HORIZON-HLTH-2021-STAYHLTH-01-02

Fonte Standard Gras
Identifiant

Unité de recherche

SEFRI

Numéro de projet

23.00231

Titre du projet

HORIZON-HLTH-2021-STAYHLTH-01-02

Textes relatifs à ce projet

	Allemand	Français	Italien	Anglais
Résumé des résultats (Abstract)	-	-	-

Textes saisis

Catégorie	Texte
Résumé des résultats (Abstract) (Anglais)	A key problem in Mental Health is that up to one third of patients suffering from major mental disorders develop resistance against drugtherapy. However, patients showing early signs of treatment resistance (TR) do not receive adequate early intensive pharmacologicaltreatment but instead they undergo a stepwise trial-and-error treatment approach. This situation originates from three major knowledgeand translation gaps: i.) we lack effective methods to identify individuals at risk for TR early in the disease process, ii.) we lack effective,personalized treatment strategies grounded in insights into the biological basis of TR, and iii.) we lack efficient processes to translatescientific insights about TR into clinical practice, primary care and treatment guidelines. It is the central goal of PSYCH-STRATA tobridge these gaps and pave the way for a shift towards a treatment decision-making process tailored for the individual at risk for TR. Tothat end, we aim to establish evidence-based criteria to make decisions of early intense treatment in individuals at risk for TR across themajor psychiatric disorders of schizophrenia, bipolar disorder and major depression. PSYCH-STRATA will i.) dissect the biological basisof TR and establish criteria to enable early detection of individuals at risk for TR based on the integrated analysis of an unprecedentedcollection of genetic, biological, digital mental health, and clinical data. ii.) Moreover, we will determine effective treatment strategies ofindividuals at risk for TR early in the treatment process, based on pan-European clinical trials in SCZ, BD and MDD. These efforts willenable the establishment of novel multimodal machine learning models to predict TR risk and treatment response. Lastly, iii.) we willenable the translation of these findings into clinical practice by prototyping the integration of personalized treatment decision supportand patient-oriented decision-making mental health boards.

Catégorie

Texte

Résumé des résultats (Abstract)
(Anglais)

A key problem in Mental Health is that up to one third of patients suffering from major mental disorders develop resistance against drugtherapy. However, patients showing early signs of treatment resistance (TR) do not receive adequate early intensive pharmacologicaltreatment but instead they undergo a stepwise trial-and-error treatment approach. This situation originates from three major knowledgeand translation gaps: i.) we lack effective methods to identify individuals at risk for TR early in the disease process, ii.) we lack effective,personalized treatment strategies grounded in insights into the biological basis of TR, and iii.) we lack efficient processes to translatescientific insights about TR into clinical practice, primary care and treatment guidelines. It is the central goal of PSYCH-STRATA tobridge these gaps and pave the way for a shift towards a treatment decision-making process tailored for the individual at risk for TR. Tothat end, we aim to establish evidence-based criteria to make decisions of early intense treatment in individuals at risk for TR across themajor psychiatric disorders of schizophrenia, bipolar disorder and major depression. PSYCH-STRATA will i.) dissect the biological basisof TR and establish criteria to enable early detection of individuals at risk for TR based on the integrated analysis of an unprecedentedcollection of genetic, biological, digital mental health, and clinical data. ii.) Moreover, we will determine effective treatment strategies ofindividuals at risk for TR early in the treatment process, based on pan-European clinical trials in SCZ, BD and MDD. These efforts willenable the establishment of novel multimodal machine learning models to predict TR risk and treatment response. Lastly, iii.) we willenable the translation of these findings into clinical practice by prototyping the integration of personalized treatment decision supportand patient-oriented decision-making mental health boards.