ABSTRACT
OBJECTIVE: The differential diagnosis of bipolar illness vs. borderline personality is controversial. Both conditions manifest impulsive behavior, unstable interpersonal relationships, and mood symptoms. This study examines whether and which mood clinical features can differentiate between both conditions. METHOD: A total of 260 patients (mean ± standard deviation age 41 ± 13 years, 68% female) attending to a mood clinic were examined for diagnosis of bipolar illness and borderline personality disorder using SCID-I, SCID-II, and clinical mood criteria extracted from Mood Disorder Questionnaire (MDQ). They were analyzed using diagnoses as dependent variables. Predictors of bipolar and borderline diagnoses were identified by multivariable logistic regressions, and predictive validity of models was assessed using ROC curve analysis. RESULTS: Bipolar illness was strongly predicted by elevated mood (OR = 4.02, 95% CI: 1.80-9.15), increased goal-directed activities (OR = 3.90, 95% CI: 1.73-8.96), and episodicity of mood symptoms (OR = 3.48, 95% CI 1.49-8.39). This triad model predicted bipolar illness with 88.7% sensitivity, 81.4% specificity, and obtained an auROC of 0.91 (95% CI: 0.76-0.96) and a positive predictive value of 85.1%. For borderline personality disorder, only female gender was a statistically significant predictor (OR = 3.41, 95% CI: 1.29-13.7), and the predictive model obtained an auROC of 0.67 (95% CI: 0.53-0.74). CONCLUSION: In a mood disorder clinic setting, manic criteria and episodic mood course distinguished bipolar illness from borderline personality disorder.
Subject(s)
Bipolar Disorder/diagnosis , Borderline Personality Disorder/diagnosis , Mood Disorders/diagnosis , Adult , Bipolar Disorder/physiopathology , Borderline Personality Disorder/physiopathology , Cross-Sectional Studies , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Models, Statistical , Mood Disorders/physiopathology , Sensitivity and SpecificityABSTRACT
BACKGROUND: Despite availability of validated screening tests for mood disorders, busy general practitioners (GPs) often lack the time to use them routinely. This study aimed to develop a simplified clinical predictive score to help screen for presence of current mood disorder in low-income primary care settings. METHODS: In a cross-sectional study, 197 patients seen at 10 primary care centers in Santiago, Chile completed self-administered screening tools for mood disorders: the Patient Health questionnaire (PHQ-9) and the Mood Disorder Questionnaire (MDQ). To determine participants' current-point mood disorder status, trained clinicians applied a gold-standard diagnostic interview (SCID-I). A simplified clinical predictive model (CM) was developed based on clinical features and selected questions from the screening tools. Using CM, a clinical predictive score (PS) was developed. Full PHQ-9 and GP assessment were compared with PS. RESULTS: Using multivariate logistic regression, clinical and demographic variables predictive of current mood disorder were identified for a simplified 8-point predictive score (PS). PS had better discrimination than GP assessment (auROC-statistic=0.80 [95% CI 0.72, 0.85] vs. 0.58 [95% CI 0.52, 0.62] p-value <0.0001), but not as good as the full PHQ-9 (0.89 [95% CI 0.85, 0.93], p-value=0.03). Compared with GP assessment, PS increased sensitivity by 50% at a fixed specificity of 90%. Administered in a typical primary care clinical population, it correctly predicted almost 80% of cases. LIMITATIONS: Further research must verify external validity of the PS. CONCLUSION: An easily administered clinical predictive score determined, with reasonable accuracy, the current risk of mood disorders in low-income primary care settings.
