Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach

Dinga, Richard; Marquand, Andre F.; Veltman, Dick J.; Beekman, Aartjan T. F.; Schoevers, Robert A.; van Hemert, Albert M.; Penninx, Brenda W. J. H.; Schmaal, Lianne

doi:10.1038/s41398-018-0289-1

Cited by 125Treatment of Major DepressionAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesMental Health Research Topics

Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach

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Dinga, Richard, Marquand, Andre F., Veltman, Dick J., Beekman, Aartjan T. F., Schoevers, Robert A., van Hemert, Albert M., Penninx, Brenda W. J. H., Schmaal, Lianne·2018·Translational Psychiatry

DOI 10.1038/s41398-018-0289-1PMC PMC6218451

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Abstract

Many variables have been linked to different course trajectories of depression. These findings, however, are based on group comparisons with unknown translational value. This study evaluated the prognostic value of a wide range of clinical, psychological, and biological characteristics for predicting the course of depression and aimed to identify the best set of predictors. Eight hundred four unipolar depressed patients (major depressive disorder or dysthymia) patients were assessed on a set involving 81 demographic, clinical, psychological, and biological measures and were clinically followed-up for 2 years. Subjects were grouped according to (i) the presence of a depression diagnosis at 2-year follow-up (yes n = 397, no n = 407), and (ii) three disease course trajectory groups (rapid remission, n = 356, gradual improvement n = 273, and chronic n = 175) identified by a latent class growth analysis. A penalized logistic regression, followed by tight control over type I error, was used to predict depression course and to evaluate the prognostic value of individual variables. Based on the inventory of depressive symptomatology (IDS), we could predict a rapid remission course of depression with an AUROC of 0.69 and 62% accuracy, and the presence of an MDD diagnosis at follow-up with an AUROC of 0.66 and 66% accuracy. Other clinical, psychological, or biological variables did not significantly improve the prediction. Among the large set of variables considered, only the IDS provided predictive value for course prediction on an individual level, although this analysis represents only one possible methodological approach. However, accuracy of course prediction was moderate at best and further improvement is required for these findings to be clinically useful.

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The model in Dinga et al. (2018) predicted who would develop TRD (defined as chronic depression with no improvement after two years of any or no treatments) and correctly predicted about half (47%) of the TRD patients.
“The model predicted who would develop TRD (defined as chronic depression with no improvement after two years of any or no treatments), found that about half (47%) of the TRD patients were correctly predicted to be so.”
From Irremediability in psychiatric euthanasia: examining the objective standard (2023) · cites this paper as Dinga et al., 2018
The Results (Prediction of LCGA course trajectory groups; page unavailable) report 47% sensitivity for the chronic-depression group. The Methods (Definition of outcome groups and Participants; pages unavailable) define that group as having no relief from symptoms over two years and say participants could receive treatment or none. The paper does not define this outcome as treatment-resistant depression (TRD), so that label is not supported. (LLM, conf=0.88)
Experimental — accuracy not yet measured
The Dinga et al. (2018) study is the only prediction model built on naturalistic study data that the review found, and it has the longest prediction endpoint.
“This is the only prediction model built on naturalistic study data that we found, and the study with the longest prediction endpoint.”
From Irremediability in psychiatric euthanasia: examining the objective standard (2023) · cites this paper as Dinga et al., 2018
The Introduction describes a prediction model using naturalistic NESDA data with a two-year follow-up. The paper cannot establish what the citing review found or whether this was its longest prediction endpoint; the Discussion also describes a previous model predicting outcomes over 10–12 years. (LLM, conf=0.87)
Experimental — accuracy not yet measured
The Dinga et al. (2018) study lacks external validation.
“However, it lacks external validation.”
From Irremediability in psychiatric euthanasia: examining the objective standard (2023) · cites this paper as Dinga et al., 2018
Only one study involved long-term follow-up of sustained remission.
“only one study involved long-term follow-up of sustained remission (Dinga et al., 2018)”
From Irremediability in psychiatric euthanasia: examining the objective standard (2023) · cites this paper as Dinga et al., 2018

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Irremediability in psychiatric euthanasia: examining the objective standard
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“Acc F1 Other Section B. Predicting depressed patients’ development of TRD Dinga et al. (2018) Multinomial generalization of penalized elastic net logistic regression classifiers with 10-fold cross-validation Cross-validation No 0.66 0.47 0.61 Chang et al. (2019) Neural network architecture with a…”
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Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Remission During 2-Year Follow-up
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Authors: Dinga, R., Marquand, A. F., Veltman, D. J., Beekman, A. T. F., Schoevers, R. A., van Hemert, A. M., Penninx, B. W. J. H., & Schmaal, L.
Affiliations: Amsterdam Neuroscience (NL)
University Medical Center Groningen (NL)
The University of Melbourne (AU)
Corresponding author: Schmaal, Lianne
ORCID: Veltman, Dick J.
Beekman, Aartjan T. F.
Schoevers, Robert A.
Penninx, Brenda W. J. H.
Schmaal, Lianne
Venue: Translational Psychiatry
Publisher: Springer Science and Business Media LLC
Published: 2018-11-05
Volume: 8
Issue: 1
Pages: 241-241
ISSN: 2158-3188 (ISSN-L)
DOI: 10.1038/s41398-018-0289-1
PMC: PMC6218451
OpenAlex: W2900054827
Cited by: 125
References: 37
Citations by year: 2026: 11 · 2025: 16 · 2024: 10 · 2023: 17 · 2022: 25 · 2021: 22 · 2020: 22 · 2019: 2
FWCI: 7.42
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Translational Psychiatry (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, other-oa)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Pure Amsterdam UMC (publishedVersion, cc-by)
Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
Leiden Repository (Leiden University) (submittedVersion)
Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Radboud Repository (Radboud University) (submittedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
Radboud Repository (Radboud University) (submittedVersion, other-oa)
Funders: Nederlandse Organisatie voor Wetenschappelijk Onderzoek (016.156.415)
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, dysthymia, rapid remission, chronic depression, Inventory of Depressive Symptomatology, prognostic factors, latent class growth analysis, penalized logistic regression
MeSH terms: Machine Learning, Adult, Major Depressive Disorder, Depressive Disorder, Major, Female, Follow-Up Studies, Humans, Male, Middle Aged, Prognosis, Disease Progression, Dysthymic Disorder

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Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach

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