Status
Completed
Title
Antidepressant Decision Aid Tools and Risk Assessment
What were the objectives of the study?
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 was the research 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
Lead Applicant Organisation Name
Sponsor
University of Nottingham
Location of research
Universities of Nottingham & Oxford
Date on which research approved
01-Apr-2018
Project reference ID
R83 and OX20
Generic ethics approval reference
18/EM/0400
Are all data accessed are in anonymised form?
Yes
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.
What were the main findings?
• Between 2005 and 2017, more than 33,000 young people aged 5-17 years were prescribed an antidepressant by their GP. Over 90% were aged 12-17 and two-thirds were females. Two thirds of the antidepressants prescribed first were selective serotonin reuptake inhibitors (SSRIs).
• Of those prescribed an antidepressant, 37% had a record of visiting a paediatrician or psychiatrist. However, the numbers varied by the patient’s age and type of antidepressant prescribed. For example, half of 5-11-year-olds and 36% of 12-17-year-olds had a record of visiting a paediatric or psychiatric specialist.
• The study also looked at reasons why the antidepressants were prescribed. For many children and young people it was difficult to determine why the antidepressant was prescribed – as 46% had no record of the most likely indications. Overall, depression (38%) and anxiety (13%) were the most common reasons recorded. For children aged 5-11 years the most frequently recorded indications were enuresis (28%) and autism (9%), but 57% had no recorded indication. Over half of the 12-17-year-olds first prescribed an SSRI had a record of depression (53%) and 16% had anxiety recorded.
These results suggest a substantial proportion of children and young people receive prescriptions for antidepressants without the involvement of a relevant specialist, such as a mental health specialist or paediatrician. The authors concluded that further research is needed to explore factors that influence how and why GPs prescribe antidepressants to children and young people.
Reference: Jack RH, Joseph RM, Coupland C, Butler D, Hollis C, Morriss R, Knaggs RD, Cipriani A, Cortese S, Hippisley-Cox J. Secondary care specialist visits made by children and young people prescribed antidepressants in primary care: a descriptive study using the QResearch database. BMC Medicine. 2020;18(1):93.
Link to publication: https://rdcu.be/b3SDJ
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
Public Benefit Statement
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
Publications
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Trends in antidepressant prescriptions in children and young people in England, 1998-2017: protocol of a cohort study using linked primary and secondary care datasets
Authors: Jack RH, Hollis C, Coupland C, Morriss R, Knaggs RD, Cipriani A, Cortese S, Hippisley-Cox J
Ref: Evidence Based Mental Health 2019 dot:10.1136/ebmental-2019-300097
https://ebmh.bmj.com/content/early/2019/06/28/ebmental-2019-300097.long -
Secondary care specialist visits made by children and young people prescribed antidepressants in primary care: a descriptive study using the QResearch database
Authors: Jack, RH, Joseph, RM, Coupland, C, Butler D, Hollis, C, Morriss, Knaggs, RD, Cipriani, A, Cortese, S, Hippisley-Cox, J
Ref:
https://rdcu.be/b3SDJ -
Incidence and prevalence of primary care antidepressant prescribing in children and young people in England, 1998–2017: A population-based cohort study
Authors: Ruth H. Jack , Chris Hollis, Carol Coupland, Richard Morriss, Roger David Knaggs, Debbie Butler, Andrea Cipriani, Samuele Cortese, Julia Hippisley-Cox
Ref:
https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003215
Press Releases
- Teenagers' use of antidepressants is rising with variations across regions and ethnic groups
- Teens and antidepressants, stamp duty savings and earthquake probabilities
Access Type
Trusted Research Environment (TRE)