Abstract

Background: 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. Methods: 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. Aims: 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. Results: 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. Discussion: 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.