Antidepressant Decision Aid Tools and Risk Assessment


Antidepressant Decision Aid Tools and Risk Assessment

What is the aim of the study and why is it important?

Depression is very common affecting up to 11% of the population. One of the treatments for depression is antidepressant medication. There are many different types of antidepressants and most of them are effective. Some antidepressants have more adverse reactions and side effects than others and some antidepressants are affected by other medicines that a patient might be taking. Some patients may be more susceptible than others to getting adverse effects. Doctors and patients need better information to help select which antidepressants might be the best for an individual patient, taking account of their medical history and other medications. In this program of work, we aim to identify any unexpected or adverse reactions to drugs used to treat mental health conditions due to how they are affected by other drugs (also known as drug-drug interactions). This will help improve treatment choices for patients and safety monitoring of new and commonly used medicines. As a result of this study we will create a decision aid for use in clinical practice, to help doctors and patients decide which will be the best antidepressant treatment for individual patients. Objectives: We have identified a number of objectives to help us achieve the above aims as follows: 1. To review the literature to identify potential serious drug-drug interactions and drug-disease interactions for antidepressant medication 2. To describe current epidemiology of antidepressant medication use in primary care populations overall and by subgroups (e.g. which drugs are most commonly used by sex, age, ethnic group, co-morbidity, pregnancy etc) 3. To analyse trends in uptake of individual antidepressants over the last 20 years overall and by population subgroup 4. To investigate the extent of polypharmacy among users of antidepressant medication 5. To quantify the percentage of patients at risk of known serious drug-drug or drug-disease interactions 6. To investigate increased mortality observed with mirtazapine including analysis of cause specific mortality 7. To evaluate the use of machine learning approaches to quantify drug-interactions and health outcomes of antidepressants overall, by ethnic group and among patients with co-morbidities and polypharmacy and compare and calibrate these with traditional statistical methods 8. To use the above to identify patients most at risk of adverse outcomes of antidepressants 9. To translate the above knowledge into clinical decision support tools to help optimise treatment choices for patients. This could include so focus groups/qualitative work. 10. To use the above as the basis for an intelligent adaptive system to identify adverse outcomes associated with antidepressants

How is the research being done?

We are using primary care information from the QResearch database, which contains data on patients from over 1500 general practices in England. This dataset has been linked to secondary care and mortality datasets, and will allow us to determine whether particular mental health medicines interact with other drugs to produce harmful outcomes, how likely this is to happen, and which patients are most susceptible. We will be using both established statistical methods and novel machine learning approaches to investigate this. Machine learning is where computers are helped to process enormous amounts of data to find patterns and develop predictive models, without being explicitly programmed to do so.

Chief Investigator

Julia Hippisley-Cox

Location of research

Universities of Nottingham & Oxford

Date on which research approved


Project reference ID


Are all data accessed are in anonymised form?


Brief summary of the dataset to be released (including any sensitive data)

GP data for patients prescribed antidepressants; linked hospital and mortality data for mental health diagnoses.

Implications and Impact

We hope to find out which drugs cause serious problems when taken alongside different antidepressant drugs for mental health conditions, and produce an online tool that can be used to inform which antidepressant treatment will be best for patients.

Funding Source

NIHR Biomedical Research Centre

Research Team

Julia Hippisley-Cox, Carol Coupland, Ruth Jack, Debbie Butler, Rebecca Joseph, Jon Garibaldi, Richard Morriss, Chris Hollis, Roger Knaggs, Digital Research Service: University of Nottingham

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