Personalise Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and Big Data

NCT05608330 | Unknown DMC

• 1 other identifier: 286484
• Study Type: interventional
• Target: 504
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Depression

Outcome Measures

Primary Outcomes (1)

• 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

Secondary Outcomes (12)

• Self-rated change in depressive symptoms from baseline

  Baseline, week 2, 4, 6, 8, 12, 16, 20, 24

• Observer-rated change in depressive symptoms from baseline

  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

  Week 8

• The number of participants who discontinue from treatment at 24 weeks due to any cause

  Week 24

• The number of participants who discontinue from treatment at 8 weeks due to adverse events

  Week 8

Study Arms (2)

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

Other: PETRUSHKA tool

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.

Other: Usual Care

Interventions

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.

PETRUSHKA tool

Usual Care OTHER

Any antidepressant prescribed by clinician based upon their clinical judgement.

Usual Care

Eligibility Criteria

• Age: 18 Years - 74 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Aged 18 - 74 years inclusive;
• 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

You may not qualify if:

• Prescribed any antidepressant in the preceding 4 weeks;
• 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).

Sponsors & Collaborators

• University of Oxford: lead
• National Institute for Health Research, United Kingdom: collaborator

Related Publications (8)

• 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.

  PMID: 30763612 BACKGROUND

• 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.

  PMID: 33848231 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.

  PMID: 26803397 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.

  PMID: 32188600 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.

  PMID: 25986470 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.

  PMID: 27974039 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.

  PMID: 25976040 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.

  PMID: 1615114 BACKGROUND

MeSH Terms

Conditions

Depression

Condition Hierarchy (Ancestors)

Behavioral Symptoms; Behavior

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Masking Details: Assessors will be blind when administering rating scales at week 8 and 24, and statisticians will be blind to the allocated treatment during analysis.
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 21, 2022
• First Posted: November 8, 2022
• Study Start: November 1, 2022
• Primary Completion: July 1, 2023
• Study Completion: November 1, 2023
• Last Updated: November 8, 2022

Record last verified: 2022-10

Data Sharing

• IPD Sharing: Will not share