Title: Choosing the right antidepressant for depressive disorder
Funder: Department of Health and Social Care

Abstract: Background and research question Depression is the second leading causes of global burden and a significant challenge for health systems worldwide. Antidepressants are commonly used to treat moderate to severe depressive disorder. Only in the UK, 64.7m prescriptions of antidepressants were dispensed in 2016. Despite these figures, many patients are given antidepressants which do not work or cause adverse events. In fact, the majority of these prescriptions are for less than 30 days, which is much shorter than required to see a clinical effect. A key issue which drives this problem is that the perceived efficacy of the drugs is low and that, while there is a wide choice of antidepressants, no information is available to guide the personalised selection of the most efficacious and tolerable drug for a specific patient.Aims and objective I aim to develop and test an evidence-based clinical decision support tool to personalise treatment in depression in the NHS.MethodsDuring Phase 1 (Evidence synthesis), I will: systematically review published and unpublished antidepressant trials in adults with depressive disorder; combine aggregate and individual-patient data in a network meta-analysis; rank the different antidepressants and stratify outcomes into subgroup of patients with different treatment indications based on patients' individual characteristics. During Phase 2 (Development of the treatment algorithm), I will: develop a clinical decision support tool (treatment algorithm) to combine the stratified recommendations from randomised evidence with the preferences and values of patients. Patients' characteristics (i.e. age, symptom severity, previous treatments) and preferences about side effects will be entered in the algorithm. This information will vary from one person to another and will change the final output, tailoring the ranking of antidepressants to each individual; incorporate routinely collected data derived from large observational datasets in primary and secondary care into the algorithm. Matching the patient's characteristics, the combination of information from randomised and observational data will increase the precision and reliability of the treatment recommendations; run a series focus groups with patients, carers and clinicians to inform the development of an easily used and acceptable platform by which the clinical value and practical utility of the algorithm can be maximised. During Phase 3 (Randomised trial), I will conduct a randomised, multi-centre trial. I will recruit 496 participants with depressive disorder who have been prescribed antidepressants from GP surgeries or outpatient clinics across 5 centres in the UK, comparing the use of the treatment algorithm versus usual care. The primary outcome will be the rate of discontinuation due to any cause, as a proxy of the clinical acceptability of the algorithm.Anticipated impact and disseminationDevelopment and validation of a treatment algorithm for personalised prescription of antidepressants in depression, to be used in routine clinical settings within the NHS. Publication of the results from the three phases of the project in high-impact journals and dissemination of the findings to the general audience via patient and public engagement. Data and analyses will be made freely available, as appropriate, to support the future enhancement of classication algorithms.

Lay abstract: Background Depression is very common and a major health problem. Antidepressants are the first line treatment for depressive disorder. In England alone, 64.7m prescriptions of antidepressants were dispensed in 2016, more than twice the 31m antidepressants dispensed in 2006. Many patients, however, are given antidepressants, which, for them, prove ineffective or cause intolerable side-effects. This happens because antidepressants are prescribed without a clear understanding of which drug is the most appropriate medication for each individual in terms of both efficacy and tolerability. So, people stop the antidepressant early because they are prescribed a drug which might work for an average person, but has not been tailored to them individually. There is widespread concern about the negative consequences if antidepressants are prescribed without taking into account individual patients circumstances. Regulatory bodies and guidelines developers across the world have recommended the improved targeting of antidepressant treatment for depression, but this advice has not yet been translated into practice. I propose that we already have sufficient evidence to differentiate treatments according to personal characteristics, and the preferences and values of patients themselves, and carers, should also be included in the choice of treatment. People with a diagnosis of depression need additional support during the consultation visit when they make decisions about starting a new treatment. Many patients feel intimidated by the prospect of taking antidepressants, especially during the first few weeks of treatment.Aim The starting point for this proposal is that, in fact, we already have the knowledge which can markedly reduce these shortcomings. That is, by a more careful analysis of existing data, we can better tailor the choice of a specific drug to a specific person, to increase the chances that the drug will be tolerable and effective. The aim of this project is to develop an internet-based system which will help doctors and patients together choose the best antidepressant for each individual with moderate to severe symptoms of depression, considering together both the efficacy and the adverse events of these drugs.Design and methods This system will integrate the best available scientific information with the preferences of patients to provide, for the first time, a bespoke clinical decision aid for antidepressant treatment. There are many databases that can be analysed with innovative approaches to understand which medicine is more effective or causes less adverse events in each particular individual. The system will be tested in a scientifically sound study of depressed patients who are to be treated with antidepressant medication in both primary and secondary care across the UK. The intervention is designed to enhance both the efficacy and tolerability of the antidepressant treatment which is predicted to enhance its acceptability. The primary outcome of the study is therefore adherence to the prescribed antidepressant treatment after 8 weeks.Patient and public involvement During the project, patients and carers will be involved in the design and development of the internet-based system. In 5 years' time, this project will provide patients, carers and doctors with a user-friendly, innovative tool for shared decision making in depression, ready to be implemented in both primary and secondary care.Dissemination and impact This new model of collaborative care will: (i) produce scientific publications (ii) increase the number of people who respond to and tolerate the prescribed antidepressant; (iii) help reduce costs for the NHS and the broader community, by reducing the number of prescriptions of antidepressants that are ineffective or discontinued prematurely; (iv) provide a model that can be extended to non-pharmacological treatments (for instance, psychotherapy) and beyond depression to include other psychiatric and non-psychiatric disorders, such as schizophrenia, diabetes and epilepsy.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING
Health categories: MENTAL HEALTH