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.

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.

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.

Auditory Schizophrenia vs EUPD

Differentiating between auditory hallucinations caused by Schizophrenia and Emotionally Unstable Personality Disorder (EUPD) is a frequent challenge in psychiatry; doing so accurately is vital as the treatments are very different. Despite this, there is a lack of evidence on how to differentiate these hallucinations, with numerous conflicting results.

One of the main reasons results have been so conflicting is that previous studies have reviewed only a very small number of patients. By using CRIS, we hope to review the records of a far larger number of patients, making us more able to detect any differences in the hallucinations that are present.

To this end, we have created several variables which describe aspects of auditory hallucinations that previous studies have suggested may differentiate EUPD and Schizophrenia, and that should be recorded in clinical notes. We will review the records of 300 patients with each diagnosis and record their hallucination in several descriptive categorical variables (see the table above for details). These can then be used to quantitatively compare whether there is any descriptive difference in auditory hallucinations between EUPD and Schizophrenia, and which variables independently drive that effect.

Child Refugee

Refugee and asylum-seeking children and young people often struggle with mental health problems. Many studies have shown an urgent need for treatments that address mental health problems in this population. However, refugee and asylum-seeking children and young people face significant barriers when trying to access the support they need. These barriers can include language barriers, cultural differences in conceptualisation of mental health, limited resources, and difficulties navigating the healthcare system in a new country. Even when they do manage to access treatment, there is still a lack of understanding about the specific mental health symptoms and problems they face as well as the treatment that is offered and received. This knowledge gap makes it challenging for healthcare professionals to provide targeted and effective support.

ClinicalInstabilityPredictorHonos

The clinical global impressions scale (CGI) is a very brief assessment of a patient’s mental health. It asks one question to the clinician asking how mentally unwell a patient is at the visit, rated from 0-7 (with 0 being not at all mentally unwell and 7 being among the most unwell patients). A previous study published in Lancet Psychiatry last year (https://doi.org/10.1016/S2215-0366(23)00066-4) showed that patients whose CGI scores fluctuated more were more likely to be admitted to hospital than those who were more stable.

If this concept holds true, it should be applicable to other measures, like the Health of the Nation Outcome Scales (HoNOS), which is used routinely in the Trust. HoNOS has clinicians rating a patient from 0-4 on 12 domains measuring behaviour, impairment, symptoms and social functioning. We want to test whether instability in HoNOS scores can predict how likely a patient is of being admitted to hospital.

Transition to Schizophrenia

This project aims to predict patients’ likelihood of developing schizophrenia after experiencing substance-induced psychosis. I will extract electronic health record data to study the demographic, social, and clinical factors associated with a new diagnosis of schizophrenia or other permanent psychoses following substance-induced psychosis.

I will use these findings to develop and validate a web-based calculator that calculates the likelihood a person with substance-induced psychosis develops permanent psychoses at a specific time in the future. Two separate datasets will be used for development (South London and Maudsley NHS Foundation Trust) and validation (Oxford Health NHS Foundation Trust) respectively.

Page last reviewed: 7 October, 2024