Major depressive disorder (MDD) is a common and burdensome severe mental disorder, which is expected to become the leading cause of disease burden worldwide. Most patients with MDD remain untreated/undertreated. For many decades “a trial and error” approach has been adopted for selecting the best treatment plan for each individual patient, but more recently a personalized treatment approach has been proposed, by taking into account several individual and clinical factors (e.g., clinical stage, comorbidity, duration of illness). Therefore, the aim of this study is to address the most relevant innovations in the personalized treatment plan for patients with MDD.
Recent findingsIn recent years, several pharmacological and nonpharmacological innovations have been introduced in the treatment of patients with MDD. As regards pharmacological treatments, the newly developed drugs have an innovative mechanism of action, targeting the glutamatergic systems. These drugs are highly effective in improving depressive symptoms, with a good level of safety and tolerability. As regards nonpharmacological interventions, innovations include both new strategies targeting different domains (e.g., lifestyle interventions aiming to improve the physical symptoms of depression or virtual reality) and classical interventions provided through innovative mechanisms (e.g., web-based psychotherapies and use of digital approaches). Patients globally report a good level of acceptability of these interventions.
SummaryDepression is a heterogeneous, complex and multidimensional disorder, representing one of the leading causes of disability worldwide. The final aim of the management of patients is functional recovery, which can be achieved by using personalized, integrated and recovery-oriented interventions. Several innovative pharmacological and nonpharmacological treatments are now available; interventions should be selected on the basis of the patient's needs and preferences in order to tailor the treatment, according to a shared decision-making approach.
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