Seeing the signs: Using the course of residual depressive symptomatology to predict patterns of relapse and recurrence of major depressive disorder
Open accessAbstract
BACKGROUND: Major depressive disorder (MDD) is characterized by high relapse/recurrence rates. Predicting individual patients' relapse/recurrence risk has proven hard, possibly due to course heterogeneity among patients. This study aimed to (1) identify homogeneous data-driven subgroups with different patterns of relapse/recurrence and (2) identify associated predictors. METHODS: For a year, we collected weekly depressive symptom ratings in 213 primary care MDD patients. Latent class growth analyses (LCGA), based on symptom-severity during the 24 weeks after no longer fulfilling criteria for the initial major depressive episode (MDE), were used to identify groups with different patterns of relapse/recurrence. Associations of baseline predictors with these groups were investigated, as were the groups' associations with 3- and 11-year follow-up depression outcomes. RESULTS: LCGA showed that heterogeneity in relapse/recurrence after no longer fulfilling criteria for the initial MDE was best described by four classes: "quick symptom decline" (14.0%), "slow symptom decline" (23.3%), "steady residual symptoms" (38.7%), and "high residual symptoms" (24.1%). The latter two classes showed lower self-esteem at baseline, and more recurrences and higher severity at 3-year follow-up than the first two classes. Moreover, the high residual symptom class scored higher on neuroticism and lower on extraversion and self-esteem at baseline. Interestingly, the steady residual symptoms and high residual symptoms classes still showed higher severity of depressive symptoms after 11 years. CONCLUSION: Some measures were associated with specific patterns of relapse/recurrence. Moreover, the data-driven relapse/recurrence groups were predictive of long-term outcomes, suggesting that patterns of residual symptoms could be of prognostic value in clinical practice.
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Prior episodes and residual depressive symptoms remain the best predictors of future episodes.
“Prior episodes and residual depressive symptoms remain the best predictors of future episodes (Eaton et al., 2008, Solomon et al., 2000, Verhoeven et al., 2018)”
From Static and treatment-responsive brain biomarkers of depression relapse vulnerability following prophylactic psychotherapy: Evidence from a randomized control trial (2022) · cites this paper as Verhoeven et al., 2018
The Introduction (page not provided) identifies residual symptoms and the number of previous episodes as predictors of depressive illness course or recurrence. But it does not rank them as the best predictors. Section 3.3 (page not provided) reports that the number of previous episodes did not predict residual-symptom patterns in this study. (LLM, conf=0.87)
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- Authors
- Verhoeven, F. E. A., Wardenaar, K. J., Ruhé, H. G. E., Conradi, H. J., & de Jonge, P.
- Affiliations
- University Medical Center Groningen (NL)
- University of Amsterdam (NL)
- Faculty of Behavioural and Social Sciences; Department of Developmental Psychology; University of Groningen; Groningen The Netherlands
- Corresponding author
- Verhoeven, Floor E.A.
- Venue
- Depression and Anxiety
- Publisher
- Wiley
- Published
- 2017-12-11
- Volume
- 35
- Issue
- 2
- Pages
- 148-159
- ISSN
- 1091-4269 (ISSN-L)
- OpenAlex
- W2771593976
- Cited by
- 31
- References
- 55
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 6 · 2023: 2 · 2022: 5 · 2021: 3 · 2020: 4 · 2019: 5 · 2018: 2
- FWCI
- 2.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Depression and Anxiety (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (91812607;, 016.126.059;)
- Netherlands Foundation for Mental Health
- Medical Sciences Program and Chronic Diseases Program
- Research Foundations of the Health Insurance Company
- Regional Health Insurance Company
- University Hospital Groningen
- ZonMw (91812607)
- Universitair Medisch Centrum Groningen
- 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 › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- major depressive disorder, relapse, recurrence, residual depressive symptoms, symptom course, prognosis, latent class growth analysis, primary care patients, self-esteem, neuroticism, extraversion, long-term outcomes
- MeSH terms
- Adult, Major Depressive Disorder, Female, Follow-Up Studies, Humans, Male, Middle Aged, Recurrence, Disease Progression
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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