Універсітэт Арызоны і Універсітэт Радбауда прасунуліся ў лячэнні дэпрэсіі

A new study from the University of Arizona (U of A) and Radboud University in the Netherlands introduces a precision mental health care approach for treating depression. This research highlights the need for personalized treatment strategies due to the varied causes and symptoms of depression, which can involve psychological, biological, and social factors.

The study, which spanned a decade, emphasizes that depression treatment should not follow a uniform method. Current practices often rely on a trial-and-error strategy, where patients may go through multiple medications or therapies before finding one that alleviates their symptoms. According to Zachary Cohen, senior author and assistant professor at U of A, about 50% of individuals do not respond to first-line treatments. He noted the variability in treatment response, with some patients benefiting significantly while others do not.

Focusing on adult depression, the research team analyzed data from randomized clinical trials worldwide, evaluating five commonly used treatment options: antidepressant medications, cognitive therapy, behavioral therapy, interpersonal therapy, and short-term psychodynamic therapy. Ellen Driessen, lead researcher and assistant professor of clinical psychology at Radboud University, explained that the team assessed various patient characteristics, including age, gender, and comorbid conditions like anxiety or personality disorders, to determine which treatments might be more effective for specific groups.

The researchers aim to develop a clinical decision support tool that takes multiple patient variables into account to generate tailored treatment recommendations. This tool would differ from existing guidelines, which typically provide generalized recommendations. By inputting individual patient data into the tool, clinicians could receive personalized treatment suggestions designed to optimize outcomes.

The research team collected and processed data from over 60 clinical trials involving nearly 10,000 patients. They collaborated with an international group of scientists to create a robust analysis strategy. Cohen mentioned that it took five years to clean and combine existing data to build a reliable prediction model.

The study outlines a protocol for creating this clinical decision support tool, with actual development planned for the next one to two years. The team intends to conduct clinical trials to assess the effectiveness of the tool in matching patients with suitable treatments. If successful, it may be implemented in real-world clinical settings.

The envisioned tool could function as a simple computer program or web application, allowing clinicians and patients to input information easily. This advancement aims to improve the use of existing treatment resources and address the substantial personal and societal costs associated with depression.

If the tool proves effective, it could have global applications. Cohen pointed out that the variables used for the recommendations are straightforward to obtain through self-report questionnaires and clinical assessments, making the implementation cost-effective. This research represents a promising step toward more individualized mental health care, potentially leading to better outcomes for those suffering from depression.