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

Abstract

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.

Creating sparser prediction models of treatment outcome in depression: a proof-of-concept study using simultaneous feature selection and hyperparameter tuning

“…With respect to treatment outcome, we selected a reduction of ≥ 50% on a symptom scale sum score after 6 weeks of treatment as the target variable for the clinical data set because it represents one of the most widely used definitions of treatment outcome in MDD research. Recently, more and more critique has come up on MDD measurement in general [54] and on symptom scale sum score-based outcome definitions in particular (for a review, see [55], for instance). The definition of response used here represents an artificial dichotomization of an ordinal scale and is therefore associated with loss of information [56].…”