A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression

Rob Saunders, Zachary D. Cohen, Gareth Ambler, Robert J. DeRubeis, Nicola Wiles, David Kessler, Simon Gilbody, Steve D. Hollon, Tony Kendrick, Ed Watkins, David Richards, Sally Brabyn, Elizabeth Littlewood, Debbie Sharp, Glyn Lewis, Steve Pilling, Joshua E. J. Buckman*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)
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Background: Subgrouping methods have the potential to support treatment decision making for patients with depression. Such approaches have not been used to study the continued course of depression or likelihood of relapse following treatment. Method: Data from individual participants of seven randomised controlled trials were analysed. Latent profile analysis was used to identify subgroups based on baseline characteristics. Associations between profiles and odds of both continued chronic depression and relapse up to one year post-treatment were explored. Differences in outcomes were investigated within profiles for those treated with antidepressants, psychological therapy, and usual care. Results: Seven profiles were identified; profiles with higher symptom severity and long durations of both anxiety and depression at baseline were at higher risk of relapse and of chronic depression. Members of profile five (likely long durations of depression and anxiety, moderately-severe symptoms, and past antidepressant use) appeared to have better outcomes with psychological therapies: antidepressants vs. psychological therapies (OR (95% CI) for relapse = 2.92 (1.24–6.87), chronic course = 2.27 (1.27–4.06)) and usual care vs. psychological therapies (relapse = 2.51 (1.16–5.40), chronic course = 1.98 (1.16–3.37)). Conclusions: Profiles at greater risk of poor outcomes could benefit from more intensive treatment and frequent monitoring. Patients in profile five may benefit more from psychological therapies than other treatments.
Original languageEnglish
Article number1295
JournalJournal of Personalized Medicine
Issue number12
Publication statusPublished - 4 Dec 2021

Bibliographical note

Funding Information:
Funding: This work was supported by the Welcome Trust through a Clinical Research Fellowship to J.E.J.B. (201292/Z/16/Z), the Royal College of Psychiatrists through a grant to J.E.J.B., R.S. and S.P., MQ Foundation (for ZC: MQDS16/72), the Higher Education Funding Council for England and the National Institute of Health Research (NIHR) University College London Hospitals Biomedical Research Centre (S.P.), University College London (R.S., G.A., G.L.), Vanderbilt University (S.D.H.), University of Southampton (T.K.), University of Exeter (E.W.) and University of York (S.G.), NIHR Biomedical Research Centre at the University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol (N.W.). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • depression
  • primary care
  • latent profile analysis
  • personalised medicine
  • patient stratification


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