will be retrospective, on CRH, CR of CT imaging (cerebral and&#xD;
      thoraco-abdomino-pelvic), MRI, EEG, and daily follow-up words, from 2014 to the end of 2020.&#xD;
&#xD;
      After anonymisation, application of semantisation using natural language processing (NLP)&#xD;
      methods. The data to be extracted are entered in a document written by intensive care&#xD;
      physicians. These data will then be stored in a database. In order to meet the main&#xD;
      objective, we will develop a computer algorithm capable of predicting mortality in the study&#xD;
      population. This algorithm, based on a large database, can be designed using machine learning&#xD;
      or even deep learning techniques depending on the amount of data to be processed.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">February 1, 2020</start_date>
  <completion_date type="Anticipated">December 31, 2020</completion_date>
  <primary_completion_date type="Anticipated">December 31, 2020</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Only</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Prediction of mortality in the intensive care unit</measure>
    <time_frame>1day</time_frame>
    <description>Definition of a semantic reporting tool, automated, transition from an anonymized report to an operational and relevant database.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Prediction of mortality in the intensive care unit</measure>
    <time_frame>1day</time_frame>
    <description>Use of the database thus created to create an intelligent mortality prediction algorithm. Use also on secondary judgment criteria in order to predict other parameters mentioned below.</description>
  </primary_outcome>
  <enrollment type="Anticipated">500</enrollment>
  <condition>Cardio Respiratory Arrest</condition>
  <eligibility>
    <study_pop>
      <textblock>
        ACR for all causes, admitted in intensive care or intensive care, from the hospital centres&#xD;
        of Nice, Antibes, Cannes, Grasse, etc.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  OCA recovered from: hypoxic, ischemic, pulmonary embolism, tamponade, rhythm or&#xD;
             conduction disorder, shockable or not, intra or extra-hospital.&#xD;
&#xD;
          -  CR computerized, typed in PDF format&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
        -&#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_contact>
    <last_name>Jean DELLAMONICA</last_name>
    <phone>33 4 920 35 510</phone>
    <email>dellamonica.j@chu-nice.fr</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>romain LOMBARDI</last_name>
    <email>lombardi.r@chu-nice.fr</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Nice Hospital</name>
      <address>
        <city>Nice</city>
        <zip>06200</zip>
        <country>France</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Jean DELLAMONICA</last_name>
      <email>dellamonica.j@chu-nice.fr</email>
    </contact>
    <contact_backup>
      <last_name>romain LOMBARDI</last_name>
      <email>lombardi.r@chu-nice.fr</email>
    </contact_backup>
  </location>
  <location_countries>
    <country>France</country>
  </location_countries>
  <verification_date>July 2020</verification_date>
  <study_first_submitted>July 3, 2020</study_first_submitted>
  <study_first_submitted_qc>July 3, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 8, 2020</study_first_posted>
  <last_update_submitted>July 3, 2020</last_update_submitted>
  <last_update_submitted_qc>July 3, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">July 8, 2020</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>Heart Arrest</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/NCT04462380
org study id: 20reamed01
nct id: NCT04462380
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The overall incidence of cardiorespiratory arrest in Europe is estimated at 350,000 to
      700,000 cases per year. Survival rate is estimated at 10.7% for all rhythm disorders
      combined.

      Several examples of AI application in the medical field exist. Ting et al have developed a
      computer tool capable of diagnosing the presence of diabetic retinopathy with excellent
      power. In resuscitation, Celi et al proposed a tool capable of predicting the need for
      crystalloid vascular filling during a systemic inflammatory state. In Nature in 2018,
      Komorowski demonstrated the efficacy of AI in the hemodynamic management of sepsis. In a
      study of the renal response to fluid challenge, Zhang et al. demonstrate the effectiveness of
      the learning machine.

      Objectives: Determination of an algorithm capable of predicting the mortality of patients
      admitted to intensive care units (ICU) for ACR from hospitalization reports (CRH). Also use
      of the algorithm to predict the risk of recurrence of the arrest, the duration of mechanical
      ventilation, the appearance of sepsis, the development of organ failure, prediction of the
      CPC (Cerebral Performance Category), time to obtain catecholamine withdrawal, the appearance
      of acute renal failure with or without the need for extra-renal purification (EER) and
      duration under EER, the average length of stay.

      This project is part of a larger, nationwide project with greater power, and includes all the
      data generated during hospitalization in intensive care.

      Method: an estimated total number of patients included in this study to be between 300 and
      500. The population will come from the intensive care units of Nice, Antibes, Cannes, Grasse.

      Inclusion will be retrospective, on CRH, CR of CT imaging (cerebral and
      thoraco-abdomino-pelvic), MRI, EEG, and daily follow-up words, from 2014 to the end of 2020.

      After anonymisation, application of semantisation using natural language processing (NLP)
      methods. The data to be extracted are entered in a document written by intensive care
      physicians. These data will then be stored in a database. In order to meet the main
      objective, we will develop a computer algorithm capable of predicting mortality in the study
      population. This algorithm, based on a large database, can be designed using machine learning
      or even deep learning techniques depending on the amount of data to be processed.
observational model: Case-Only
time perspective: Retrospective
measure: Prediction of mortality in the intensive care unit
time frame: 1day
description: Definition of a semantic reporting tool, automated, transition from an anonymized report to an operational and relevant database.
measure: Prediction of mortality in the intensive care unit
time frame: 1day
description: Use of the database thus created to create an intelligent mortality prediction algorithm. Use also on secondary judgment criteria in order to predict other parameters mentioned below.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Jean DELLAMONICA
phone: 33 4 920 35 510
email: dellamonica.j@chu-nice.fr
last name: romain LOMBARDI
email: lombardi.r@chu-nice.fr
facility: 
status: Recruiting
contact: 
contact backup: 
country: France
responsible party type: Sponsor
mesh term: Heart Arrest