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Depression involves a complex interplay of psychological patterns, biological vulnerabilities and social stressors, making its causes and symptoms highly variable.
Equally complex is the treatment of depression, which requires a highly individualized approach that may involve a combination of medication, psychotherapy and lifestyle changes.
In a decadelong multiinstitutional study, University of Arizona psychologists and Radboud University technologists have developed a precision treatment approach for depression. This gives patients individualised recommendations based on multiple characteristics, such as age and gender.
The theoretical basis is that firstline treatment for depression should not be a onesizefitsall approach. This is according to Zachary Cohen, senior author on the paper and assistant professor in the Department of Psychology.
The current standard of care largely involves a trialanderror approach, in which different medications or therapies are tried until an intervention or combination that effectively alleviates symptoms is found:
“About 50% of people don’t respond to firstline treatments for depression. There’s a lot of heterogeneity of treatment response, meaning that there are some people who respond really well and some people who don’t.“
The study focused specifically on depression in adults. The research team brought together patient data from randomized clinical trials conducted worldwide that have assessed the efficacy of five widely used depression treatments.
Before treatment, patients were evaluated on a variety of dimensions, including for associated psychiatric conditions such as anxiety and personality disorders, said Ellen Driessen, the study’s lead researcher and assistant professor of clinical psychology at Radboud University.
The researchers hope their results will lead to the creation of a clinical decision support tool, an algorithm that simultaneously considers many variables, such as age, gender and comorbid conditions and the relationships among the variables to create a single recommendation. Once the patient’s variables are fed into the tool, it will generate a personalised recommendation as opposed to a guideline that provides a list of generalised recommendations.
The data that the team generated looked at the patients’ outcomes from clinical trials of antidepressant medications, cognitive therapy, behavioral therapy, interpersonal therapy, and shortterm psychodynamic therapy, a form of indepth talk therapy.
The research group spent around 10 years collecting and processing data from over 60 trials involving almost 10,000 patients. Researchers from different parts of the world participated in the initiative by sharing data from their studies. The research group also brought together an international group of scientists from different disciplines to develop the strategy for analyzing the data.
Going forward, the team plans to conduct a clinical trial evaluating the benefits of using a clinical decision support tool to help match patients to their optimal treatment. If the results are favourable, the tool could be scaled up and implemented in realworld clinical contexts. The researchers envision the tool to be a simple computer program or web application in which patient information can be entered.
The scientists hope to provide clinicians, people with depression, and society a means to make more efficient use of existing treatment resources and help reduce the immense personal and societal costs associated with depression.
The research appears in the journal PLoS One, with the paper titled “Developing a multivariable prediction model to support personalized selection among five major empiricallysupported treatments for adult depression. Study protocol of a systematic review and individual participant data network metaanalysis.”
