Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics in the assessment of major depressive disorder
Authors: Ebrahimi Omid V. , Borsboom Denny , Hoekstra Ria H. A. , Epskamp Sacha , Ostinelli Edoardo Giuseppe , Bastiaansen Jojanneke A. , Cipriani Andrea 
Published: August 2023

BackgroundMajor depressive disorder (MDD) is a heterogeneous mental disorder. International guidelines present overall symptom severity as the key dimension for clinical characterisation. However, additional layers of individual differences may reside within severity levels related to differences in how symptoms interact with one-another in a given patient, referred to as symptom dynamics. We investigate these individual differences by estimating the proportion of patients that display differences in their symptom relationship patterns while sharing the same overall symptom severity.MethodsPatients with MDD recruited at four centres in the Netherlands between 2016-2018 rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for symptoms were collected through Ecological Momentary Assessments scheduled to measure each patient five times per day for 28 days. Each patient’s symptom dynamics were estimated using dynamic networks based on the graphical vector autoregressive model. Individual differences in these symptom relationship patterns in groups of patients sharing the same symptom severity levels were estimated using Individual Network Invariance Tests, before the overall proportion of patients that displayed differential symptom dynamics while sharing the same symptom severity was calculated. To compute 95% bootstrapped confidence intervals around this proportion, 10,000 re-estimations following random draws with replacement of the symptom severity groups were performed. A supplementary simulation study was conducted to investigate the accuracy of our methodology by identifying its average false positive detection rate in a simulated scenario where no individual differences should be present.ResultsOut of 74 patients, 73 were analysed (Mage = 34.57 [SD 13.12]; 56.16% females; 63.01% employed) and 8,395 observations were conducted across the 28-day period (average completed assessments per person: 115; SD 16.81). 23 severity levels (IDS-SR values) were observed in the sample, enabling the investigation of individual differences in 23 groups of MDD patients (between 2 to 6 patients per group) sharing the same severity. Differential symptom dynamics were identified across 63.01% (95% bootstrapped CI 40.98, 82.05) of patients displaying the same severity. The simulation study revealed our method’s average false detection of individual differences in our scenario to be 2.22%.ConclusionsThe majority of MDD patients sharing the same symptom severity displayed differential symptom dynamics. Our findings were robust against false positive conclusions. Examining symptom dynamics provides information about the person-specific psychopathological expression of patients beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may serve as a promising dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.