- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06042595
Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
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.
Study Type
Enrollment (Actual)
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- patients of at least 18 years of age
- included in inpatient psychotherapy treatment program in a hospital for psychosomatic medicine
- provided information about admission and discharge date
Exclusion Criteria:
- bipolar, acute psychotic or substance abuse disorder
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
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).
|
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.
|
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).
|
Collaborators and Investigators
Sponsor
Investigators
- Study Director: Ulrike Dinger-Ehrenthal, Prof. Dr., Department of Psychosomatic Medicine and Psychotherapy, Medical Faculty, Heinrich-Heine University Düsseldorf
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- Dropout-Prediction-2023
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
Clinical Trials on Machine Learning
-
Tang-Du HospitalCompleted
-
Singapore General HospitalNot yet recruitingMachine LearningSingapore
-
Chang Gung Memorial HospitalCompletedIntubation | Machine LearningTaiwan
-
University of North Carolina, Chapel HillBill and Melinda Gates FoundationCompletedPregnancy Related | Machine Learning | Gestational AgeUnited States, Zambia
-
University of PennsylvaniaEnrolling by invitationArtificial Intelligence | Machine Learning | Diagnostic ImagingUnited States
-
Academisch Medisch Centrum - Universiteit van Amsterdam...CompletedBlood Pressure | Machine Learning | Hemodynamic Instability | Prediction Models
-
Shanghai Zhongshan HospitalNot yet recruitingMachine Learning | Near-infrared Vision | Microcirculatory Status
-
Southeast University, ChinaRecruitingHigh-risk Patients | Risk Reduction | Machine LearningChina
-
AHEPA University HospitalGeorge Papanicolaou Hospital; University General Hospital of Heraklion; University... and other collaboratorsRecruitingArtificial Intelligence | Machine Learning | Electronic Medical RecordsGreece
-
University of California, BerkeleyCompletedPhysical Activity | Exercise | Mood | Machine Learning | Mobile HealthUnited States
Clinical Trials on Psychotherapy
-
Universidade Federal do Rio de JaneiroCompleted
-
Khushal Khan Khattak Univeristy, Karak, PakistanNot yet recruiting
-
Randi UlbergUniversity of OsloCompleted
-
Hopital MontfortThe Ottawa HospitalCompletedDepression | Parkinson's DiseaseCanada
-
Ohio State UniversityRecruitingDepression | Borderline Personality Disorder | Emotion RegulationUnited States
-
Marianne Lau, MD, DSci.The Ministry of Science, Technology and Innovation, DenmarkUnknownBulimia Nervosa (BN) | Binge Eating Disorder (BED) | Eating Disorder Not Otherwise Specified (EDNOS)Denmark
-
University of Wisconsin, MadisonNational Institute of Mental Health (NIMH)Completed
-
Istituto per la Ricerca e l'Innovazione BiomedicaIstituto di Gestalt HCC Italy - Centro Clinico e di Ricerca in Psicoterapia...Not yet recruitingRelationship, Professional PatientItaly
-
University Hospital FreiburgCompletedChronic Major Depressive DisorderGermany
-
Oslo University HospitalSouth-Eastern Norway Regional Health AuthorityUnknown