Advances in digital psychiatry: Towards an extended definition of major depressive disorder symptomatology (Preprint)

BACKGROUND Diagnosing major depressive disorder (MDD) is challenging, with diagnostic manuals failing to capture the wide range of clinical symptoms that are endorsed by individuals with the condition. OBJECTIVE The aim of this study was to provide evidence for an extended definition of MDD symptomatology. METHODS Symptom data were collected via a digital assessment that was developed for the Delta Study [1]. Random forest classification with nested cross-validation was used to distinguish between individuals with MDD and those with subthreshold symptomatology of the disorder using i) disorder-specific symptoms and ii) transdiagnostic symptoms. The diagnostic performance of the Patient Health Questionnaire-9 (PHQ-9) was also examined. RESULTS A depression-specific model demonstrated good predictive performance when distinguishing between individuals with MDD (n = 64) and those with subthreshold depression (n = 140) (AUC = .89; sensitivity = 82.4%; specificity = 81.3%; accuracy = 81.6%). The inclusion of transdiagnostic symptoms of psychopathology, including symptoms of depression, generalized anxiety disorder, insomnia, emotional instability, and panic disorder, significantly improved the model performance (AUC = .95; sensitivity = 86.5%; specificity = 90.8%; accuracy = 89.5%). The PHQ-9 was excellent at identifying MDD but over diagnosed the condition (sensitivity = 92.2%; specificity = 54.3%; accuracy = 66.2%). CONCLUSIONS Our findings are in line with the notion that current diagnostic practices may present an overly narrow conception of mental health. Further, our study provides proof-of-concept support for the clinical utility of a digital assessment to inform clinical decision-making in the evaluation of MDD. CLINICALTRIAL