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

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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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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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Fig. 1 Model predictions. Confusion matrices for classifiers are depicted in panel a for binary prediction, i.e., presence or absence of a unipolar depression diagnosis at follow-up (major depressive disorder or dysthymia), and b for prediction of the three LCGA course trajectory groups. Number and color in each cell describe the proportion of predictions. For example, chance level would be 0.5 in each cell in the confusion matrix in a, and 0.333 in the confusion matrix in b. Violin plots of the spread of predicted values are depicted in panel c for binary prediction, i.e., presence or absence of a unipolar depression diagnosis at follow-up, and d for predicting the three course trajectory groups
Fig. 1 Model predictions. Confusion matrices for classifiers are depicted in panel a for binary prediction, i.e., presence or absence of a unipolar depression diagnosis at follow-up (major depressive disorder or dysthymia), and b for prediction of the three LCGA course trajectory groups. Number and color in each cell describe the proportion of predictions. For example, chance level would be 0.5 in each cell in the confusion matrix in a, and 0.333 in the confusion matrix in b. Violin plots of the spread of predicted values are depicted in panel c for binary prediction, i.e., presence or absence of a unipolar depression diagnosis at follow-up, and d for predicting the three course trajectory groupsCC BY · PubMed Central
Fig. 2 Stability paths. Stability paths of elastic-net logistic regression showing selection probabilities of each variable with respect to amount of applied regularization. The less regularization is applied, the more variables will be included in the model and the higher the chance for a false-positive selection. The stability selection approach allows us to statistically control for false-positive discovery. Variables crossing the marked regions are statistically significantly related to the outcome variable with the error correction pfwer < 0.05 according to the stability selection theory. Other variables that crossed the probability threshold (they have been selected at least 75% of times under resampling) might also be important, but they did not survive the multiple comparison correction. a, b Logistic regression trained on all variables. c, d Logistic regression trained only on the individual items from the inventory of depressive symptomatology (IDS) questionnaire
Fig. 2 Stability paths. Stability paths of elastic-net logistic regression showing selection probabilities of each variable with respect to amount of applied regularization. The less regularization is applied, the more variables will be included in the model and the higher the chance for a false-positive selection. The stability selection approach allows us to statistically control for false-positive discovery. Variables crossing the marked regions are statistically significantly related to the outcome variable with the error correction pfwer < 0.05 according to the stability selection theory. Other variables that crossed the probability threshold (they have been selected at least 75% of times under resampling) might also be important, but they did not survive the multiple comparison correction. a, b Logistic regression trained on all variables. c, d Logistic regression trained only on the individual items from the inventory of depressive symptomatology (IDS) questionnaireCC BY · PubMed Central
Fig. 3 Performance of different data modalities. Mean area under the curve for predictive models of naturalistic course of depression. a Predicting the presence or absence of a unipolar depression diagnosis 2 years after the baseline measurement. b Predicting the three depression course trajectory groups
Fig. 3 Performance of different data modalities. Mean area under the curve for predictive models of naturalistic course of depression. a Predicting the presence or absence of a unipolar depression diagnosis 2 years after the baseline measurement. b Predicting the three depression course trajectory groupsCC BY · PubMed Central

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Metadata

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)
  • Leiden University Medical Center (NL)
  • The University of Melbourne (AU)
Corresponding author
Schmaal, Lianne
ORCID
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
Licence
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 (medical) › 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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