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.