Personalise antidepressant treatment for unipolar depression combining individual choices, risks and big data (PETRUSHKA): rationale and protocol

Abstract

IntroductionMatching treatment to specific patients is too often a matter of trial and error, while treatment efficacy should be optimised by limiting risks and costs and by incorporating patients’ preferences. Factors influencing an individual’s drug response in major depressive disorder may include a number of clinical variables (such as previous treatments, severity of illness, concomitant anxiety etc) as well demographics (for instance, age, weight, social support and family history). Our project, funded by the National Institute of Health Research, is aimed at developing and subsequently testing a precision medicine approach to the pharmacological treatment of major depressive disorder in adults, which can be used in everyday clinical settings.Methods and analysisWe will jointly synthesise data from patients with major depressive disorder, obtained from diverse datasets, including randomised trials as well as observational, real-world studies. We will summarise the highest quality and most up-to-date scientific evidence about comparative effectiveness and tolerability (adverse effects) of antidepressants for major depressive disorder, develop and externally validate prediction models to produce stratified treatment recommendations. Results from this analysis will subsequently inform a web-based platform and build a decision support tool combining the stratified recommendations with clinicians and patients’ preferences, to adapt the tool, increase its’ reliability and tailor treatment indications to the individual-patient level. We will then test whether use of the tool relative to treatment as usual in real-world clinical settings leads to enhanced treatment adherence and response, is acceptable to clinicians and patients, and is economically viable in the UK National Health Service.DiscussionThis is a clinically oriented study, coordinated by an international team of experts, with important implications for patients treated in real-world setting. This project will form a test-case that, if effective, will be extended to non-pharmacological treatments (either face-to-face or internet-delivered), to other populations and disorders in psychiatry (for instance, children and adolescents, or schizophrenia and treatment-resistant depression) and to other fields of medicine.

Antidepressants in Children and Adolescents: Meta-Review of Efficacy, Tolerability and Suicidality in Acute Treatment

“…The available evidence base is not enough and randomised data should probably not be the only source of information. One way forward is to use comparative analysis of individual patient data in combination with high-quality real-world data to identify effect modifiers and prognostic factors that can inform tailored treatments and shared clinical decision making across a number of psychiatric conditions and interventions ( 52 ). This will be a material move towards a real precision-psychiatry approach that may improve the clinical outcome (and quality of life) of our patients ( 53 ).…”

Efficacy and acceptability of pharmacological and non-pharmacological interventions for non-specific chronic low back pain: a protocol for a systematic review and network meta-analysis

“…The results from this NMA will provide an important evidence base for clinicians to inform treatment decisions by providing a comparative assessment of a wide range of interventions [55]. This will help efforts to develop a precision medicine approach to the treatment for non-specific chronic low back pain, which can be used in everyday clinical settings.…”

Real-world effect of antidepressants for depressive disorder in primary care: protocol of a population-based cohort study

“…PETRUSHKA seeks to ultimately develop and test a precision medicine approach to the pharmacological treatment of major depressive disorder by synthesizing data coming from randomized controlled trials (RCTs) and data coming from observational studies and patient registries. [12] This study builds on a solid foundation of research performed on QResearch on the safety of antidepressants use in people aged 20-64 years, [10] and in older people [9] and contributes to the field by focusing on clinically relevant outcomes which have been evaluated also in RCTs.…”

“…The predictive model will then be used in PETRUSHKA to develop a web-based treatment algorithm to help clinicians, patients and carers to personalise the choice of antidepressant in primary care. [12]…”