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/NCT06013709</url>
    </required_header>
    <id_info>
      <org_study_id>PRESAGE</org_study_id>
      <nct_id>NCT06013709</nct_id>
    </id_info>
    <brief_title>A Machine Learning Algorithm to Predict Health Clinical Situations in Primary Healthcare for Frail Older Adults.</brief_title>
    <official_title>A Machine Learning Algorithm to Predict Health Clinical Situations (Fall, Undernutrition Risk, Depression Risk, Heart Failure Risk) and Improv Decision-support Tools in Primary Healthcare for Older Adults Living at Home.</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Presage</agency>
        <agency_class>Industry</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Assistance Publique - Hôpitaux de Paris</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Assistance Publique Hopitaux De Marseille</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Presage</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>
        Introduction: We developed a machine learning algorithm to predict the risk of emergency&#xD;
        hospitalization within the new 7 to 14 days with a good predictive performance (AUC=0.85).&#xD;
        Data recorded by home aides were send in real time to a secure server to be analyzed by our&#xD;
        machine learning algorithm, which predicted risk level and displayed it on a secure web-based&#xD;
        medical device. This study aims to implement and to evaluate the sensitivity and&#xD;
        specificity's predictions of Presage system for four clinical situations with a high impact&#xD;
        on unscheduled hospitalization of older adults living at home: falls, risk of depression (is&#xD;
        sadder), risk of undernutrition (eat less well) and risk of heart failure (swollen leg).&#xD;
  &#xD;
        Methods This is a retrospective observational multicenter study. To gain insight on both&#xD;
        short-and middle-term predictions and how the risk factors evolve through different periods&#xD;
        of observation, we developed a series of models which predict the risk of future clinical&#xD;
        symptoms.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        This is a retrospective observational multicenter study. This study was conducted on two&#xD;
        distinct cohorts.&#xD;
  &#xD;
        Data between January 2020 - February 2023 from 50 home care facilities using PRESAEGE CARE&#xD;
        medical device on a daily basis were analyzed. 740 853 data from 27 439 visits by home aides&#xD;
        for 1 478 patients. The patients' mean age was 84,89 years (SD = 8.9 years) with a moderate&#xD;
        dependency level and the sample included 1 038 women (70%).&#xD;
  &#xD;
        PRESAGE CARE is a medical device CE marked to predict emergency hospitalizations. This&#xD;
        e-health system is based on a questionnaire focused on functional and clinical autonomy (ie,&#xD;
        activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes&#xD;
        in behavior (eg, recognition and aggressiveness), and communication with the HA or their&#xD;
        surroundings.&#xD;
  &#xD;
        Based on these data, some others risks are evaluated and predict by the artificial&#xD;
        intelligence algorithm.&#xD;
  &#xD;
        This study aims to evaluate the sensitivity and specificity's predictions of PRESAGE CARE&#xD;
        system for four clinical situations with a high impact on unscheduled hospitalization of&#xD;
        olders adults living at home: falls, risk of depression (is sadder), risk if (eat less well)&#xD;
        and risk of heart failure (swollen leg).&#xD;
  &#xD;
        The principal objective was the sensitivity and specificity of four events' prediction:&#xD;
        falls, &quot;is sadder&quot;, &quot;eat less well&quot; and &quot;swollen leg&quot; for non-tautological events (when&#xD;
        events no appear in the observation window).&#xD;
  &#xD;
        Secondary objective was the sensitivity and specificity of four events' prediction: falls,&#xD;
        &quot;is sadder&quot;, &quot;eat less well&quot; and &quot;swollen leg&quot; for tautological events (when events appear in&#xD;
        the observation window).&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Completed</overall_status>
    <start_date type="Actual">April 1, 2016</start_date>
    <completion_date type="Actual">December 1, 2022</completion_date>
    <primary_completion_date type="Actual">April 1, 2016</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>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Sensitivity and specificity of four events' prediction: falls, &quot;is sadder&quot;, &quot;eat less well&quot; and &quot;swollen leg&quot; for non-tautological events ((when events no appear in the observation window).</measure>
      <time_frame>between one to six weeks</time_frame>
      <description>To evaluate the predictive performance of the models, we examined out-of-sample performance metrics, including area under the receiver operator characteristic curve (AUC), 5% and 15% AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) based on confusion matric which was created as followed: At each point of the ROC curve we calculated weighted average between Sensitivity and Specificity.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Sensitivity and specificity of four events' prediction: falls, &quot;is sadder&quot;, &quot;eat less well&quot; and &quot;swollen leg&quot; for tautological events ((when events no appear in the observation window).</measure>
      <time_frame>between one to six weeks</time_frame>
      <description>To evaluate the predictive performance of the models, we examined out-of-sample performance metrics, including area under the receiver operator characteristic curve (AUC), 5% and 15% AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) based on confusion matric which was created as followed: At each point of the ROC curve we calculated weighted average between Sensitivity and Specificity.</description>
    </secondary_outcome>
    <enrollment type="Actual">1478</enrollment>
    <condition>Frail Elderly Syndrome</condition>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>PRESAE CARE</intervention_name>
      <description>PRESAGE CARE is a medical device CE marked based on artificial intelligence to prevent and reduce emergency department visits and unplanned hospitalization among frail older adults living at home.&#xD;
  These device is based on the use of a short questionnaire focused on functional and clinical autonomy (ie, activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes in behavior (eg, recognition and aggressiveness), and communication with the home care aides (HAs)or their surroundings. This questionnaire is composed of very simple and easy-to-understand questions, giving a global view of the person's condition. For each of the 27 questions, a yes/no answer was requested. Data recorded by HAs were sent in real time to a secure server to be analyzed by our machine learning algorithm, which predicted the risk level on emergency hospitalization risk and some health clinical situations and displayed it on a web-based secure medical device.</description>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Frail older adults receiving the help of a home care aide using PRESAGE CARE device in&#xD;
          France.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  frail older adults aged 65 years old and over&#xD;
  &#xD;
            -  Receive the help of a home care aide using PRESAGE CARE&#xD;
  &#xD;
            -  All eligible persons were invited to participate and were included if they provided&#xD;
               consent&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>65 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 21, 2023</study_first_submitted>
    <study_first_submitted_qc>August 24, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 28, 2023</study_first_posted>
    <last_update_submitted>August 24, 2023</last_update_submitted>
    <last_update_submitted_qc>August 24, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 28, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>frailty</keyword>
    <keyword>older adults</keyword>
    <keyword>artificial intelligence</keyword>
    <keyword>falls</keyword>
    <keyword>undernutrition</keyword>
    <keyword>depression</keyword>
    <keyword>heart failure risk</keyword>
    <!-- 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/NCT06013709
  org study id: PRESAGE
  nct id: NCT06013709
  lead sponsor: 
  collaborator: 
  collaborator: 
  is fda regulated drug: No
  is fda regulated device: No
  Introduction: We developed a machine learning algorithm to predict the risk of emergency
        hospitalization within the new 7 to 14 days with a good predictive performance (AUC=0.85).
        Data recorded by home aides were send in real time to a secure server to be analyzed by our
        machine learning algorithm, which predicted risk level and displayed it on a secure web-based
        medical device. This study aims to implement and to evaluate the sensitivity and
        specificity's predictions of Presage system for four clinical situations with a high impact
        on unscheduled hospitalization of older adults living at home: falls, risk of depression (is
        sadder), risk of undernutrition (eat less well) and risk of heart failure (swollen leg).
  
        Methods This is a retrospective observational multicenter study. To gain insight on both
        short-and middle-term predictions and how the risk factors evolve through different periods
        of observation, we developed a series of models which predict the risk of future clinical
        symptoms.
  This is a retrospective observational multicenter study. This study was conducted on two
        distinct cohorts.
  
        Data between January 2020 - February 2023 from 50 home care facilities using PRESAEGE CARE
        medical device on a daily basis were analyzed. 740 853 data from 27 439 visits by home aides
        for 1 478 patients. The patients' mean age was 84,89 years (SD = 8.9 years) with a moderate
        dependency level and the sample included 1 038 women (70%).
  
        PRESAGE CARE is a medical device CE marked to predict emergency hospitalizations. This
        e-health system is based on a questionnaire focused on functional and clinical autonomy (ie,
        activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes
        in behavior (eg, recognition and aggressiveness), and communication with the HA or their
        surroundings.
  
        Based on these data, some others risks are evaluated and predict by the artificial
        intelligence algorithm.
  
        This study aims to evaluate the sensitivity and specificity's predictions of PRESAGE CARE
        system for four clinical situations with a high impact on unscheduled hospitalization of
        olders adults living at home: falls, risk of depression (is sadder), risk if (eat less well)
        and risk of heart failure (swollen leg).
  
        The principal objective was the sensitivity and specificity of four events' prediction:
        falls, "is sadder", "eat less well" and "swollen leg" for non-tautological events (when
        events no appear in the observation window).
  
        Secondary objective was the sensitivity and specificity of four events' prediction: falls,
        "is sadder", "eat less well" and "swollen leg" for tautological events (when events appear in
        the observation window).
  observational model: Cohort
  time perspective: Retrospective
  measure: Sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for non-tautological events ((when events no appear in the observation window).
  time frame: between one to six weeks
  description: To evaluate the predictive performance of the models, we examined out-of-sample performance metrics, including area under the receiver operator characteristic curve (AUC), 5% and 15% AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) based on confusion matric which was created as followed: At each point of the ROC curve we calculated weighted average between Sensitivity and Specificity.
  measure: Sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for tautological events ((when events no appear in the observation window).
  time frame: between one to six weeks
  description: To evaluate the predictive performance of the models, we examined out-of-sample performance metrics, including area under the receiver operator characteristic curve (AUC), 5% and 15% AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) based on confusion matric which was created as followed: At each point of the ROC curve we calculated weighted average between Sensitivity and Specificity.
  intervention type: Device
  intervention name: PRESAE CARE
  description: PRESAGE CARE is a medical device CE marked based on artificial intelligence to prevent and reduce emergency department visits and unplanned hospitalization among frail older adults living at home.
  These device is based on the use of a short questionnaire focused on functional and clinical autonomy (ie, activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes in behavior (eg, recognition and aggressiveness), and communication with the home care aides (HAs)or their surroundings. This questionnaire is composed of very simple and easy-to-understand questions, giving a global view of the person's condition. For each of the 27 questions, a yes/no answer was requested. Data recorded by HAs were sent in real time to a secure server to be analyzed by our machine learning algorithm, which predicted the risk level on emergency hospitalization risk and some health clinical situations and displayed it on a web-based secure medical device.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 65 Years
  maximum age: N/A
  healthy volunteers: No
  responsible party type: Sponsor