# PERMEPSY: a multicentre, randomized, double-blind proof-of-concept trial of personalized metacognitive training for adults with psychosis — a study protocol

## TL;DR

This study tests a personalized version of metacognitive training for psychosis using machine learning to improve treatment outcomes.

## Contribution

The study introduces a machine learning platform to personalize metacognitive training for psychosis treatment.

## Key findings

- A machine learning algorithm was developed to predict clinical outcomes and guide personalized treatment.
- The trial will compare personalized MCT to standard MCT in improving symptoms and cognitive impairments.
- The study aims to reduce the burden of psychosis through tailored, technology-assisted interventions.

## Abstract

While psychological interventions are effective at improving symptoms of psychosis, accessible, cost- and time-efficient treatments remain limited. Personalized medicine has emerged as a promising approach, tailoring interventions to individual needs. Metacognitive Training (MCT), with its established efficacy and adaptable format, is well-suited for personalization. The PERMEPSY project (Towards a Personalized Medicine Approach to Psychological Treatment for Psychosis) aims to deliver tailored MCT intervention for individuals with psychosis.

PERMEPSY is an international study funded by ERAPerMed (JTC2022) involving five clinical partners (Spain, Chile, France, Germany, Poland) and one technological partner (Spain). The project involves a proof-of-concept clinical trial recruiting 51 participants from each center for a total of 255 adult participants with psychosis in a prospective study (Registration: NCT06603922, 19-09-2024). The trial will test the efficacy of a Machine Learning (ML)-derived platform at predicting clinical and functional outcomes from baseline scores and compare a personalized MCT (P-MCT) to a classical MCT based on the platform’s predictions.

PERMEPSY seeks to (1) develop and test the predictive power of an algorithm that could support decision-making, and (2) ascertain whether P-MCT is more effective than MCT at improving key symptoms and cognitive impairments associated to psychosis.

A harmonized retrospective database enabled the development of a predictive ML algorithm, integrated into an innovative platform. This platform provides clinicians with the information needed to deliver P-MCT. Predictions include changes in positive symptoms (e.g., delusions), insight, self-esteem, and treatment adherence.

By integrating diverse data types and innovative technology, PERMEPSY addresses the need for personalized, effective treatment in psychosis, aiming to reduce individual and systemic burdens while supporting clinicians in their decision-making.