Predicting recurrence of depression using cardiac complexity in individuals tapering antidepressants

Objective: It is currently unknown whether complexity and variability of cardiac dynamics predicts future depression and whether within-subject change herein precedes recurrence of depression. We test this in an innovative repeated single-subject study in individuals who had a history of depression and were tapering their antidepressantsMethods: In 50 individuals, electrocardiogram (ECG) derived Interbeat interval (IBI) time-series data were collected every morning and evening, for 4 months. Usable data were obtained from 14 participants who experienced a transition (i.e., clinically significant increase in depressive symptoms) and 14 who did not. The mean, standard deviation, Higuchi dimension and multiscale entropy, calculated from IBIs, were examined for time-trends. These quantifiers were also averaged over a baseline period and compared between the groups.Results: No consistent trends were observed in any quantifier before increases in depressive symptoms within individuals. The entropy baseline levels significantly differed between the two groups (Morning: p-value <0.001, Cohen’s d=-2.185; Evening: p-value <0.001, Cohen’s d=-1.797) and predicted the recurrence of depressive symptoms, in the current sample. Moreover, higher mean IBIs and Higuchi dimensions were observed in individuals who experienced transitions.Conclusions: While we found little evidence to support the existence of within individual warning signals in IBI time-series data preceding an upcoming depressive transition, our results indicate that individuals who taper antidepressants and showed lower entropy of cardiac dynamics exhibited a higher chance of recurrence of depression. Hence, entropy could be a potential digital phenotype for assessing the risk of recurrence of depression in the short-term while tapering antidepressants.