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Cited by 94Treatment of Major DepressionAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesMental Health Treatment and Access

A New Prediction Model for Evaluating Treatment-Resistant Depression

Kautzky, Alexander, Baldinger-Melich, Pia, Kranz, Georg S., Vanicek, Thomas, Souery, Daniel, Montgomery, Stuart, Mendlewicz, Julien, Zohar, Joseph +3 more·2017·The Journal of Clinical Psychiatry
DOI 10.4088/jcp.15m10381
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Abstract

OBJECTIVE: Despite a broad arsenal of antidepressants, about a third of patients suffering from major depressive disorder (MDD) do not respond sufficiently to adequate treatment. Using the data pool of the Group for the Study of Resistant Depression and machine learning, we intended to draw new insights featuring 48 clinical, sociodemographic, and psychosocial predictors for treatment outcome. METHOD: Patients were enrolled starting from January 2000 and diagnosed according to DSM-IV. Treatment-resistant depression (TRD) was defined by a 17-item Hamilton Depression Rating Scale (HDRS) score ≥ 17 after at least 2 antidepressant trials of adequate dosage and length. Remission was defined by an HDRS score < 8. Stepwise predictor reduction using randomForest was performed to find the optimal number for classification of treatment outcome. After importance values were generated, prediction for remission and resistance was performed in a training sample of 400 patients. For prediction, we used a set of 80 patients not featured in the training sample and computed receiver operating characteristics. RESULTS: The most useful predictors for treatment outcome were the timespan between first and last depressive episode, age at first antidepressant treatment, response to first antidepressant treatment, severity, suicidality, melancholia, number of lifetime depressive episodes, patients' admittance type, education, occupation, and comorbid diabetes, panic, and thyroid disorder. While single predictors could not reach a prediction accuracy much different from random guessing, by combining all predictors, we could detect resistance with an accuracy of 0.737 and remission with an accuracy of 0.850. Consequently, 65.5% of predictions for TRD and 77.7% for remission can be expected to be accurate. CONCLUSIONS: Using machine learning algorithms, we could demonstrate success rates of 0.737 for predicting TRD and 0.850 for predicting remission, surpassing predictive capabilities of clinicians. Our results strengthen data mining and suggest the benefit of focus on interaction-based statistics. Considering that all predictors can easily be obtained in a clinical setting, we hope that our model can be tested by other research groups.

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  • A second set of five studies, involving large STAR*D or GRSD datasets, identified which depressed patients would not respond to their second or subsequent antidepressant trial.

    “A second set of five studies, involving large STAR*D or GRSD (Group for the Study of Resistant Depression) datasets, identified which depressed patients would not respond to their second (Kautzky et al., 2017, 2018, 2019; Perlis, 2013) or subsequent (Nie, Vairavan, Narayan, Ye, & Li, 2018) antidepressant trial.”

    From Irremediability in psychiatric euthanasia: examining the objective standard (2023) · cites this paper as Kautzky et al., 2017

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Authors
Kautzky, A., Baldinger-Melich, P., Kranz, G. S., Vanicek, T., Souery, D., Montgomery, S., Mendlewicz, J., Zohar, J., Serretti, A., Lanzenberger, R., & Kasper, S.
Affiliations
  • Medical University of Vienna (AT)
  • Université Libre de Bruxelles (BE)
  • University of London (GB)
  • Sheba Medical Center (IL)
  • University of Bologna (IT)
ORCID
Venue
The Journal of Clinical Psychiatry
Publisher
Physicians Postgraduate Press, Inc
Published
2017-01-03
Volume
78
Issue
02
Pages
215-222
ISSN
0160-6689 (ISSN-L)
DOI
10.4088/jcp.15m10381
OpenAlex
W2566453300
Cited by
94
References
0
Citations by year
2026: 4 · 2025: 9 · 2024: 6 · 2023: 12 · 2022: 10 · 2021: 17 · 2020: 10 · 2019: 10 · 2018: 12 · 2017: 3 · 2016: 1
FWCI
6.68
Type
journal article
Language
en
Open access
closed
Locations
  • The Journal of Clinical Psychiatry (publishedVersion)
  • PubMed (publishedVersion)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
  • Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
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
treatment-resistant depression, major depressive disorder, treatment outcome prediction, remission prediction, antidepressant response, clinical predictors, sociodemographic predictors, psychosocial predictors, suicidality, melancholia, comorbidities
MeSH terms
Machine Learning, Algorithms, Antidepressive Agents, Demography, Depressive Disorder, Major, Major Depressive Disorder, Humans, Models, Psychological, Prognosis, Psychiatric Status Rating Scales, Remission Induction, Reproducibility of Results, Comorbidity, Outcome Assessment, Health Care, Depressive Disorder, Treatment-Resistant, Sociological Factors

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