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/NCT06042595</url>
    </required_header>
    <id_info>
      <org_study_id>Dropout-Prediction-2023</org_study_id>
      <nct_id>NCT06042595</nct_id>
    </id_info>
    <brief_title>Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach</brief_title>
    <official_title>Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>University Hospital Heidelberg</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>University Hospital Heidelberg</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 study aims to develop a prediction model of premature treatment termination in&#xD;
        psychosomatic hospitals using a machine learning approach.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        The aim of the study is to identify risk factors that lead to or predict premature treatment&#xD;
        termination in psychosomatic hospitals. In the long-term, the study shall help to develop&#xD;
        more precise prediction models that can enhance communication between therapists and patients&#xD;
        about potential dropout and- if necessary- adaption of treatment in using a feedback loop.&#xD;
  &#xD;
        Since it is still not clear which variables play a major role in predicting treatment&#xD;
        termination in psychosomatic hospitals, the study design is exploratory and includes a broad&#xD;
        range of intake patient characteristics. The purpose of this study is hereby, to develop a&#xD;
        prediction model based on the information that are routinely assessed at intake. Therefore,&#xD;
        three kind of variables are planned to be included: (1) demographic and other clinical&#xD;
        variables (e.g. age, gender, ICD-10 diagnoses), (2) psychological questionnaire data (e.g.&#xD;
        PHQ, SF-12, EB-45, IIP-32, OPD-SFK), and (3) physiological data (e.g. routine laboratory&#xD;
        data, blood pressure). For the study, all patients that started inpatient psychotherapy at&#xD;
        the medical centre Heidelberg between 2015 and January 2022 will be included, resulting in a&#xD;
        sample size of approximately N = 2000. As the average dropout rate based on meta analytical&#xD;
        results is around 20%, one can assume that up to 400 patients prematurely dropped out of&#xD;
        treatment.&#xD;
  &#xD;
        To calculate the prediction model, it is planned to use a machine learning approach which is&#xD;
        highly functional in big data sets. Using a Random Forest Model for binary outcomes (regular&#xD;
        treatment length vs. premature treatment termination) it is envisioned to identify variables&#xD;
        that contribute to the prediction of premature treatment termination at intake. Additionally,&#xD;
        waiting list effects will be considered by taking into account the waiting duration between&#xD;
        the initial intake interview and the moment of the hospital admission. Therefore, the study&#xD;
        will, for the first time, investigate a prediction model for premature treatment termination&#xD;
        in inpatient psychotherapy including clinically relevant physiological data as well as&#xD;
        waiting time effects in preparation of the psychosomatic treatment.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Completed</overall_status>
    <start_date type="Actual">January 2015</start_date>
    <completion_date type="Actual">January 2022</completion_date>
    <primary_completion_date type="Actual">January 2022</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>Premature treatment termination (vs. treatment completion)</measure>
      <time_frame>Premature treatment termination will be operationalized as a dummy variable. Regular treatment duration is 8 weeks of inpatient psychotherapy. Data will be reported for 7 years of continuous study enrolment (01/2015 - 01/2022).</time_frame>
      <description>Premature treatment termination will be classified based on the treatment duration. Classification will be made retrospectively for each patient based on the duration of the inpatient treatment and if applicable (duration &lt; 49 days) on the hospital discharge letter to screen for reasons of the shorter treatment duration.</description>
    </primary_outcome>
    <enrollment type="Actual">2023</enrollment>
    <condition>Premature Treatment Termination</condition>
    <condition>Dropout Prediction</condition>
    <condition>Inpatient Psychotherapy</condition>
    <condition>Machine Learning</condition>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>Psychotherapy</intervention_name>
      <description>Patients treated at the inpatient psychotherapy unit of the University Hospital receive 8 to 10 weeks of multimodal psychotherapeutic treatment. Treatment consists of individual as well as group psychodynamic therapy. Additionally, patients receive an individual combination of music, art, relaxation and body-oriented group therapy. Therapeutic treatment is provided by a multiprofessional, interdisciplinary team of psychotherapists with either a medical or psychology degree, art and music therapists, specialist nurses, social workers, and physiotherapists.</description>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          The study population consists of patients being treated at the inpatient unit of University&#xD;
          Hospital Heidelberg, department for psychosomatic medicine.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  patients of at least 18 years of age&#xD;
  &#xD;
            -  included in inpatient psychotherapy treatment program in a hospital for psychosomatic&#xD;
               medicine&#xD;
  &#xD;
            -  provided information about admission and discharge date&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  bipolar, acute psychotic or substance abuse disorder&#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>Ulrike Dinger-Ehrenthal, Prof. Dr.</last_name>
      <role>Study Director</role>
      <affiliation>Department of Psychosomatic Medicine and Psychotherapy, Medical Faculty, Heinrich-Heine University Düsseldorf</affiliation>
    </overall_official>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 11, 2023</study_first_submitted>
    <study_first_submitted_qc>September 15, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 18, 2023</study_first_posted>
    <last_update_submitted>September 15, 2023</last_update_submitted>
    <last_update_submitted_qc>September 15, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 18, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>University Hospital Heidelberg</investigator_affiliation>
      <investigator_full_name>Simone Jennissen</investigator_full_name>
      <investigator_title>Principal Investigator</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Premature Birth</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
      <ipd_description>Individual participant data will not be shared because study data is provided by mental health patients and is therefore subject to strict protection.</ipd_description>
    </patient_data>
    <!-- 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/NCT06042595
  org study id: Dropout-Prediction-2023
  nct id: NCT06042595
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The study aims to develop a prediction model of premature treatment termination in
        psychosomatic hospitals using a machine learning approach.
  The aim of the study is to identify risk factors that lead to or predict premature treatment
        termination in psychosomatic hospitals. In the long-term, the study shall help to develop
        more precise prediction models that can enhance communication between therapists and patients
        about potential dropout and- if necessary- adaption of treatment in using a feedback loop.
  
        Since it is still not clear which variables play a major role in predicting treatment
        termination in psychosomatic hospitals, the study design is exploratory and includes a broad
        range of intake patient characteristics. The purpose of this study is hereby, to develop a
        prediction model based on the information that are routinely assessed at intake. Therefore,
        three kind of variables are planned to be included: (1) demographic and other clinical
        variables (e.g. age, gender, ICD-10 diagnoses), (2) psychological questionnaire data (e.g.
        PHQ, SF-12, EB-45, IIP-32, OPD-SFK), and (3) physiological data (e.g. routine laboratory
        data, blood pressure). For the study, all patients that started inpatient psychotherapy at
        the medical centre Heidelberg between 2015 and January 2022 will be included, resulting in a
        sample size of approximately N = 2000. As the average dropout rate based on meta analytical
        results is around 20%, one can assume that up to 400 patients prematurely dropped out of
        treatment.
  
        To calculate the prediction model, it is planned to use a machine learning approach which is
        highly functional in big data sets. Using a Random Forest Model for binary outcomes (regular
        treatment length vs. premature treatment termination) it is envisioned to identify variables
        that contribute to the prediction of premature treatment termination at intake. Additionally,
        waiting list effects will be considered by taking into account the waiting duration between
        the initial intake interview and the moment of the hospital admission. Therefore, the study
        will, for the first time, investigate a prediction model for premature treatment termination
        in inpatient psychotherapy including clinically relevant physiological data as well as
        waiting time effects in preparation of the psychosomatic treatment.
  observational model: Cohort
  time perspective: Retrospective
  measure: Premature treatment termination (vs. treatment completion)
  time frame: Premature treatment termination will be operationalized as a dummy variable. Regular treatment duration is 8 weeks of inpatient psychotherapy. Data will be reported for 7 years of continuous study enrolment (01/2015 - 01/2022).
  description: Premature treatment termination will be classified based on the treatment duration. Classification will be made retrospectively for each patient based on the duration of the inpatient treatment and if applicable (duration < 49 days) on the hospital discharge letter to screen for reasons of the shorter treatment duration.
  intervention type: Behavioral
  intervention name: Psychotherapy
  description: Patients treated at the inpatient psychotherapy unit of the University Hospital receive 8 to 10 weeks of multimodal psychotherapeutic treatment. Treatment consists of individual as well as group psychodynamic therapy. Additionally, patients receive an individual combination of music, art, relaxation and body-oriented group therapy. Therapeutic treatment is provided by a multiprofessional, interdisciplinary team of psychotherapists with either a medical or psychology degree, art and music therapists, specialist nurses, social workers, and physiotherapists.
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Ulrike Dinger-Ehrenthal, Prof. Dr.
  role: Study Director
  affiliation: Department of Psychosomatic Medicine and Psychotherapy, Medical Faculty, Heinrich-Heine University Düsseldorf
  responsible party type: Principal Investigator
  investigator affiliation: University Hospital Heidelberg
  investigator full name: Simone Jennissen
  investigator title: Principal Investigator
  mesh term: Premature Birth
  sharing ipd: No
  ipd description: Individual participant data will not be shared because study data is provided by mental health patients and is therefore subject to strict protection.