Positive symptoms and course of illness in people with first episode schizophrenia

Lead Supervisor
Dr Margaret Heslin
Research FellowHealth Services and Population Department, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London
margaret.heslin@kcl.ac.uk

Co-supervisors
Dr Rashmi Patel & Professor Rob Stewart (Joint second)
Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London

Project Details

Schizophrenia is a debilitating severe mental illness with substantial impacts on individuals across the life-course. Course of illness shows high individual variability from distinct, individual episodes followed by prolonged recovery in some people, to a long-term and highly disabling course in others. 

Examining factors predicting outcomes in people with schizophrenia is clearly important: not only because this advances our understanding of the underlying disorder, but also because findings could provide potential targets for interventions. Despite the importance and variability of outcomes in schizophrenia, little is known about the differentiating role of positive symptoms (hallucinations, delusions, formal thought disorder and bizarre behaviour) and positive symptom clusters in prognosis following the onset of disorder. Work that has been conducted in this area has had substantial limitations including measuring positive symptoms at a single time point by researchers using psychometric scales, plus defining and observing outcomes at a single time point using outcome scales. This misses the important detail of symptom presentation gathered and recorded by clinicians and ignores the complexity of defining recovery. 

This project aims to investigate associations between positive symptoms of schizophrenia and course of illness, using the breath and depth of information clinically collected to identify and define both the expose and outcome. 

A historic cohort of incident cases with schizophrenia will be identified through a de-identified mental health Electronic Health Records (EHRs). Natural language processing (NLP) techniques will be used to extract information on positive symptoms within the first six months of presentation to services. Cases will be followed up using these records over 5 years. Administrative, clinical and functional data will be combined and used to define outcome in a more nuanced way.

Informal training in the use of electronic health records and natural language processing will be provided as well as formal courses on the same topics built into the training programme. The student will be based at the BRC Nucleus – a dedicated office suite for clinical informatics work funded by local trustees which brings together researchers and technical staff to maximise collaboration and support. They will thus be surrounded by a range of colleagues with expertise in informatics, psychiatry, psychology, social science, statistics, and epidemiology to name but a few, and will be part of a collaborative team working on the use of electronic health records and informatics in mental health research.

Datasets

This project will use Electronic Health Records (EHRs) from the South London and Maudsley (SLaM) NHS Trust Foundation electronic Patient Journey System (ePJS). Since 2006, comprehensive health records from over 280,000 patients in the ePJS have been de-identified and made accessible via the Clinical Record Interactive Search tool (CRIS). CRIS holds all information documented by professionals involved in the provision of specialist mental health care for all people in contact with SLaM mental healthcare services from 1 January 2007 to date [16]. SLaM covers the four London boroughs of Lambeth, Southwark, Lewisham and Croydon.

Ethical approval was granted for the use of CRIS as a secondary database in 2008 and renewed in 2013 (Oxford C Research Ethics Committee, reference 08/H0606/71+5).

KeywordsSchizophrenia, first episode psychosis, positive symptoms