Cited by 103Treatment of Major DepressionAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesPsychotherapy Techniques and Applications

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The potential of predictive analytics to provide clinical decision support in depression treatment planning

Kessler, Ronald C.·2018·Current Opinion in Psychiatry

DOI 10.1097/yco.0000000000000377

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Abstract

PURPOSE OF REVIEW: To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD). RECENT FINDINGS: Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem. SUMMARY: Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

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Additional file 2 of The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation
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Additional file 1 of A randomized controlled trial on using predictive algorithm to adapt level of psychological care for community college students: STAND triaging and adapting to level of care study protocol
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Additional file 2 of Towards optimal treatment selection for borderline personality disorder patients (BOOTS): a study protocol for a multicenter randomized clinical trial comparing schema therapy and dialectical behavior therapy
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Additional file 3 of Towards optimal treatment selection for borderline personality disorder patients (BOOTS): a study protocol for a multicenter randomized clinical trial comparing schema therapy and dialectical behavior therapy
Wibbelink, C. J. M., Arntz, A. R., Grasman, R. P. P. P., Sinnaeve, R., Boog, M., Bremer, O. M. C., Dek, E. C. P., Alkan, S. G., James, C., Koppeschaar, A. M., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19129061
Additional file 4 of Towards optimal treatment selection for borderline personality disorder patients (BOOTS): a study protocol for a multicenter randomized clinical trial comparing schema therapy and dialectical behavior therapy
Wibbelink, C. J. M., Arntz, A. R., Grasman, R. P. P. P., Sinnaeve, R., Boog, M., Bremer, O. M. C., Dek, E. C. P., Alkan, S. G., James, C., Koppeschaar, A. M., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19129064
Additional file 1 of Towards optimal treatment selection for borderline personality disorder patients (BOOTS): a study protocol for a multicenter randomized clinical trial comparing schema therapy and dialectical behavior therapy
Wibbelink, C. J. M., Arntz, A. R., Grasman, R. P. P. P., Sinnaeve, R., Boog, M., Bremer, O. M. C., Dek, E. C. P., Alkan, S. G., James, C., Koppeschaar, A. M., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19129055
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Development of a baseline battery to optimize depression treatment assignment in primary care: Balancing breadth and brevity
Pozuelo, J. R., Kessler, R. C., Patel, V., Lahiri, A., Parashar, Y., Herzallah, M. M., Khanduri, M., O’Neill, K., Martinez, A., Bhan, A., et al. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.121674
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Authors: Kessler, R. C.
Affiliations: Harvard University (US)
Corresponding author: Kessler, Ronald C.
ORCID: Kessler, Ronald C.
Venue: Current Opinion in Psychiatry
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2018-01
Volume: 31
Issue: 1
Pages: 32-39
ISSN: 0951-7367 (ISSN-L)
DOI: 10.1097/yco.0000000000000377
OpenAlex: W2765870488
Cited by: 103
References: 88
Citations by year: 2026: 4 · 2025: 10 · 2024: 9 · 2023: 14 · 2022: 17 · 2021: 14 · 2020: 11 · 2019: 18 · 2018: 6
FWCI: 1.22
Type: journal article
Language: en
Open access: closed
Locations: Current Opinion in Psychiatry (publishedVersion)
PubMed (publishedVersion)
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 › Clinical Psychology › Psychotherapy Techniques and Applications
Keywords: predictive analytics, clinical decision support, major depressive disorder, personalized treatment, treatment response prediction, antidepressants, psychotherapy, combination therapy, remission prediction, biomarkers, self-report scale, neurocognitive tests, pragmatic trials
MeSH terms: Antidepressive Agents, Combined Modality Therapy, Major Depressive Disorder, Humans, Prospective Studies, Psychotherapy, Decision Support Systems, Clinical

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The potential of predictive analytics to provide clinical decision support in depression treatment planning

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