Ignite Creation Date:
2025-12-24 @ 1:59 PM
Ignite Modification Date:
2025-12-31 @ 3:15 PM
Study NCT ID:
NCT06042595
Status:
COMPLETED
Last Update Posted:
2023-09-18
First Post:
2023-08-11
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'interventionBrowseModule': {'meshes': [{'id': 'D011613', 'term': 'Psychotherapy'}], 'ancestors': [{'id': 'D004191', 'term': 'Behavioral Disciplines and Activities'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 2023}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2015-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-09', 'completionDateStruct': {'date': '2022-01', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2023-09-15', 'studyFirstSubmitDate': '2023-08-11', 'studyFirstSubmitQcDate': '2023-09-15', 'lastUpdatePostDateStruct': {'date': '2023-09-18', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-09-18', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-01', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Premature treatment termination (vs. treatment completion)', 'timeFrame': '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.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Premature Treatment Termination', 'Dropout Prediction', 'Inpatient Psychotherapy', 'Machine Learning']}, 'descriptionModule': {'briefSummary': 'The study aims to develop a prediction model of premature treatment termination in psychosomatic hospitals using a machine learning approach.', 'detailedDescription': '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.\n\nSince 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.\n\nTo 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study population consists of patients being treated at the inpatient unit of University Hospital Heidelberg, department for psychosomatic medicine.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* patients of at least 18 years of age\n* included in inpatient psychotherapy treatment program in a hospital for psychosomatic medicine\n* provided information about admission and discharge date\n\nExclusion Criteria:\n\n* bipolar, acute psychotic or substance abuse disorder'}, 'identificationModule': {'nctId': 'NCT06042595', 'briefTitle': 'Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach', 'organization': {'class': 'OTHER', 'fullName': 'University Hospital Heidelberg'}, 'officialTitle': 'Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach', 'orgStudyIdInfo': {'id': 'Dropout-Prediction-2023'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Psychotherapy', 'type': 'BEHAVIORAL', '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.'}]}, 'contactsLocationsModule': {'overallOfficials': [{'name': 'Ulrike Dinger-Ehrenthal, Prof. Dr.', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Department of Psychosomatic Medicine and Psychotherapy, Medical Faculty, Heinrich-Heine University Düsseldorf'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'Individual participant data will not be shared because study data is provided by mental health patients and is therefore subject to strict protection.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University Hospital Heidelberg', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Simone Jennissen', 'investigatorAffiliation': 'University Hospital Heidelberg'}}}}