Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients

• Published in: BMC medicine Vol. 18; no. 1; p. 400
• Main Authors: Hebbrecht, K, Stuivenga, M, Birkenhäger, T, Morrens, M, Fried, E I, Sabbe, B, Giltay, E J
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23-12-2020; BioMed Central; BMC
• Subjects: Adult; Aged; Analysis; Cluster Analysis; Clustering; Cross-Sectional Studies; Customization; Decision Support Techniques; Depressive Disorder, Major - diagnosis; Depressive Disorder, Major - epidemiology; Depressive Disorder, Major - therapy; Dynamics; Female; Heterogeneity; Humans; Individuality; Insomnia; Inter-individual variation; Intra-individual variation; Major depressive disorder; Male; Medicine; Mental depression; Middle Aged; Networks; Patients; Precision medicine; Precision Medicine - methods; Precision Medicine - standards; Precision Medicine - statistics & numerical data; Psychiatric research; Psychological manifestations of general diseases; Psychotherapy - methods; Psychotherapy - standards; Psychotropic drugs; Remission; Routine outcome monitoring; Statistical analysis; Symptom dynamics; Time Factors; Treatment Outcome; Belgium; Cluster analysis; Routine outcome monitoring; Intra-individual variation; Symptom trajectories; Inter-individual variation; Symptom dynamics; Major depressive disorder
• ISSN: 1741-7015; 1741-7015
• DOI: 10.1186/s12916-020-01867-5

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.
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.
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.
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.