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Design and methods of the research unit 5187 PREACT (towards precision psychotherapy for non-respondent patients: from signatures to predictions to clinical utility) – a study protocol for a multicentre observational study in outpatient clinics
Study design for improving personalized psychotherapy by identifying patient patterns to predict treatment outcomes in outpatient clinics
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Abstract
A cohort of 585 patients with internalising disorders is being recruited to investigate to cognitive-behavioural therapy.
- Less than 50% of patients with internalising disorders achieve clinically meaningful improvement after cognitive-behavioural therapy.
- The study aims to identify bio-behavioural signatures associated with treatment non-response (TNR).
- Emotion regulation is considered a key mechanism in both cognitive-behavioural therapy and treatment non-response.
- State-of-the-art machine learning methods will be utilized for single-patient predictions based on high-dimensional neuroimaging data.
- The research seeks to enhance precision psychotherapy by establishing optimal predictors of treatment outcomes.
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Key numbers
585 patients
Cohort Size
Patients with internalizing disorders recruited across four outpatient clinics.
75%
Prediction Accuracy Goal
Targeted accuracy for predicting using machine learning.
50%
Rate
Less than half of patients with internalizing disorders show clinically meaningful improvement with .