PRONIA
Research type
Research Study
Full title
Personalised Prognostic Tools for Early Psychosis Management
IRAS ID
140271
Contact name
Stephen Wood
Contact email
Sponsor organisation
University of Birmingham
Duration of Study in the UK
5 years, 0 months, 0 days
Research summary
Affective and non-affective psychoses have a major negative impact on human society, account for a large percentage of the global burden of disease and have a high socioeconomic burden. This burden is largely caused by two core features of affective and non-affective psychotic illnesses, their onset in adolescence and early adulthood and their long-term disabling courses and outcomes. Both factors lead to enduring social and vocational exclusion and contribute to 8-20 times higher suicide rates in effected patients.
Reliable and broadly accessible prognostic tools will significantly alleviate this disease burden by enabling individualised risk prediction, thus paving the way to the targeted prevention of psychoses. However, to date no reliable prediction tools have been developed. Therefore, the aim of this study is to use routine brain imaging and complementary data to optimise candidate biomarkers for the prediction and staging of psychoses, and to generate a prognostic system that generalises well across European mental health services. We will develop and validate new multi-modal risk quantification tools to reliably predict mental health-related disability in young help-seeking persons. The fusion of these novel prognostic tools with human clinical knowledge will produce cybernetic prognostic services that accurately identify help-seekers at the highest risk of psychotic illnesses, poor functioning and suicide-related mortality.
To achieve these goals, we aim to collect multivariate data (including structural and functional neuroimaging data, as well as clinical, genomic and metabolic information) from four different samples of participants aged 16-40 years: those with recent onset psychosis, young people at-risk for psychosis, individuals with recent-onset depression, and healthy controls. Participants will be followed up at multiple time points over the next 18 months to allow for the monitoring of symptom development. Their data will then undergo complex machine learning algorithms to develop reliable prediction tools.
REC name
West Midlands - Coventry & Warwickshire Research Ethics Committee
REC reference
14/WM/0019
Date of REC Opinion
26 Feb 2014
REC opinion
Further Information Favourable Opinion