DepressionPETRUSHKANCT05608330
Personalise Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and Big Data
A study testing PETRUSHKA tool in people with Depression, run by University of Oxford.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 74 years
- Conditions the study is for
- Depression
- What is being tested?
- PETRUSHKA tool
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 504 participants
- Where
- No sites listed yet
- Study dates
- Nov 2022to Nov 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 74 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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).
About this study
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.
Read the detailed description
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.
- Conditions
- Depression
- Being tested
- PETRUSHKA tool, Usual Care
- Medical subject headings
- Depression
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PETRUSHKA toolOther
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 CareOther
Any antidepressant prescribed by clinician based upon their clinical judgement.
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: 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. Receives: Other: Usual Care |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 8 Weeks
Secondary outcomes (12)
Self-rated change in depressive symptoms from baseline
Self-rated change in depressive symptoms measured using the 9-item Patient Health questionnaire
Measured: Baseline, week 2, 4, 6, 8, 12, 16, 20, 24
Observer-rated change in depressive symptoms from baseline
Change in depressive symptoms measured using the observer-rated 17-item Hamilton Depression Rating Scale
Measured: 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
Measured: Week 8
The number of participants who discontinue from treatment at 24 weeks due to any cause
Discontinuation from treatment due to any cause
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: 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)
Measured: 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.
Measured: 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)
Measured: 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)
Measured: Baseline, week 4,8,12 and 24
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Nov 2022
- Study startNov 2022 (estimated)
- Primary completionJul 2023 (estimated)
- Study completionNov 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for DepressionStudies of the same condition, open and under way.
- More trials run by University of OxfordThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 504 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Oxford
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute for Health Research, United Kingdom
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05608330
- Sponsor's protocol ID
- 286484
- Acronym
- PETRUSHKA
- Status last verified
- Oct 2022
- First submitted
- 21 Oct 2022
- Last updated
- 8 Nov 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 30763612Austin 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.
PubMed 33848231Chekroud 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.
PubMed 26803397Riley 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.
PubMed 32188600Tervonen 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.
PubMed 25986470Califf 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.
PubMed 27974039Chekroud 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.
PubMed 25976040Lewis 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.
PubMed 1615114Cipriani 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.
PubMed 41779422
Study at a glance
From the registry record, last updated by the sponsor on 8 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Personalise Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and Big Data
- ClinicalTrials.gov ID
- NCT05608330
- Acronym / sponsor ID
- PETRUSHKA / 286484
- Conditions
- Depression
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 504 participants (estimated)
- Lead sponsor
- University of OxfordUniversity, hospital or other
- Collaborators
- National Institute for Health Research, United Kingdom
- Study start
- Nov 2022 (estimated)
- Primary completion
- Jul 2023 (estimated)
- Study completion
- Nov 2023 (estimated)
- First posted
- 8 Nov 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| PETRUSHKA tool | Experimental | Other: PETRUSHKA toolThe 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 | Other: Usual CareRoutine care delivered in the NHS (i.e. selection of the antidepressant based primarily on the clinicians' judgement) termed 'usual care' in this study. |
Who may qualify
- Ages
- 18 to 74 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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;
Key exclusion criteria (7 listed)
- 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;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| PETRUSHKA toolIn 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. | Other | PETRUSHKA tool |
| Usual CareAny antidepressant prescribed by clinician based upon their clinical judgement. | Other | Usual Care |
Primary outcome
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.
Time frame: 8 Weeks
The number of participants who are still taking the allocated antidepressants after 8 weeks.
12 secondary outcomes
Self-rated change in depressive symptoms from baseline
Time frame: Baseline, week 2, 4, 6, 8, 12, 16, 20, 24
Observer-rated change in depressive symptoms from baseline
Time frame: 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
Time frame: Week 8
The number of participants who discontinue from treatment at 24 weeks due to any cause
Time frame: Week 24
The number of participants who discontinue from treatment at 8 weeks due to adverse events
Time frame: Week 8
The number of participants who discontinue from treatment at 24 weeks due to adverse events
Time frame: Week 24
Self-rated change in anxiety symptoms from baseline
Time frame: Baseline, week 2, 4, 6, 8, 12, 20, and 24,
Observer-rated change in anxiety symptoms from baseline
Time frame: Baseline, week 2, 4, 6, 8, 12, 20, and 24,
The impact of depression on quality of life and capability wellbeing
Time frame: Baseline, week 4,8,12,24
A reduction in risk of suicidality from baseline
Time frame: 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.
Time frame: Baseline, week 4, 8, 12 and 24
A change in the health/social care costs of depression (direct and indirect) from baseline
Time frame: Baseline, week 4,8,12 and 24
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Nov 2022.
- Background: PubMed 30763612Christodoulou 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.
- Background: PubMed 33848231Austin 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.
- Background: PubMed 26803397Chekroud 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.
- Background: PubMed 32188600Riley 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.
- Background: PubMed 25986470Tervonen 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.
- Background: PubMed 27974039Califf 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.
- Background: PubMed 25976040Chekroud 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.
- Background: PubMed 1615114Lewis 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.
- Derived from this study: PubMed 41779422Cipriani 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing PETRUSHKA tool in people with Depression, run by University of Oxford.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 8 Nov 2022. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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. (time frame: 8 Weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05608330, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Nov 2022.