Brain and Behavior (Jul 2021)

Self‐report screening instruments differentiate bipolar disorder and borderline personality disorder

  • Brian A. Palmer,
  • Mehak Pahwa,
  • Jennifer R. Geske,
  • Simon Kung,
  • Malik Nassan,
  • Kathryn M. Schak,
  • Renato D. Alarcon,
  • Mark A. Frye,
  • Balwinder Singh

DOI
https://doi.org/10.1002/brb3.2201
Journal volume & issue
Vol. 11, no. 7
pp. n/a – n/a

Abstract

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Abstract Background Bipolar disorder (BD) and borderline personality disorder (BPD) share overlapping phenomenology and are frequently misdiagnosed. This study investigated the diagnostic accuracy of the Mood Disorder Questionnaire (MDQ) and McLean Screening Instrument for Borderline Personality Disorder (MSI) in a clinical inpatient setting and whether individual screening items could differentiate BD from BPD. Methods 757 sequential inpatients admitted to a Mood Disorder Unit completed both the MDQ and MSI. Screen positive for the MDQ was defined as ≥7/13 symptoms endorsed with concurrence and at least moderate impact. Screen positive for the MSI was defined as a score of ≥7. The clinical discharge summary diagnosis completed by a board‐certified psychiatrist was used as the reference standard to identify concordance rates of a positive screen with clinical diagnosis. Individual items predicting one disorder and simultaneously predicting absence of other disorder by odds ratio (OR>and <1) were identified. Results Both screening instruments were more specific than sensitive (MDQ 83.7%/ 67.8%, MSI 73.2% / 63.3%). MDQ individual items (elevated mood, grandiosity, increased energy, pressured speech, decreased need for sleep, hyperactivity) were significant predictors of BD diagnosis and non‐predictors of BPD diagnosis. Whereas MSI subitem, self‐harm behaviors/suicidal attempts predicted BPD in the absence of BD; distrust and irritability were additional predictors of BPD. Conclusion While this study is limited by the lack of structured diagnostic interview, these data provide differential symptoms to discriminate BD and BPD. Further work with larger datasets and more rigorous bioinformatics machine learning methodology is encouraged to continue to identify distinguishing features of these two disorders to guide diagnostic precision and subsequent treatment recommendations.

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