First PRONIA Results
Summary description of project context and objectives
Reliable and accessible tools for an individualised early recognition of psychoses are increasingly at the centre stage of international mental healthcare policy and research. This is due to the fact that affective and non-affective psychoses typically commence in the most productive and critical period of life – late adolescence and early adulthood – frequently entailing long-term disability and increased mortality in the affected patients. These factors drive the 6.3% of the global burden of disease caused by these disorders and account for the €207 billion per year in Europe alone spent due to direct and indirect costs. These figures emphasise that psychoses are the most expensive brain-related disorders, with a similar health-economic impact as cardiovascular diseases.
In consequence, a significant component in the reduction of this burden is to provide preventive treatments to those at highest risk of developing these devastating illnesses in the future. However, these targeted interventions will only become regularly and widely available if quantifiable biological markers of the emerging disease and its impact on a patient’s long-term functional outcome will become part of the normal diagnostic workflows in clinical psychiatry. The characterisation of these markers could also have a significant impact on the development of novel preventive interventions by e.g. providing new biological surrogate markers and quantification methods to the drug discovery pipelines of the pharmaceutical industry.
Objectives: Therefore, the multi-centre collaborative project PRONIA (“Personalised Prognostic Tools for Early Psychosis Management”) has been awarded with €6,000,000 FP7 grant of the European Commission to first (i) explore the utility of routine brain imaging and complementary data in predicting different mental health-related outcomes in persons with at-risk and early stages of psychoses, and (ii) evaluate which predictive signatures in these data generalise well across different mental health services and pathways to care. Secondly, based on the knowledge gained through this biomarker validation process, PRONIA will implement new multi-modal risk quantification tools and embed them into an eHealth prototype providing telepsychiatric services along with biomarker-based risk stratification tools to accurately predict mental health-related disability in young help-seeking persons. More specifically, we will:
- Optimise the PRONIA consortium’s candidate imaging markers for a clinically reliable prediction and staging of psychoses by augmenting them with complementary patient data and generalising them across mental health services on the basis of cross-centre, multi-modal pattern recognition (COMPARE).
- Develop & validate new surrogate markers for an individualised risk quantification by analysing brain imaging and complementary data with COMPARE in order to
- Disseminate & commercially exploit these surrogate markers by delivering cybernetic prognostic services to health services, research institutions and the biopharmaceutical industry through a telemedicine-based European company.
The third objective expresses PRONIA’s ultimate goal to realise licensing, commercialisation and sustained engineering strategies of these biomarker-based early recognition services through broadly available telemedicine applications. This will provide psychosis risk profiling tools to diverse target groups in the healthcare markets, including care-givers, the pharmaceutical industry and research institutions. By disseminating objective risk quantification, PRONIA’s products will provide firm diagnostic grounds for preventive therapy, improving outcomes and reducing costs. Thus, they will offer a unique selling proposition to the mental health sectors in Europe and beyond.
Work performed since the beginning of the project and the main results achieved so far
WP1 started developing standardised clinical examination workflows in 07/2013. Until 12/2013 these workflows were fully digitalised in the PRONIA portal allowing for centralised data acquisition and management (see WP9). Mainly due to the local heterogeneity of ethical and data security approval processes, recruitment start was deferred to a locally varying degree, ranging from the 15/02/2014 (LMU) to the 29/07/2014 (University of Birmingham). Thus, the originally cumulative recruitment time of 81 months (until 03/2015) was reduced to 71 months. During this time, the whole consortium screened 1105 participants. This resulted in 484 participants included in the study. Importantly, a QC process was set up consisting of (1) monthly telephone case conferences to evaluate the caseness of CHR participants, (2) quarterly FAQ conferences to train clinical raters, and (3) biannual inter-rater reliability testing, which demonstrated the high reproducibility of our clinical assessment workflows.
WP2 together with WP11 implemented a certification framework for the fully modular and object-based re-engineering and extension of the project’s machine learning platform NeuroMiner. Conceptualisation of NeuroMiner2 (NM2) and its interfaces started in October 2013 paralleled by an in-depth documentation of the program’s functions and capabilities. Subsequently, the programming of NM2 modules commenced in 01/2014. In 01/2015 we started to test the program on non-PRONIA data to further improve its computational efficiency. The pilot data analysis phase will be started in 06/2015.
WP3 defined a harmonised MRI framework to guarantee standardised cross-site management of MRI protocols and data. This included (1) local and consortium-wide MRI parameters and SOPs for harmonised, but not over-calibrated data acquisition, (2) the development of a software tool for anonymizing, organizing and uploading the participants’ MRI data to the PRONIA portal, and (3) the implementation of centralised MRI processing and QC algorithms. These activities were complemented by a successful PRONIA calibration study, which generated complete MRI sets for 6 HC subjects, who travelled to all sites.
WP4 finalised a computerised PRONIA multi-domain neuropsychological battery in December 2013 that allows the study participants to be tested on tablet PCs connected to the PRONIA portal. QC measures ascertained a high quality and inter-rater reliability of the neurocognitive data acquisition across all PRONIA sites.
WP5 implemented the blood sampling SOPs, including (1) the provision of standardised blood collection kits, (2) local workflows, (3) shipment and storage at the Helmholtz centre. Based on this blood sampling infrastructure we have so far processed samples from 248 study participants. Genetic characterisation of these samples started based on the recent genome-wide significant hits published by the Psychiatric Genetics Consortium.
WP8 developed a MRI spatial distortion tool for DTI that corrects EPI distortions by reducing eddy-current and motion artifacts related to diffusion gradients in different spatial directions. Furthermore, WP8 identified technical, physiological and morphological parameters causing structural distortions and developed a new fMRI method 3D RUFIS, which does not have geometrical distortions and provides silent MRI acquisitions. Comparative testing of different rs-fMRI sequences and 3DRUFIS was carried out.
WP9 set up an internet-accessible database that implements fully digitalised clinical questionnaires and workflows and interfaces with the MRI and neuropsychological data acquisition tools. Finally, WP9 started working on a web-based PRONIA@home interface that will enable the telepsychiatric evaluation of help-seeking individuals.
WP10 developed a new strategy for the external validation of PRONIA’s prognostic prototype after the projected funding of the Melbourne site was cut by 60%. Therefore, collaborations with the EU-FP7 project PSYSCAN were initiated to create recruitment and analysis synergies in Melbourne. Furthermore, PRONIA entered negotiations with the US projects NAPLS 3 and PNC to implement a framework for external validation and harmonised data acquisition. These efforts led to the submission of a joint proposal at NIMH to support the infrastructure of this collaboration.
WP11 established strategies to monitor the consortium’s freedom-to-operate (f-t-o), to manage IP rights within PRONIA and with respect to patent authorities, and to develop a commercialisation roadmap for the project. A first patent application (PCT/EP2014/002154) was filed on 5 August 2014 and is presently pending. Furthermore, WP11 provided support to WP2 & 3 to guide software development toward certification in the sense of the Medical Device Directive 93/42/EC.
WP12 implemented a project management plan for PRONIA including an internal communication strategy. In addition to the project meetings, regular phone conferences were carried out to discuss actual progress; :milliarium, a web-based project management and communication tool, serves as a central platform for important documents and the documentation of the work progress. Dissemination material has been provided, including a project logo, a project website, leaflets and presentation templates.
The expected final results and their potential impact and use (including the socio-economic impact and the wider societal implications of the project so far)
With great care the project also conceptualised and pushed forward the implementation of the advanced bioinformatics platform NeuroMiner2 which will extract robust multi-modal biomarker signatures from the rich sets of imaging-based, clinical, neuropsychological and genetic data collected so far. This progress also includes the development of centralised and robust data processing streams, which – together with the predictive models – will provide the candidate building blocks of the final prognostic system. The impact of this generic bioinformatics processing and prediction framework extends beyond the psychosis field as it will allow identifying and validating treatment-relevant neurobiological signatures for major neuropsychiatric diseases in the increasingly high-dimensional data spaces generated by the life sciences.