Redefining Therapeutic Outcomes of Depression Treatment

Predicting treatment outcome in depression: an introduction into current concepts and challenges

Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.

Back to Basics: The Importance of Conceptual Clarification in Psychological Science

Although the lack of conceptual clarity has been observed to be a widespread and fundamental problem in psychology, conceptual clarification plays a mostly marginal role in psychological research. In this article, we argue that better conceptualization of psychological phenomena is needed to move psychology forward as a science. We first show how conceptual unclarity seeps through all aspects of psychological research, from everyday concepts to statistical measures. We then turn to recommendations on how to improve conceptual clarity in psychology, emphasizing the importance of seeing research as an iterative process in which it is necessary to revisit the phenomena that are the foundations of theories and models, as well as how they are conceptualized and measured.