Grant: Fellowship PA: Mach-Gaensslen Found. -Mental Health, Addictions/Cognitive Health

Selecting the best treatment for clinical depression can be challenging as multiple factors can contribute to poor outcomes, such as poor early treatment response, physical and psychiatric comorbidities, or treatment resistance. Based on a study recently published by our group, we developed a decision tree to determine whether an older adult with depression should switch to an alternative treatment or continue the same treatment. Early treatment response, duration of the depressive episode, and a history of non-response to a previous antidepressant were identified as predictors in this decision tree. Our findings support that readily available clinical information can be used to guide treatment decisions for a more personalized approach to depression treatment. This proposal aims to design decision trees for three clinical scenarios where treatment of depression becomes more challenging: a) when a patient who is started on an antidepressant is not showing treatment response after four weeks; b) when a patient has depression with comorbid alcohol misuse; or c) when a patient has previously not responded to treatment. Data from three large depression clinical trials will be used to construct decision trees for: a) deciding which patients would benefit from changing to a different treatment vs. staying on the same treatment after they do not achieve treatment response after four weeks; b) deciding which patients with depression and comorbid alcohol misuse are most likely to benefit from addition of telephone-based support; and c) deciding which patients with treatment resistant depression would benefit from a switch to a different medication vs. addition of another medication. These decision trees will incorporate patient-specific factors than can be collected in a routine clinical encounter. Findings from this project can be used to guide clinical decisions for a more personalized approach to the treatment of depression.