A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression
Open accessAbstract
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.
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In major depressive disorder, 18-77% of patients experienced a relapse.
“Likewise, with regard to major depressive disorder (MDD), 18–77% of patients experienced a relapse [22–28].”
From Psychological interventions to prevent relapse in anxiety and depression: A systematic review and meta-analysis (2022) · cites this paper as [26]
The Discussion reports relapse likelihoods of 17.5%–45.7% across patient profiles, not 18%–77%. Its 77.5% figure refers to continued chronic depression, a separate outcome defined in Methods §2.3. (LLM, conf=0.90)
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Kent, P. M., Stochkendahl, M. J., Christensen, H. W., & Kongsted, A. (2015) · Chiropractic & Manual Therapies · doi:10.1186/s12998-015-0064-9 · Cited by 11 · see citing works
Buckman, J. E. J., Saunders, R., Cohen, Z. D., Clarke, K., Ambler, G., DeRubeis, R. J., Gilbody, S. M., Hollon, S. D., Kendrick, T., Watkins, E. R., et al. (2019) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15225.3 · Cited by 11 · see citing works
Self-citationSpiegel, D. R., & Webb, K. (2012) · PubMed · Cited by 11 · see citing works
Buckman, J. E. J., Saunders, R., Cohen, Z. D., Clarke, K., Ambler, G., DeRubeis, R. J., Gilbody, S. M., Hollon, S. D., Kendrick, T., Watkins, E. R., et al. (2019) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15225.1 · Cited by 9 · see citing works
Self-citationCognitive Behaviour Therapy for Withdrawal from Antidepressant Medication: A Single Case Series
Cromarty, P., Jonsson, J., Moorhead, S., & Freeston, M. H. (2010) · Behavioural and Cognitive Psychotherapy · doi:10.1017/s1352465810000512 · Cited by 7 · cited 1× · see citing works
“Information on the risks of chronic depression and relapse might also inform treatment decisions on continuation or discontinuation of medications [69,70].”
BackgroundBreaking the Rhythm of Depression: Cognitive Behavior Therapy and Relapse Prevention for Depression
Bockting, C. (2010) · University of Groningen research database (University of Groningen / Centre for Information Technology) · Cited by 5 · see citing works
Moriarty, A. S., Paton, L. W., Snell, K. I. E., Riley, R. D., Buckman, J. E. J., Gilbody, S. M., Chew-Graham, C. A., Ali, S., Pilling, S., Meader, N., et al. (2021) · Diagnostic and Prognostic Research · doi:10.1186/s41512-021-00101-x · Cited by 5 · cited 1× · see citing works
“Other studies to develop more accurate models are planned [45], but no findings are available yet, and the clinical utility of any such models are yet to be established.”
BackgroundSelf-citation
Flagged first, then by citations
Research works printed but not linked: 2
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- [58]Stata Statistical Software: Release 16 Stata Press College Station, TX, USA 2019
Cited by
Sorted by citations
Socioeconomic Indicators of Treatment Prognosis for Adults With Depression
Buckman, J. E. J., Saunders, R., Stott, J. C. H., Cohen, Z. D., Arundell, L.-L., Eley, T. C., Hollon, S. D., Kendrick, T., Ambler, G., Watkins, E. R., et al. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.0100 · Cited by 97 · see citing works
Self-citationKrijnen-de Bruin, E., Scholten, W., Muntingh, A., Maarsingh, O., van Meijel, B., van Straten, A., & Batelaan, N. (2022) · PLOS ONE · doi:10.1371/journal.pone.0272200 · Cited by 23 · see citing works
Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records
Xu, Z., Vekaria, V., Wang, F., Cukor, J., Su, C., Adekkanattu, P. M., Brandt, P., Jiang, G., Kiefer, R. C., Luo, Y., et al. (2023) · Psychiatric Research and Clinical Practice · doi:10.1176/appi.prcp.20220015 · Cited by 18 · see citing works
“In addition, prior studies based on EHRs mainly extracted feature information from patients' medical records (10, 12–14), such as medication dose information, to predict treatment dropout or remission after receiving antidepressants.”
BackgroundCognitive remediation for depression vulnerability: Current challenges and new directions
Zwalmen, Y. V., Hoorelbeke, K., Liebaert, E., de Mévergnies, C. N., & Koster, E. H. W. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.903446 · Cited by 14 · see citing works
Development of a model to predict psychotherapy response for depression among Veterans
Ziobrowski, H. N., Cui, R., Ross, E. L., Liu, H., Puac‐Polanco, V., Turner, B., Leung, L. B., Bossarte, R. M., Bryant, C., Pigeon, W. R., et al. (2022) · Psychological Medicine · doi:10.1017/s0033291722000228 · Cited by 13 · see citing works
“Based on this fact, researchers have examined whether multivariable models that combine information across a range of individually significant predictors can improve the prediction of psychotherapy treatment response (DeRubeis et al., 2014; Huibers et al., 2015; Saunders et al., 2021).”
BackgroundDai, F., Wengler, K., He, X., Wang, J., Yang, J., Parsey, R. V., & DeLorenzo, C. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03292-9 · Cited by 12 · see citing works
Carr, C. E., Millard, E., Dilgul, M., Bent, C., Wetherick, D., French, J., & Priebe, S. (2023) · Pilot and Feasibility Studies · doi:10.1186/s40814-023-01285-3 · Cited by 12 · see citing works
“Further stratification of patient characteristics may be useful in a larger trial [97].”
BackgroundStålner, O., Nordin, S., & Madison, G. (2022) · BMC Public Health · doi:10.1186/s12889-022-13966-4 · Cited by 7 · see citing works
Prieto-Vila, M., González‐Blanch, C., Estupiñá, F. J., Buckman, J. E. J., Saunders, R., Muñoz‐Navarro, R., Moriana, J. A., Rodríguez-Ruiz, P., Barrio‐Martínez, S., Carpallo-González, M., et al. (2024) · European Psychiatry · doi:10.1192/j.eurpsy.2024.27 · Cited by 6 · see citing works
“Participants with a basic education were more likely to follow the relapse trajectory compared to the recovery trajectory, consistent with prior studies [11, 16].”
BackgroundResultInfluentialSelf-citationda Rocha, C. G., von Gunten, A., Vandel, P., Jopp, D. S., Ribeiro, O. M. P. L., & Verloo, H. (2025) · JMIR Aging · doi:10.2196/64352 · Cited by 6 · see citing works
“Furthermore, previous episodes of depression or diagnosed depression were also identified as relevant, highlighting the importance of considering an individual’s psychiatric history as past experiences with depression increase the risk of recurrence [69].”
Moriarty, A. S., Paton, L. W., Snell, K. I. E., Archer, L., Riley, R. D., Buckman, J. E. J., Chew-Graham, C. A., Gilbody, S. M., Ali, S., Pilling, S., et al. (2024) · BMJ Mental Health · doi:10.1136/bmjment-2024-301226 · Cited by 4 · see citing works
Self-citationA Systematic Review of Long-term Antidepressant Outcomes in Comorbid Depression and Type 2 DiabetesPreprint
Jeffery, A., Buckman, J. E. J., Francis, E., Walters, K. R., Wong, I. C. K., Osborn, D., & Hayes, J. (2022) · medRxiv · doi:10.1101/2022.04.11.22273519 · Cited by 5 · see citing works
Self-citationOsborn, T. G., Saunders, R., & Fonagy, P. (2025) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-025-02922-3 · Cited by 2 · see citing works
“We set the caliper at 0.02 standard deviations of the propensity score [40] and matched each case to its nearest neighbour to ensure optimal pairings in all scenarios [39, 41].”
InfluentialSelf-citationGliske, K., Berry, K. R., Ballard, J., Schmidt, C., Kroll, E., Kohlmeier, J., Killian, M., & Fenkel, C. (2023) · JMIR Formative Research · doi:10.2196/47917 · Cited by 1 · see citing works
“Although previous analyses have identified subgroups based on the levels of mental health acuity at intake [19,21], this is the first study to our knowledge to include self-harm.”
BackgroundBonnet, U. (2024) · Fortschritte der Neurologie · Psychiatrie · doi:10.1055/a-2332-6107 · Cited by 1 · see citing works
“All these re-sults seem to support the conclusion that CBT is more beneficial than AD for relapse prevention in MDD [7–9].”
ResultZhang, T., Buckman, J. E. J., Suh, J. W., Stott, J. C. H., Singh, S., Jena, R., Naqvi, S. A., Pilling, S., Cape, J. D., & Saunders, R. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.08.027 · Cited by 1 · see citing works
Self-citationRauf, H. A., Noor, F., & Maynard, S. A. (2025) · Counselling and Psychotherapy Research · doi:10.1002/capr.12888 · Cited by 1 · see citing works
Carr, C. E., Millard, E., Dilgul, M., Bent, C., Wetherick, D., French, J., & Priebe, S. (2024) · Figshare · doi:10.6084/m9.figshare.26587212
Reneses, B., García-Huéscar, M., Argudo, I., Machín, M., Bravo, G., Saz, M., Zamarro, M. L., Díaz‐Marsá, M., & Fraguas, D. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.121128
Precision Approaches for Scalable Digital and Clinic-Based Interventions in Mental Health
Bohnert, A. S. B., Fritsche, L., & Sen, S. (2026) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2026.01.004
- Authors
- Saunders, R., Cohen, Z. D., Ambler, G., DeRubeis, R. J., Wiles, N., Kessler, D., Gilbody, S., Hollon, S. D., Kendrick, T., Watkins, E., Richards, D., Brabyn, S., Littlewood, E., Sharp, D., Lewis, G., Pilling, S., & Buckman, J. E. J.
- Affiliations
- University College London (GB)
- University of California, Los Angeles (US)
- Department of Psychology, School of Arts and Sciences, 425 S. University Avenue, Philadelphia, PA 19104, USA
- University of Bristol (GB)
- University of York (GB)
- Vanderbilt University (US)
- University of Southampton (GB)
- Department of Psychology, University of Exeter, Sir Henry Wellcome Building for Mood Disorders Research, Perry Road, Exeter EX4 4QG, UK
- Western Norway University of Applied Sciences (NO)
- St Pancras Hospital (GB)
- Corresponding author
- Buckman, Joshua E. J.
- ORCID
- Venue
- Journal of Personalized Medicine
- Publisher
- MDPI AG
- Published
- 2021-12-04
- Volume
- 11
- Issue
- 12
- Pages
- 1295
- ISSN
- 2075-4426 (ISSN-L)
- PMC
- PMC8703621
- OpenAlex
- W4200528146
- Cited by
- 21
- References
- 70
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 5 · 2023: 3 · 2022: 7
- FWCI
- 2.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Personalized Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Open Research Exeter (University of Exeter) (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (201292/Z/16/Z)
- MQ Foundation (MQDS16/72)
- Higher Education Funding Council for England (-)
- UCL Biomedical Research Centre (-)
- University College London (-)
- Vanderbilt University (-)
- University of Southampton (-)
- University of Exeter (-)
- University of York (-)
- NIHR Bristol Biomedical Research Centre (-)
- Topics
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Keywords
- chronic depression, depression relapse, patient stratification, latent profile analysis, symptom severity, depression duration, anxiety, antidepressants, psychotherapy, usual care, treatment decision-making, personalized treatment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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