Premature Treatment TerminationNCT06042595
Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach
An observational study of Premature Treatment Termination and Dropout Prediction, run by University Hospital Heidelberg.
Trial summary
The study aims to develop a prediction model of premature treatment termination in psychosomatic hospitals using a machine learning approach. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Premature Treatment Termination, Dropout Prediction, Inpatient Psychotherapy
- What is being tested?
- Psychotherapy
- Type of treatment
- Lifestyle or therapy programme
- How many people
- 2,023 participants
- Where
- No sites listed yet
- Study dates
- Jan 2015to Jan 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of patients being treated at the inpatient unit of University Hospital Heidelberg, department for psychosomatic medicine.
Who may be able to join
- 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
Who cannot join
- bipolar, acute psychotic or substance abuse disorder
About this study
The study aims to develop a prediction model of premature treatment termination in psychosomatic hospitals using a machine learning approach.
Read the 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.
- Conditions
- Premature Treatment Termination, Dropout Prediction, Inpatient Psychotherapy, Machine Learning
- Being tested
- Psychotherapy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PsychotherapyBehavioural
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
Premature treatment termination (vs. treatment completion)
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.
Measured: 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).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Sep 2023
- Study startJan 2015
- Primary completionJan 2022
- Study completionJan 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Premature Treatment TerminationStudies of the same condition, open and under way.
- More trials run by University Hospital HeidelbergThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 2,023 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Hospital Heidelberg
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Simone Jennissen, University Hospital Heidelberg
- Investigators
- Ulrike Dinger-Ehrenthal, Prof. Dr., Study director, Department of Psychosomatic Medicine and Psychotherapy, Medical Faculty, Heinrich-Heine University Düsseldorf
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06042595
- Sponsor's protocol ID
- Dropout-Prediction-2023
- Status last verified
- Sep 2023
- First submitted
- 11 Aug 2023
- Last updated
- 18 Sep 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared because study data is provided by mental health patients and is therefore subject to strict protection.
Study at a glance
From the registry record, last updated by the sponsor on 18 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach
- ClinicalTrials.gov ID
- NCT06042595
- Acronym / sponsor ID
- Dropout-Prediction-2023
- Conditions
- Premature Treatment Termination, Dropout Prediction, Inpatient Psychotherapy, Machine Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,023 participants (actual)
- Lead sponsor
- University Hospital HeidelbergUniversity, hospital or other
- Study start
- Jan 2015 (actual)
- Primary completion
- Jan 2022 (actual)
- Study completion
- Jan 2022 (actual)
- First posted
- 18 Sep 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The study aims to develop a prediction model of premature treatment termination in psychosomatic hospitals using a machine learning approach.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of patients being treated at the inpatient unit of University Hospital Heidelberg, department for psychosomatic medicine.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (1 listed)
- bipolar, acute psychotic or substance abuse disorder
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| PsychotherapyPatients 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. | Behavioural | – |
Primary outcome
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Sep 2023.
- Individual participant dataSharing planned: No. Individual participant data will not be shared because study data is provided by mental health patients and is therefore subject to strict protection.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ulrike Dinger-Ehrenthal, Prof. Dr. study director
Department of Psychosomatic Medicine and Psychotherapy, Medical Faculty, Heinrich-Heine University Düsseldorf
Questions about this trial
What is this clinical trial studying?
An observational study of Premature Treatment Termination and Dropout Prediction, run by University Hospital Heidelberg.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 18 Sep 2023. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome 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).). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Sep 2023.