Personalise Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and Big Data
NCT ID: NCT05608330
Last Updated: 2022-11-08
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
NA
504 participants
INTERVENTIONAL
2022-11-30
2023-11-30
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
TREATMENT
SINGLE
Study Groups
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PETRUSHKA tool
The intervention is the PETRUSHKA web-based App (also called PETRUSHKA tool), a clinical decision-support system that incorporates a personalised evidence-based prediction model with individual patient preferences, to prescribe the best antidepressant to adults with depression
PETRUSHKA tool
In the experimental arm, the PETRUSHKA tool will automatically select the antidepressants that have the best profile in terms of efficacy and acceptability for each individual participant (based on their baseline demographic and clinical characteristics) and then ask the participant to provide their preferences about common (and non-serious) adverse events. Based on patient's preferences and their individual characteristics, the PETRUSHKA tool will then identify the three best antidepressants for the participant. The clinician and the participant will be presented with an overall recommendation (in the format of a pictogram) showing how strongly each antidepressant is recommended for that individual patient. Via a shared decision-making process, the participant and the clinician will then agree on which antidepressant to choose from the shortlist.
Usual Care
Routine care delivered in the NHS (i.e. selection of the antidepressant based primarily on the clinicians' judgement) termed 'usual care' in this study.
Usual Care
Any antidepressant prescribed by clinician based upon their clinical judgement.
Interventions
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PETRUSHKA tool
In the experimental arm, the PETRUSHKA tool will automatically select the antidepressants that have the best profile in terms of efficacy and acceptability for each individual participant (based on their baseline demographic and clinical characteristics) and then ask the participant to provide their preferences about common (and non-serious) adverse events. Based on patient's preferences and their individual characteristics, the PETRUSHKA tool will then identify the three best antidepressants for the participant. The clinician and the participant will be presented with an overall recommendation (in the format of a pictogram) showing how strongly each antidepressant is recommended for that individual patient. Via a shared decision-making process, the participant and the clinician will then agree on which antidepressant to choose from the shortlist.
Usual Care
Any antidepressant prescribed by clinician based upon their clinical judgement.
Eligibility Criteria
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Inclusion Criteria
* Willing and able to give informed consent for participation in the trial;
* Clinical diagnosis of depression (either single episode or recurrent), for which an antidepressant is clinically indicated;
* Willing to start antidepressant treatment as monotherapy;
* Able to read/understand and/or complete self-administered questionnaires online in English;
* Willing to meet any clinical requirements related to taking a specific medication
Exclusion Criteria
* Current or historical diagnosis of ADHD, Alcohol/Substance Use Disorder, bipolar disorder, dementia, eating disorders, mania/hypomania, OCD, PTSD, psychosis/schizophrenia, Treatment Resistant Depression (having tried 2 or more antidepressants for the same depressive episode at adequate dose and time);
* Diagnosis of arrhythmias (including Q-T prolongation, heart block), recent MI, poorly controlled epilepsy, acute porphyrias;
* Require urgent mental care or admission (including suicidal intent/plans);
* Concurrently enrolled in another investigational medicinal product (IMP) trial or an interventional trial about depression;
* Participants who are currently pregnant, planning pregnancy or lactating;
* Has a medical, social or other condition which, in the investigator's opinion , may make the participant unable to comply with all the trial requirements (e.g., terminal illness - motor neuron disease).
18 Years
74 Years
ALL
No
Sponsors
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National Institute for Health Research, United Kingdom
OTHER_GOV
University of Oxford
OTHER
Responsible Party
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References
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Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, Moons KGM, Collins G, van Smeden M. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020 Mar 18;368:m441. doi: 10.1136/bmj.m441. No abstract available.
Tervonen T, Naci H, van Valkenhoef G, Ades AE, Angelis A, Hillege HL, Postmus D. Applying Multiple Criteria Decision Analysis to Comparative Benefit-Risk Assessment: Choosing among Statins in Primary Prevention. Med Decis Making. 2015 Oct;35(7):859-71. doi: 10.1177/0272989X15587005. Epub 2015 May 18.
Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019 Jun;110:12-22. doi: 10.1016/j.jclinepi.2019.02.004. Epub 2019 Feb 11.
Austin PC, Harrell FE Jr, Steyerberg EW. Predictive performance of machine and statistical learning methods: Impact of data-generating processes on external validity in the "large N, small p" setting. Stat Methods Med Res. 2021 Jun;30(6):1465-1483. doi: 10.1177/09622802211002867. Epub 2021 Apr 13.
Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, Cannon TD, Krystal JH, Corlett PR. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry. 2016 Mar;3(3):243-50. doi: 10.1016/S2215-0366(15)00471-X. Epub 2016 Jan 21.
Califf RM, Robb MA, Bindman AB, Briggs JP, Collins FS, Conway PH, Coster TS, Cunningham FE, De Lew N, DeSalvo KB, Dymek C, Dzau VJ, Fleurence RL, Frank RG, Gaziano JM, Kaufmann P, Lauer M, Marks PW, McGinnis JM, Richards C, Selby JV, Shulkin DJ, Shuren J, Slavitt AM, Smith SR, Washington BV, White PJ, Woodcock J, Woodson J, Sherman RE. Transforming Evidence Generation to Support Health and Health Care Decisions. N Engl J Med. 2016 Dec 15;375(24):2395-2400. doi: 10.1056/NEJMsb1610128. No abstract available.
Chekroud AM, Krystal JH. Personalised pharmacotherapy: an interim solution for antidepressant treatment? BMJ. 2015 May 14;350:h2502. doi: 10.1136/bmj.h2502. No abstract available.
Lewis G, Pelosi AJ, Araya R, Dunn G. Measuring psychiatric disorder in the community: a standardized assessment for use by lay interviewers. Psychol Med. 1992 May;22(2):465-86. doi: 10.1017/s0033291700030415.
Other Identifiers
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286484
Identifier Type: -
Identifier Source: org_study_id
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