Clinical trial NCT06042595: Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach
Official title: Predicting Premature Treatment Termination in Inpatient Psychotherapy: A Machine Learning Approach
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Premature Treatment Termination
Dropout Prediction
Inpatient Psychotherapy
Machine Learning
Lead sponsor: University Hospital Heidelberg
Sponsor class: OTHER
First posted: 2023-09-18
Enrollment: 2023
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study population consists of patients being treated at the inpatient unit of University Hospital Heidelberg, department for psychosomatic medicine.

Brief summary: 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.

Eligibility criteria: 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

Primary outcomes:
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).
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: Department of Psychosomatic Medicine and Psychotherapy, Medical Faculty, Heinrich-Heine University Düsseldorf
name: Ulrike Dinger-Ehrenthal, Prof. Dr.
role: STUDY_DIRECTOR
References: