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| Name | Class |
|---|---|
| National Institute for Health Research, United Kingdom | OTHER_GOV |
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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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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| PETRUSHKA tool | Experimental | 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 |
|
| Usual Care | Placebo Comparator | Routine care delivered in the NHS (i.e. selection of the antidepressant based primarily on the clinicians' judgement) termed 'usual care' in this study. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| PETRUSHKA tool | Other | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| To determine whether using the PETRUSHKA tool to "personalise" antidepressant treatment, results in an increased proportion of patients continuing the allocated treatment, compared to usual care. | The number of participants who are still taking the allocated antidepressants after 8 weeks. | 8 Weeks |
| Measure | Description | Time Frame |
|---|---|---|
| Self-rated change in depressive symptoms from baseline | Self-rated change in depressive symptoms measured using the 9-item Patient Health questionnaire | Baseline, week 2, 4, 6, 8, 12, 16, 20, 24 |
| Observer-rated change in depressive symptoms from baseline |
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Inclusion Criteria:
Exclusion Criteria:
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30763612 | Background | 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. | |
| 33848231 | Background |
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| Release Date | Unrelease Date | Unrelease Date Unknown | Reset Date | MCP Release Number |
|---|---|---|---|---|
| Jul 6, 2026 |
| ID | Term |
|---|---|
| D003863 | Depression |
| ID | Term |
|---|---|
| D001526 | Behavioral Symptoms |
| D001519 | Behavior |
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Assessors will be blind when administering rating scales at week 8 and 24, and statisticians will be blind to the allocated treatment during analysis.
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| Usual Care | Other | Any antidepressant prescribed by clinician based upon their clinical judgement. |
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Change in depressive symptoms measured using the observer-rated 17-item Hamilton Depression Rating Scale |
| Baseline, week 2, 4, 6, 8, 12, 16, 20, 24 |
| The number of participants who discontinue from treatment at 8 weeks due to any cause | Discontinuation from treatment due to any cause | Week 8 |
| The number of participants who discontinue from treatment at 24 weeks due to any cause | Discontinuation from treatment due to any cause | Week 24 |
| The number of participants who discontinue from treatment at 8 weeks due to adverse events | Discontinuation from treatment due to adverse events only | Week 8 |
| The number of participants who discontinue from treatment at 24 weeks due to adverse events | Discontinuation from treatment due to adverse events only | Week 24 |
| Self-rated change in anxiety symptoms from baseline | Self-rated change in anxiety symptoms measured using the 7-item Generalised Anxiety Disorder Assessment | Baseline, week 2, 4, 6, 8, 12, 20, and 24, |
| Observer-rated change in anxiety symptoms from baseline | Observer-rated change in anxiety symptoms using the Hamilton Anxiety Rating Scale | Baseline, week 2, 4, 6, 8, 12, 20, and 24, |
| The impact of depression on quality of life and capability wellbeing | EQ-5D-5L questionnaire (self-rated) | Baseline, week 4,8,12,24 |
| A reduction in risk of suicidality from baseline | Columbia Suicide Severity Rating Scale (observer-rated), ranging 1-5, where 1 is least severe and 5 is most severe. | Baseline, week 8 and 24 |
| An improvement in the functional outcome from baseline, with 0 being not at all and with 40 being very severely impaired. | Work and Social Adjustment Scale (self-rated) | Baseline, week 4, 8, 12 and 24 |
| A change in the health/social care costs of depression (direct and indirect) from baseline | Health Economics Questionnaire (self-rated) | Baseline, week 4,8,12 and 24 |
| 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. |
| 26803397 | Background | 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. |
| 32188600 | Background | 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. |
| 25986470 | Background | 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. |
| 27974039 | Background | 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. |
| 25976040 | Background | 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. |
| 1615114 | Background | 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. |
| 41779422 | Derived | Cipriani A, Fernandes KBP, Mulsant BH, Efthimiou O, Williams N, Mort S, Elgarf R, Liu Q, Haque N, Potts J, Ede R, Fox R, Liboni M, Nesi Cavicchioli DA, Simon J, Smith KA, Zangani C, Li Z, Taylor U, Husain MI, Cipriani M, Carpaneze Dalaqua PV, Leite EF, Aliano Gambaro G, Nabhan Silveira D, Manfredin Vila L, Liboni Cavicchioli F, Furukawa TA, Naci H, Ostinelli EG; PETRUSHKA Team. A Decision-Support System to Personalize Antidepressant Treatment in Major Depressive Disorder: A Randomized Clinical Trial. JAMA. 2026 Apr 14;335(14):1219-1231. doi: 10.1001/jama.2026.1327. |