Personalise Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and Big Data
Personalise Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and Big Data
PETRUSHKA is aimed at developing and subsequently testing a personalised approach to the pharmacological treatment of major depressive disorder in adults, which can be used in everyday NHS clinical settings.
We have collected data from patients with major depressive disorder, obtained from diverse datasets, including randomised trials as well as real-world registries (registers that hold routinely collected NHS data from the UK). These data summarise the most reliable and most up-to-date scientific evidence about benefits and adverse effects of antidepressants for depression and have been used to inform the PETRUSHKA prediction model to produce individualised treatment recommendations. The prediction model underpins a web-based decision support tool (the PETRUSHKA tool) which incorporates the patient's and clinician's preferences in order to rank treatment options and tailor the treatment to each patient.
This trial will recruit participants from the NHS within primary care in England and investigate whether the use of the PETRUSHKA tool is better than 'usual care' treatment in terms of adherence to antidepressant treatment, clinical response and quality of life, and its cost-effectiveness over a 6-months follow up.
The PETRUSHKA tool, employs a bespoke algorithm to identify the best antidepressant for each individual patient. The algorithm: (a) is based on a prediction model which uses a combination of advanced analytics (statistics) and machine learning methods (artificial intelligence); (b) uses a dataset which is a combination of real-world data (QResearch: https://www.qresearch.org/) from over 1 million primary care patients with depression in England and Wales, and individual participant data from about 40,000 patients recruited in randomised controlled trials; (c) incorporates preferences from patients and clinicians (especially about adverse events); (d) generates a ranked list of personalised treatment recommendations that will inform the clinical discussion between clinicians and patients, and the final treatment decision. The clinical decision aid tool is implemented in the form of a web-based application, accessible from any computer or tablet.
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