Associations between NIH Toolbox Emotion Battery measures and previous suicide attempt in bipolar I disorder

Abstract

Suicide attempts are more prevalent in people with bipolar I disorder (BD-I) than in the general population. Most prior studies of suicide in BD-I have focused on separate emotion-related assays or clinician-administered scales, whereas a single, brief, and multidimensional battery of self-report measures has not yet been explored. Here, we utilized the NIH Toolbox Emotion Battery (NIHTB-EB) to assess various emotional measures, determine which were cross-sectionally associated with prior suicide attempt in BD-I, evaluate whether the NIHTB-EB could be used to identify past suicide attempt in BD-I with machine learning, and compare model performance versus using clinical mood scales. The study included 39 participants with BD-I and history of suicide attempt, 48 with BD-I without history of suicide attempt, and 58 controls. We found that 9 of the 17 measures were associated with past suicide attempt in BD-I. The initial random forest model indicated that the most important distinguishing variables were perceived stress, emotional support, anger-hostility, anger-physical aggression, perceived rejection, loneliness, and self-efficacy. Overall, the models utilizing NIHTB-EB measures performed better (69.0 % to 70.1 % accuracy) than the model containing clinical mood scale information without the NIHTB-EB measures (57.5 % accuracy). These findings suggest the NIHTB-EB could be a useful and easy-to-deploy tool in understanding the role of emotion-related measures in suicide in BD-I. Furthermore, these results highlight specific emotional subdomains that could be promising targets for longitudinal studies or interventions aimed at reducing suicide in BD-I.

Keywords: Bipolar disorder; Emotion; Machine learning; NIH toolbox; Psychological stress; Social support; Suicide attempt.