Ongoing CRIS projects: general psychiatry

Discriminating N-Methyl-D-Aspartate Receptor-antibody encephalitis (NMDAR-Ab-E) from primary psychosis

NMDAR-antibody encephalitis (NMDAR-Ab-E) is a condition in which the body mistakenly attacks a person’s NMDA receptors in the brain. These receptors are important in normal brain function including learning, memory, and normal conscious awareness. The antibodies reduce available receptors and so affect these functions. As the disease worsens breathing and heart function are affected meaning that some patients become critically unwell.

People with NMDAR-Ab-E typically present with psychiatric symptoms and the symptoms can overlap with common severe mental illnesses. This makes diagnosis challenging and NMDAR-Ab-E is commonly misdiagnosed as a primary psychosis, delaying life-saving neurological treatments. Establishing which clinical features differentiate NMDAR-Ab-E from primary psychoses will improve diagnosis and care   for both groups.

The mental state examination (MSE) is a standardised clinical interview which forms part of the assessment of psychiatric and neurological disorders. We have looked at MSE data from a cohort of NMDAR-Ab-E positive patients and coded their symptoms using a standardised coding scheme. We now wish to collect the same data for patients presenting with a first episode of primary psychosis. To do this, we will extract mental state data such as symptoms and specific behaviours from case records of a psychosis early intervention service, using expert annotators to derive the information from free-text and code it using the same coding tool . The tool is a simple list of the features being present or absent and the level of confidence for that impression. We will then compare the two groups to determine which clinical features best discriminate between these diagnoses.

CHRONOSIG P1

Aims: This research will build clinical-decision tools to support clinicians in making decisions and triaging referrals in NHS clinical practice. The tools will identify and improve access to care for people who have traditionally been excluded from, underrepresented, or poorly served by mental health services in the UK, ensuring they receive equitable care.

Background: People needing treatment for mental health problems are often first seen by their general practitioners, in hospital Emergency Departments or even in educational settings and staff then refer them to mental health services. This network of referral routes converges on community mental health teams who make decisions about suitability for treatment, how urgently to see people and which specialist teams need to be involved. In routine care, all referrals are assessed or ‘triaged’ by a multi-disciplinary team and often require the person is clinically reviewed over multiple contacts to eventually decide on treatment pathways. In 2019 this referral process cost the NHS £326 million. Electronic health records contain data from clinical encounters between patients and professionals, over multiple visits and this creates a longitudinal ‘fingerprint’ of the patient’s mental health over time. These clinical records are a rich source of information, which is particularly pertinent to mental health because mental illness is largely expressed as language and behaviour and the primary tool for the clinician is the interview, recorded as clinical notes and correspondence. However, this information is voluminous and time consuming to process (even by expert clinicians) when triaging referrals. Recent advances in artificial intelligence and natural language processing will help unlock this information to make triage more efficient and access to care more robust.

Work plan: Using a decade’s worth of observational data from the largest collection of the secondary care mental health electronic health records in the UK alongside state-of-the art neural network algorithms for natural language processing, we will develop, train and validate models capable of characterising a patient’s trajectory directly from clinical notes. The model’s effectiveness, safety and efficiency will be evaluated in simulated multidisciplinary triage teams at four NHS Foundation Trusts for pre-clinical use.

Patient and public involvement: Through the Patient and Public Involvement group of the Oxford Health Biomedical Research Centre, patients, carers and clinicians will be involved throughout with regular meetings for co-producing the project scope, milestones and evaluating the clinical efficacy, safety and ethical boundaries of the clinical decision support tools.  Deliverables: An intelligent operational automation tool, based on a longitudinal patients’ trajectory derived from historical observations, will help streamline the process of being referred to an NHS mental health team. It will also help identify people with the highest care needs and suitable for services and clinical trials.

Clozapine NLP

Clozapine is the most effective treatment for patients with schizophrenia who have not improved on other antipsychotic drugs. Previous research has shown that clozapine remains an under-used option in the treatment of schizophrenia. Surveys among UK psychiatrists point to the difficulty in identifying the right patients to offer clozapine to. Here we propose to use the Oxford Health UK CRIS Database to develop a way to identify patients who are potential candidates for clozapine and also their likelihood for treatment success.

The results of this study could lead to the development of a software that alerts clinicians to patients under their care that can be offered clozapine treatment. The software could also give an indication of how likely patients are to benefit from the drug or to suffer significant side effects – this can help guide the conversation between psychiatrist and patient on whether to start the drug.

Prescribing gabapentinoids

Gabapentin and pregabalin (together called the ‘gabapentinoids’) are drugs prescribed and taken by many people in the US and UK. Common reasons for taking these medications are to decrease anxiety, reduce epileptic fits/seizures and alleviate pain related to nerve damage. However, often these medications are prescribed for disorders in which they have not been licensed (i.e. passed formal medical regulatory approval and recommendation for use). For instance, in patients with bipolar disorder the gabapentinoids are given to help with mood improvement, but there is no good evidence to suggest that they really work. Importantly, studies have shown that these medications can have troublesome side effects, some of which are potentially dangerous. These include dizziness, drowsiness, difficulty concentrating, blurred vision, movement disturbances and accidental injury. Furthermore, there is an increased risk of death by overdose when taken with drugs such as opioids (which include commonly prescribed painkillers such as codeine and illegal drugs such as ‘heroin’). For these reasons, the gabapentinoids were labelled as ‘controlled’ substances in the UK in 2019. This means that they are subject to more stringent measures and monitoring in clinical practice.

By conducting this study, we would like to examine the overall trends in the usage of pregabalin and gabapentin in real-world NHS practice, using the Oxford Health CRIS platform. This platform allows researchers to conduct patient-level research without being able to identify patients (hence maintaining individual anonymity). We also hope to demonstrate whether the 2019 regulatory change made an impact on the use of these medications.

Chronosig P3

Aims: We will build clinical-decision tools to support clinicians in making decisions and triaging referrals in NHS clinical practice. The tools will identify and improve access to care for people who have traditionally been excluded from, underrepresented, or poorly served by mental health services in the UK, ensuring they receive equitable care.

Background: People needing treatment for mental health problems are often first seen by their general practitioners, in hospital Emergency Departments or even in educational settings and staff then refer them to mental health services. This network of referral routes converges on community mental health teams who make decisions about suitability for treatment, how urgently to see people and which specialist teams need to be involved. In routine care, all referrals are assessed or ‘triaged’ by a multi-disciplinary team and often require the person is clinically reviewed over multiple contacts to eventually decide on treatment pathways. In 2019 this referral process cost the NHS £326 million. Electronic health records contain data from clinical encounters between patients and professionals, over multiple visits and this creates a longitudinal ‘fingerprint’ of the patient’s mental health over time. These clinical records are a rich source of information, which is particularly pertinent to mental health because mental illness is largely expressed as language and behaviour and the primary tool for the clinician is the interview, recorded as clinical notes and correspondence. However, this information is voluminous and time consuming to process (even by expert clinicians) when triaging referrals. Recent advances in artificial intelligence and natural language processing will help unlock this information to make triage more efficient and access to care more robust.

Work plan: Using a decade’s worth of observational data from the largest collection of the secondary care mental health electronic health records in the UK alongside state-of-the art neural network algorithms for natural language processing, we will develop, train and validate models capable of characterising a patient’s trajectory directly from clinical notes. The model’s effectiveness, safety and efficiency will be evaluated in simulated multidisciplinary triage teams at four NHS Foundation Trusts for pre-clinical use.

Patient and public involvement: Through the Patient and Public Involvement group of the Oxford Health Biomedical Research Centre, patients, carers and clinicians will be involved throughout with regular meetings for co-producing the project scope, milestones and evaluating the clinical efficacy, safety and ethical boundaries of the clinical decision support tools.

Deliverables: An intelligent operational automation tool using medical information extracted from historical clinical records, will help streamline the process of being referred to an NHS mental health team. It will also help identify people with the highest care needs and suitable for services and clinical trials.

Service Economic Evaluation

The NHS is constantly re-organising in order to meet the different care needs of individuals. However, to maximise efficiencies, prioritise programmes and improve services, a data review is required. This project will extract and examine how much the data collected by NHS Trusts as part of usual care can be used to inform and improve NHS services. This would include an explorative evaluation of Early Intervention in Psychosis services to understand what areas of data collection need to improve and what valuable research is possible at the moment.

Looked after children (LAC)

In the UK, child and adolescent mental health services have an upper age limit of late teens for providing support. Meaning that In general if young people reach this age limit but still require support they are transferred to adult mental health services. However, previous research has found that for many young people this does not happen, with young people slipping through the care net despite ongoing needs. This happens at an age when mental health difficulties are most likely to emerge. People who grow up in care (e.g., foster homes) are a particularly vulnerable group, and are more likely than the general population to have mental health difficulties. These difficulties are often more severe and last for a much longer time than people have in the general population. When young people in care reach the upper age boundary of child and adolescent mental health services, they also reach the age at which they ‘age out’ of the care system. This means that at a time when they are potentially most vulnerable they are often moving into independent or semi-independent homes at the same time as switching mental health support services.

This study aims to look at how NHS mental health services manage the transition to adult mental health services for young people in care. To do this, we will track the use of mental health support for a historical cohort of young people in care as they reach this boundary, and in the years following. This will help us identify areas for improvement in mental health services so that we are able to better support care-experienced people during this time.

RECOLLECT

This project is part of the larger RECOLLECT Programme Grant for Applied Research awarded by the NIHR funding collaborative, multidisciplinary programmes of applied research to solve health and social care challenges. (NIHR200605) which aims to develop the evidence base for Recovery Colleges in England and understand how they can provide the most benefit to people who use mental health services. We will compare Recovery College students with mental health service users who do not attend the Recovery College in order to explore the effectiveness and cost-effectiveness of Recovery Colleges.

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Page last reviewed: 4 April, 2023