Title: Predicting a) risk of onset and of b) psychosis outcomes based on individual genetic and environmental profiles
Funder: Department of Health and Social Care

Abstract: Introduction: No tools currently exist to reliably predict (a) an individual's risk of developing first episode psychosis (FEP), and (b) psychosis outcomes in the next 5 years. Such tools could improve effectiveness of prevention and treatment strategies by allowing early and timely intervention that is tailored to the appropriate risk level for psychotic conversion as well as prognosis. They will also be of a special interest for school and university counselling services as they will highlight which students are more likely to develop psychosis based on pieces of information about their lives.Aims: To develop appropriate, reliable and accurate predictive models by analysing easily obtainable data about patients' lives using statistical and machine learning methods.Methods: I will utilise for the first-time data that collate data from >20 research sites across Europe and the UK capturing the data from a total of 14,826 patients diagnosed with psychosis, N=13,377 of these have been followed up for 5 years, and N=2,245 controls.Although a number of risk factors have been linked to onset of psychosis and psychosis outcomes (such as childhood adversity, migration, illicit drug use), the magnitude of their effects on an individual person is unknown. To establish this level of individual outcomes, I will utilise robust statistical and machine learning methods, such as Least Absolute Shrinkage and Selection Operator and Elastic Net, which are optimal methods for clinical prediction, personalised medicine and for an understanding of the process. Their significant advantages over the standard method of data analyses are 1) they handle a large number of predictors in relation to sample size; and 2) they address collinearity and overfitting problems (poor prediction of new cases).The performance of my models will be assessed by internal validation methods (calibration and discrimination) using cross-validation and bootstrapping methods. It will be possible to externally validate their prediction in independent samples from a completely different study. I will further estimate the clinical usefulness, the ability to make better decision with a model than without, by conducting utility-based loss of function analyses, such as decision-curves analyses and net-benefit analyses.The risk factors for both developing psychosis and outcomes to be included in my analyses were selected on the basis of (1) patients' perspectives; and (2) current literature.By the end of the project I will have developed simple and validated models for assessing (a), and (b) psychosis outcomes in the next 5 years based on 5 to 10 questions/pieces of information about a person.Implications: These results will be of interest to General Practitioners with expertise in psychosis, school and university counselling services, and specialists in early intervention services. These results should enable better focus of care, and reduce costs associated with treating patients with psychosis. This knowledge of specific risk factors for psychosis onset and poor prognosis will be useful for Public Education as they will enable clearer and better tailored public health campaigns.

Lay abstract: Psychotic disorders are severe illnesses of the mind that cause people to experience thoughts and sensations they would not otherwise experience. People with severe psychotic illnesses such as schizophrenia have difficulty completing every-day tasks; they lose contact with reality and experience a range of symptoms that usually include hallucinations (such as hearing voices) and delusions (false beliefs that are not based on reality and which they are convinced are true). Patients with these illnesses have a shorter life than the general population by up to 20 years. Treatments for these disorders currently cost about 12 billion every year in England alone.There are no one causes of psychotic disorders, but a range of different risk factors (such as childhood adversity, migration, inner city living, illicit drug use) appear to play a role. In other areas of medicine, such as cardiology and cancer, measuring someone's risk for developing an illness is already well established. For example, tools to estimate the risk of having a heart attack are well developed and used routinely by General Practitioners every day in clinics across the UK.The aims of this project are two-fold: To develop a simple method to assess the risk of the onset of psychosis using a questionnaire of between 5 to 10 items; For those patients who have developed psychosis, to develop a method of predicting illness progression in the next 5 years based on their individual profile of environmental and genetic risk factors. The study will draw on information from five large studies from across Europe and the UK, encompassing over 13,000 individuals diagnosed with psychosis with details on their risk factors, including their smoking and drinking habits, whether they take illicit drugs, childhood/adult traumatic experiences, their ethnicity, where they live and a personal genetic risk for schizophrenia.For this fellowship, I propose to use a type of analysis of the data called statistical and machine learning methods. I have chosen these methods because the current methods of data analyses that are widely used are not very reliable at predicting outcomes for patients with psychosis. In contrast, the statistical and machine learning methods that I want to use will allow for more accurate predictions to be made that are specific to an individual. Throughout the study I will involve patients and members of the general public in the study design, methodology and dissemination of my results. I will further discuss with them any ethical issues that I may encounter (such as discussing whether people would want to know that they are at a high risk for developing psychosis).On the basis of this fellowship I hope to be able to identify people who are at highest risk of illness so that prevention and early treatment options can be tailored to the risk status of each individual. Results of this research will be of a special benefit for school and university counselling services as they will highlight which students are more likely to develop psychosis based on pieces of information about their lives. They may also shed some light on why the rates of mental health issues have increased among younger people in the UK. For patients who have already developed psychosis, my results will highlight those with a poor predicted outcome and who would benefit from early treatment in the hope that enhanced intervention would improve their long-term outcome and thus increase their chances of recovery.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS
Health categories: MENTAL HEALTH