JMIR Publications

ABSTRACT

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, 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.