Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatien
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RESEARCH ARTICLE
Open Access
Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients K. Hebbrecht1,2*, M. Stuivenga1,2, T. Birkenhäger1,2,3, M. Morrens1,2, E. I. Fried5, B. Sabbe1,2 and E. J. Giltay1,2,4*
Abstract Background: Major depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom clusters and networks have mostly been studied using cross-sectional designs, temporal dynamics of symptoms within patients may yield information that facilitates personalized medicine. Here, we aim to cluster depressive symptom dynamics through dynamic time warping (DTW) analysis. Methods: The 17-item Hamilton Rating Scale for Depression (HRSD-17) was administered every 2 weeks for a median of 11 weeks in 255 depressed inpatients. The DTW analysis modeled the temporal dynamics of each pair of individual HRSD-17 items within each patient (i.e., 69,360 calculated “DTW distances”). Subsequently, hierarchical clustering and network models were estimated based on similarities in symptom dynamics both within each patient and at the group level. Results: The sample had a mean age of 51 (SD 15.4), and 64.7% were female. Clusters and networks based on symptom dynamics markedly differed across patients. At the group level, five dynamic symptom clusters emerged, which differed from a previously published cross-sectional network. Patients who showed treatment response or remission had the shortest average DTW distance, indicating denser networks with more synchronous symptom trajectories. Conclusions: Symptom dynamics over time can be clustered and visualized using DTW. DTW represents a promising new approach for studying symptom dynamics with the potential to facilitate personalized psychiatric care. Keywords: Major depressive disorder, Routine outcome monitoring, Cluster analysis, Symptom dynamics, Interindividual variation, Intra-individual variation, Symptom trajectories
Background Depression is defined by its symptoms (such as a sad mood and insomnia) that are correlated with each other. The dominant explanation in the field has been that these relations stem from a shared causal origin, a * Correspondence: [email protected]; [email protected] 1 Collaborative Antwerp Psychiatric Research Institute (CAPRI), Department of Biomedical Sciences, University of Antwerp, Stationsstraat 22c, 2570 Duffel, Belgium Full list of author information is available at the end of the article
perspective termed the common cause framework [1, 2]. The contemporary conceptualization for major depressive disorder (MDD) is similar to that of other medical conditions in that it assumes all observable depressive symptoms are caused by an underlying disease construct [1, 3]. In research, symptoms are usually added up to sum scores, and thresholds are used to indicate case status. This approach assumes that symptoms are equivalent, causally independent, and roughly inter
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