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Testing a clinical staging model for bipolar disorder using longitudinal life chart data.

Afra van der Markt1, Ursula Mh Klumpers2, Stasja Draisma1

  • 1Department of Psychiatry, Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Bipolar Disorders
|November 18, 2018
PubMed
Summary
This summary is machine-generated.

This study validates a clinical staging model for bipolar disorder (BD), showing progression rates and identifying factors like biphasic onset and male sex that accelerate illness advancement. Understanding these factors aids in managing BD progression.

Keywords:
biphasic onsetbipolar disordermale sexmood disordersmulti-state modelstagingstaging models

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Area of Science:

  • Psychiatry
  • Clinical Psychology
  • Longitudinal Studies

Background:

  • Bipolar disorder (BD) presents with diverse clinical features and can evolve over time.
  • Understanding the trajectory of BD is crucial for effective treatment and management.

Purpose of the Study:

  • To evaluate the utility of a clinical staging model for bipolar disorder.
  • To identify variables that influence the progression of bipolar disorder through distinct illness stages.

Main Methods:

  • Utilized longitudinal life chart data from 99 participants in the Stanley Foundation Bipolar Network Naturalistic Follow-up Study.
  • Employed a multi-state model to analyze monthly occurrence, duration, and sequence of illness stages (2-4).
  • Incorporated baseline variables (Stages 0, 1) and other factors to assess their impact on progression rates.

Main Results:

  • Five years post-onset (Stage 2), 72% of individuals reached Stage 3 (recurrent episodes) and 21% reached Stage 4 (continuous episodes).
  • A biphasic onset (depression-mania or mania-depression) and male sex were associated with increased progression from Stage 2 to Stage 3.
  • A small percentage (8%) showed recovery from Stage 4 back to Stage 3.

Conclusions:

  • The clinical staging model effectively tracks bipolar disorder progression using longitudinal data.
  • Key variables influencing illness transition rates were successfully identified, offering insights into BD's natural course.