A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder.

Autor: Perlman K; Montreal Neurological Institute, McGill University, 3801 Rue Université, Montréal, QC H3A 2B4, Canada. Electronic address: kelly.perlman@mail.mcgill.ca., Benrimoh D; Department of Psychiatry, McGill University, Montreal, Canada; Faculty of Medicine, McGill University, Montreal, Canada., Israel S; Department of Psychiatry, McGill University, Montreal, Canada; Douglas Mental Health University Institute, Montreal, Canada., Rollins C; Department of Psychiatry, University of Cambridge, Cambridge, England, UK., Brown E; Montreal Neurological Institute, McGill University, 3801 Rue Université, Montréal, QC H3A 2B4, Canada., Tunteng JF; Montreal Children's Hospital, McGill University Health Center, Montreal, Canada., You R; School of Physical and Occupational Therapy, McGill University, Montreal, Canada., You E; Faculty of Medicine, McGill University, Montreal, Canada., Tanguay-Sela M; Montreal Neurological Institute, McGill University, 3801 Rue Université, Montréal, QC H3A 2B4, Canada., Snook E; Douglas Mental Health University Institute, Montreal, Canada., Miresco M; Department of Psychiatry, Jewish General Hospital, Montreal, Canada., Berlim MT; Department of Psychiatry, McGill University, Montreal, Canada; Douglas Mental Health University Institute, Montreal, Canada.
Jazyk: angličtina
Zdroj: Journal of affective disorders [J Affect Disord] 2019 Jan 15; Vol. 243, pp. 503-515. Date of Electronic Publication: 2018 Sep 18.
DOI: 10.1016/j.jad.2018.09.067
Abstrakt: Introduction: The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools.
Methods: The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria.
Results: An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility.
Limitations: Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor.
Conclusion: Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field.
(Copyright © 2018 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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