Prediction of Near-term Increases in Suicidal Ideation in Recently Depressed Patients With Bipolar Ii Disorder Using Intensive Longitudinal Data

Authors: Colin A.Depp, Wesley K. Thompson, Ellen Frank, Holly A.Swartz

Abstract

Background: There are substantial gaps in understanding near-term precursors of suicidal ideation in bipolar II disorder. We evaluated whether repeated patient-reported mood and energy ratings predicted subsequent near-term increases in suicide ideation.

Methods: Secondary data were used from 86 depressed adults with bipolar II disorder enrolled in one of 3 clinical trials evaluating Interpersonal and Social Rhythm Therapy and/or pharmacotherapy as treatments for depression. Twenty weeks of daily mood and energy ratings and weekly Hamilton Depression Rating Scale (HDRS) were obtained. Penalized regression was used to model trajectories of daily mood and energy ratings in the 3 week window prior to HDRS Suicide Item ratings.

Results: Participants completed an average of 68.6 (sd=52) days of mood and energy ratings. Aggregated across the sample, 22% of the 1675 HDRS Suicide Item ratings were non-zero, indicating presence of at least some suicidal thoughts. A cross-validated model with longitudinal ratings of energy and depressed mood within the three weeks prior to HDRS ratings resulted in an AUC of 0.91 for HDRS Suicide item >2, accounting for twice the variation when compared to baseline HDRS ratings. Energy, both at low and high levels, was an earlier predictor than mood.

Limitations: Data derived from a heterogeneous treated sample may not generalize to naturalistic samples. Identified suicidal behavior was absent from the sample so it could not be predicted.

Conclusions: Prediction models coupled with intensively gathered longitudinal data may shed light on the dynamic course of near-term risk factors for suicidal ideation in bipolar II disorder.

LINK TO PUBLICATION:
Prediction of near-term increases in suicidal ideation in recently depressed patients with bipolar II disorder using intensive longitudinal data

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