The Bipolarity Index: a clinician-rated measure of diagnostic confidence

[Psychopharmacotherapy of mixed affective states in bipolar disorder]

On the example of a patient with a mixed affective episode within the framework of bipolar affective disorder, the clinical features of this psychopathological condition, the difficulties of diagnosis and selection of therapy in mixed states are presented. The use of the modern atypical antipsychotic ziprasidone in this category of patients is argued.

Rethinking ketamine and esketamine action: Are they antidepressants with mood-stabilizing properties?

Ketamine and esketamine, the S-enantiomer of the racemic mixture, have recently generated considerable interest as potential therapeutic agents for Treatment-Resistant Depression (TRD), a complex disorder that includes various psychopathological dimensions and distinct clinical profiles (e.g., comorbid personality disorder, bipolar spectrum, dysthymic disorder). This perspective article provides a comprehensive overview of the action of ketamine/esketamine from a dimensional point of view, taking into account the high prevalence of bipolarity in TRD and the evidence of the efficacy of these substances on mixed features, anxiety, dysphoric mood, and, generally, bipolar traits. Additionally, the article underscores the complexity of the pharmacodynamic mechanisms of action of ketamine/esketamine, which goes beyond the non-competitive antagonism of NMDA-R. The need for further research and evidence is highlighted, mainly to evaluate the efficacy of esketamine nasal spray in bipolar depression, the presence of bipolar elements as a predictor of response, and the potential role of these substances as mood stabilizers. The article implies that, in the future, ketamine/esketamine could be used with fewer limitations, not only as antidepressants for the most severe form of depression but also as valuable tools to stabilize subjects with mixed symptoms or bipolar spectrum.

Assessment of the diagnostic performance of various screening tools for bipolar disorder: a meta-analysis

Background

Various studies have shown that about 40–50% of patients with bipolar disorder (BD) are undiagnosed at the time of referral and are often treated as depression. The present meta-analysis was conducted to determine the diagnostic accuracy of psychometric properties of the bipolarity index (BI) in people with BD.

Methods

We systematically searched databases including, Scopus, ISI Web of Sciences (WOS), Pubmed/Medline, Embase, and PsycINFO using standard search terms.

Results

Two hundred and ninety-six records were found through the initial search. Of 679 articles, 25 duplicated studies were found and 70 were omitted due to the irrelevant titles and abstracts. The rest 450 were entered the full-text screening, of which 186 were excluded due to pre-defined inclusion criteria. Ultimately, 15 studies on 6525 patients were included. Our findings showed that the pooled sensitivity of BI in the diagnosis of BD was 0.82 (95%CI: 0.81–0.83, P < 0.0001, I2 = 99%), while the pooled specificity also was 0.73 (95%CI: 0.72–0.74, P = 0.000, I2 = 99%). The BI was significantly more accurate than the other tests with a pooled DOR of 47.2 (95%CI: 12.01–85.52, P = 0.0000, I2 = 99.2%).

Conclusion

BI appears to be a useful screening instrument with suitable psychometric properties to identify BD compared to both the MDQ and the HCL-32. Consequently, patients detected by the BI should be confirmed through diagnostic interviews. Thus, more studies are needed to explore the optimal cut-off values of BI among screened populations during long-term follow-up, since a considerable portion of individuals primarily diagnosed with major depressive disorders could have BD.

A piece of the puzzle: Does bipolarity partly explain the high prevalence of treatment resistance in depression?

In this cross-sectional study we examined whether the prevalence of treatment resistant depression (TRD) can be partly attributed to level of bipolarity. We included data of 201 patients with either episodic depression or TRD, who received treatment for their depression at either an outpatient or 2nd opinion/daytime setting, within a specialised mental healthcare department in the Netherlands. Whether level of TRD, assessed by the 'Dutch Measure for quantification of Treatment Resistance in Depression', can be partly explained by level of bipolarity, assessed by 'the Bipolarity Index', was examined using linear regression. We found no direct association between level of TRD and level of bipolarity, nor did comorbid anxiety disorders obscure an existing association. In this study we found no evidence for overlooked bipolarity contributing to the high prevalence of TRD. If replicated, we could state that additional screening on bipolarity with an instrument such as the 'Bipolarity Index' in the specialised mental health care is unnecessary.

Affective lability as a prospective predictor of subsequent bipolar disorder diagnosis: a systematic review

Objectives

The early pathogenesis and precursors of Bipolar Disorder (BD) are poorly understood. There is some cross-sectional and retrospective evidence of affective lability as a predictor of BD, but this is subject to recall biases. The present review synthesises the prospective evidence examining affective lability and the subsequent development of BD at follow-up.

Methods

The authors performed a systematic search of PubMed, PsycInfo and Embase (1960–June 2020) and conducted hand searches to identify studies assessing affective lability (according to a conceptually-inclusive definition) at baseline assessment in individuals without a BD diagnosis, and a longitudinal follow-up assessment of bipolar (spectrum) disorders. Results are reported according to the PRISMA guidelines, and the synthesis without meta-analysis (SWiM) reporting guidelines were used to strengthen the narrative synthesis. The Newcastle–Ottawa Scale was used to assess risk of bias (ROB).

Results

11 articles describing 10 studies were included. Being identified as having affective lability at baseline was associated with an increased rate of bipolar diagnoses at follow-up; this association was statistically significant in six of eight studies assessing BD type I/II at follow-up and in all four studies assessing for bipolar spectrum disorder (BSD) criteria. Most studies received a ‘fair’ or ‘poor’ ROB grade.

Conclusions

Despite a paucity of studies, an overall association between prospectively-identified affective lability and a later diagnosis of BD or BSD is apparent with relative consistency between studies. This association and further longitudinal studies could inform future clinical screening of those who may be at risk of BD, with the potential to improve diagnostic accuracy and facilitate early intervention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40345-021-00237-1.

Blood cytokines differentiate bipolar disorder and major depressive disorder during a major depressive episode: Initial discovery and independent sample replication

Bipolar disorder (BD) diagnosis currently relies on assessment of clinical symptoms, mainly retrospective and subject to memory bias. BD is often misdiagnosed as Major Depressive Disorder (MDD) resulting in ineffective treatment and worsened clinical outcome. The primary purpose of this study was to identify blood biomarkers that discriminate MDD from BD patients when in a depressed state. We have used clinical data and serum samples from two independent naturalistic cohorts of patients with a Major Depressive Episode (MDE) who fulfilled the criteria of either BD or MDD at inclusion. The discovery and replication cohorts consisted of 462 and 133 patients respectively. Patients were clinically assessed using standard diagnostic interviews, and clinical variables including current treatments were recorded. Blood was collected and serum assessed for levels of 31 cytokines using a sensitive multiplex assay. A penalized logistic regression model combined with nonparametric bootstrap was subsequently used to identify cytokines associated with BD. Interleukin (IL)-6, IL-10, IL-15, IL-27 and C-X-C ligand chemokine (CXCL)-10 were positively associated with BD in the discovery cohort. Of the five cytokines identified as discriminant features in the discovery cohort, IL-10, IL-15 and IL-27 were also positively associated with BD in the replication cohort therefore providing an external validation to our finding. Should our results be validated in a prospective cohort, they could provide new insights into the pathophysiological mechanisms of mood disorders.

Validity of the Aktibipo Self-rating Questionnaire for the Digital Self-assessment of Mood and Relapse Detection in Patients With Bipolar Disorder: Instrument Validation Study

BACKGROUND

Self-reported mood is a valuable clinical data source regarding disease state and course in patients with mood disorders. However, validated, quick, and scalable digital self-report measures that can also detect relapse are still not available for clinical care.

OBJECTIVE

In this study, we aim to validate the newly developed ASERT (Aktibipo Self-rating) questionnaire-a 10-item, mobile app-based, self-report mood questionnaire consisting of 4 depression, 4 mania, and 2 nonspecific symptom items, each with 5 possible answers. The validation data set is a subset of the ongoing observational longitudinal AKTIBIPO400 study for the long-term monitoring of mood and activity (via actigraphy) in patients with bipolar disorder (BD). Patients with confirmed BD are included and monitored with weekly ASERT questionnaires and monthly clinical scales (Montgomery-Åsberg Depression Rating Scale [MADRS] and Young Mania Rating Scale [YMRS]).

METHODS

The content validity of the ASERT questionnaire was assessed using principal component analysis, and the Cronbach α was used to assess the internal consistency of each factor. The convergent validity of the depressive or manic items of the ASERT questionnaire with the MADRS and YMRS, respectively, was assessed using a linear mixed-effects model and linear correlation analyses. In addition, we investigated the capability of the ASERT questionnaire to distinguish relapse (YMRS≥15 and MADRS≥15) from a nonrelapse (interepisode) state (YMRS<15 and MADRS<15) using a logistic mixed-effects model.

RESULTS

A total of 99 patients with BD were included in this study (follow-up: mean 754 days, SD 266) and completed an average of 78.1% (SD 18.3%) of the requested ASERT assessments (completion time for the 10 ASERT questions: median 24.0 seconds) across all patients in this study. The ASERT depression items were highly associated with MADRS total scores (P<.001; bootstrap). Similarly, ASERT mania items were highly associated with YMRS total scores (P<.001; bootstrap). Furthermore, the logistic mixed-effects regression model for scale-based relapse detection showed high detection accuracy in a repeated holdout validation for both depression (accuracy=85%; sensitivity=69.9%; specificity=88.4%; area under the receiver operating characteristic curve=0.880) and mania (accuracy=87.5%; sensitivity=64.9%; specificity=89.9%; area under the receiver operating characteristic curve=0.844).

CONCLUSIONS

The ASERT questionnaire is a quick and acceptable mood monitoring tool that is administered via a smartphone app. The questionnaire has a good capability to detect the worsening of clinical symptoms in a long-term monitoring scenario.

Examining the relationship between gray matter volume and a continuous measure of bipolarity in unmedicated unipolar and bipolar depression

BACKGROUND

It has been argued that unipolar major depressive disorder (MDD) and bipolar disorder (BD) exist on a continuous spectrum, given their overlapping symptomatology and genetic diatheses. The Bipolarity Index (BI) is a scale that considers bipolarity as a continuous construct and was developed to assess confidence in bipolar diagnosis. Here we investigated whether BI scores correlate with gray matter volume (GMV) in a sample of unmedicated unipolar and bipolar depressed individuals.

METHODS

158 subjects (139 with MDD, 19 with BD) in a major depressive episode at time of scan were assigned BI scores. T1-weighted Magnetic Resonance Imaging scans were obtained and processed with Voxel-Based Morphometry using SPM12 (CAT12 toolbox) to assess GMV. Regression was performed at the voxel level to identify clusters of voxels whose GMV was associated with BI score, (p<0.001, family-wise error-corrected cluster-level p<0.05), with age, sex and total intracranial volume as covariates.

RESULTS

GMV was inversely correlated with BI score in four clusters located in left lateral occipital cortex, bilateral angular gyri and right frontal pole. Clusters were no longer significant after controlling for diagnosis. GMV was not correlated with BI score within the MDD cohort alone.

LIMITATIONS

Incomplete clinical data required use of a modified BI scale.

CONCLUSION

BI scores were inversely correlated with GMV in unmedicated subjects with MDD and BD, but these correlations appeared driven by categorical diagnosis. Future work will examine other imaging modalities and focus on elements of the BI scale most likely to be related to brain structure and function.

White matter integrity in young medication-naïve bipolar II depressed adults

It is unknown if young medication-naïve bipolar II (BPII) depressed patients have increased white matter (WM) disruptions. 27 each of young (average 23 years) and treatment-naïve BPII depressed, unipolar depressed (UD) patients and age–sex–education matched healthy controls (HC) underwent 3 T MRIs with diffusion tensor imaging. Diagnostic ratings included Structured Clinical Interview for DSM Disorders (SCID), Montgomery-Åsberg Depression Rating Scale (MADRS), Young Mania Rating Scale (YMRS) and Hamilton Anxiety Rating Scale (HAM-A). Patients were clinically depressed (MADRS-BPII: 26.15 [SD9.25], UD: 25.56 [5.24], p = 0.86). Compared to UD, BPII had increased family bipolarity (BPII 13.6% vs UD 2.5%, p = 0.01, φc = 0.28), hypomanic symptoms (YMRS-BPII: 4.22 [4.24], UD: 1.33 [2], p = 0.02, d = 0.87), lifetime number of depressive episodes (BPII: 2.37 [1.23], UD: 1.44 [0.75], p = 0.02, d = 0.91), lifetime and current-year number of episodes (lifetime BPII: 50.85 [95.47], UD: 1.7 [1.03]; current-year BPII: 9.93 [16.29], UD: 1.11 [0.32], ps = 0.04, ds = 0.73–0.77) and longer illness duration (BPII: 4.96 years [3.96], UD: 2.99 [3.33], p = 0.15, d = 0.54). BPII showed no increased WM disruptions vs UD or HC in any of the 15 a priori WM tracts. UD had lower right superior longitudinal fasciculus (SLF) (temporal) axial diffusivity (AD) (1.14 vs 1.17 (BPII), 1.16 (HC); F = 6.93, 95% CI of\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${F}_{B}$$\end{document}FB: 0.00073, 5.22, ηp² = 0.15). Principal component analysis followed by exploratory linear discriminant analysis showed that increased R-SLF (temporal) AD, YMRS and family bipolarity distinguished BPII from UD (81.5% sensitivity, 85.2% specificity) independent of episode number and frequency. Young, medication-naïve adults with BPII depression did not show the WM disruptions distinguishing more chronically ill BP patients from UD. These WM disruptions may therefore be partly attributable to illness chronicity. Longitudinal studies should examine the trajectory of WM changes in BPII and UD and predictive validity of these baseline clinical and imaging parameters.

Bipolarity in a Collaborative Care Model Variation: Detection, Prevalence, and Outcomes

OBJECTIVE

To assess the prevalence of bipolarity and its impact on clinical course, psychiatric consultants' diagnostic impressions and respective treatment outcomes were examined for patients with depression who were treated in a collaborative care model (CoCM) of psychiatric consultation.

METHODS

Electronic records for 1,476 patients were reviewed for the presence of a mood disorder, which yielded 641 patients with complete data on several measures: the Composite International Diagnostic Interview, version 3.0 (CIDI); a questionnaire eliciting data on non-mania-related markers of bipolar disorder (family history, age of onset, course of illness, response to treatment); consultants' diagnostic impressions; and Patient Health Questionnaire-9 (PHQ-9) scores before and after consultation.

RESULTS

Of referred patients, 97% were screened for bipolar disorder. A smooth distribution of scores on the CIDI was observed. Patients were divided into four groups on the basis of their CIDI scores (≥7, positive, or <7, negative) and on the consultant's recorded Impression (positive or negative for bipolarity). Of the study sample, 21% were CIDI positive (≥7), and 35% were Impression positive (sufficient bipolarity to guide treatment recommendations). All groups demonstrated equivalent decreases in PHQ-9 scores in the 6 months since consultation, including the potentially overdiagnosed group (CIDI negative, impression positive), which comprised 22% of the study sample.

CONCLUSIONS

Universal screening for bipolarity in primary care is feasible in CoCM programs. Interpreting the data dimensionally is logical on the basis of the smooth distribution of CIDI scores. Such screening will yield high rates of bipolar disorder, much higher than previously reported. Offering treatment recommendations based on an impression of bipolarity to patients with negative CIDI results (<7) was not associated with outcomes worse than experienced by all other consultation patients. Multiple explanations of the latter finding are possible, warranting additional study.

Depression Preceding Diagnosis of Bipolar Disorder

This paper focuses on depression that precedes an onset of manifest bipolar disorder as early stage bipolar disorder. First, we review how to pragmatically identify the clinical characteristics of patients presenting with an episode of depression who subsequently go on to develop episodes of mania or hypomania. The existing literature shows a strong consensus: accurate identification of depression with early onset and recurrent course with multiple episodes, subthreshold hypomanic and/or mixed symptoms, and family history of bipolar disorder or completed suicide have been shown by multiple authors as signs pointing to bipolar diagnosis. This contrasts with relatively limited information available to guide management of such "pre-bipolar" (pre-declared bipolar) patients, especially those in the adult age range. Default assumption of unipolar depression at this stage carries significant risk. Antidepressants are still the most common pharmacological treatment used, but clinicians need to be aware of their potential harm. In some patients with unrecognized bipolar depression, antidepressants can not only produce switch to (hypo)mania, but also mixed symptoms, or worsening of depression with an increased risk of suicide. We review pragmatic management strategies in the literature beyond clinical guidelines that can be considered for this at-risk group encompassing the more recent child and adolescent literature. In the future, genetic research could make the early identification of bipolar depression easier by generating informative markers and polygenic risk scores.

[Continuous cycling bipolar affective disorders in youth]

OBJECTIVE

To reveal psychopathological characteristics and core dynamic patterns of continuous cycling bipolar affective disorders in youth.

MATERIAL AND METHODS

One hundred and seven patients (62 men and 45 women), including 59 patients of the clinical group and 48 of the follow-up group, were examined.

RESULTS AND CONCLUSION

Three types of continuous cycling bipolar affective disorders in youth are described: rhythmic, dysrhythmic and pseudorhythmic. A correlation between the dynamics of affective disorders in youth and the nosological affiliation of each of the identified types of continuous cycling course was found. The results may be useful for solving differential/diagnostic, prognostic and therapeutic tasks.

The predictive performance of the bipolarity index in a Dutch epidemiological sample manuscript

BACKGROUND

No instrumnt exists that can predict the incidence of bipolar disorders (BD). The Bipolarity index (BI), originally developed to improve diagnostic confidence for a lifetime diagnosis of BD, may predict incident BD.

AIM

To assess the predictive performance of the BI for incident BD in persons with a lifetime depression.

METHODS

The BI score was composed from different questionnaires and interviews in n = 1857 subjects without BD and with a lifetime unipolar depressive disorder from the Netherlands Study of Depression and Anxiety, a longitudinal cohortstudy. The incidence of DSM-IV defined BD I or II as a criterion diagnosis was established with the Composite International Diagnostic Interview after 2, 4, 6 and 9 years of follow-up. Cox regression analyses calculated whether the BI predicts incident BD during 9-years of follow-up. The area Under the Curve (AUC) was determined. At the optimal cut-off score, sensitivity, specificity, positive, and negative predictive values (PPV and NPV) were calculated.

RESULTS

Over the course of 9 years, bipolar conversion occurred in n = 46 subjects (2.5%). Each point increase in BI score significantly predicted incident BD (HR[95%CI]= 1.047[1.018-1.076], p = 0.001). The AUC was 0.61 (95%CI: 0.54-0.68). At the optimal cut-off of 30, sensitivity was 67%, specificity 52%, PPV 3% and NPV 98%.

LIMITATIONS

Not all items of the BI were fully covered; mean age of the sample of 42.

CONCLUSION

The BI score predicts bipolar conversion over 9 years in those with a lifetime depression. However, given the modest performance metrics, the BI cannot guide clinical decision making yet.

Early identification of bipolar from unipolar depression before manic episode: Evidence from dynamic rfMRI

OBJECTIVE

Misdiagnosis of bipolar disorder (BD) as unipolar disorder (UD) may cause improper treatment strategy to be chosen, especially in the early stages of disease. The aim of this study was to characterize alterations in specific brain networks for depressed patients who transformed into BD (tBD) from UD.

METHOD

The module allegiance from resting-fMRI by applying a multilayer modular method was estimated in 99 patients (33 tBD, 33 BD, 33 UD) and 33 healthy controls (HC). A classification model was trained on tBD and UD patients. HC was used to explore the functional declination patterns of BD, tBD, and UD.

RESULTS

Based on our classification model, difference mainly reflected in default-mode network (DMN). Compared with HC, both BD and tBD focused on the difference of somatomotor network (SMN), while UD on the abnormity of DMN. The patterns of brain network between patients with BD and tBD were well-overlapped, except for cognitive control network (CCN).

CONCLUSION

The functional declination of internal interaction in DMN was suggested to be useful for the identification of BD from UD in the early stage. The higher recruitment of DMN may predispose patients to depressive states, while higher recruitment of SMN makes them more sensitive to external stimuli and prone to mania. Furthermore, CCN may be a critical network for identifying different stages of BD, suggesting that the onset of mania in depressed patients is accompanied by CCN related cognitive impairments.

Implementing machine learning in bipolar diagnosis in China

Bipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of this study was to develop a new Bipolar Diagnosis Checklist in Chinese (BDCC) by using machine learning to shorten the Affective Disorder Evaluation scale (ADE) based on an analysis of registered Chinese multisite cohort data. In order to evaluate the importance of each item of the ADE, a case-control study of 360 bipolar disorder (BPD) patients, 255 major depressive disorder (MDD) patients and 228 healthy (no psychiatric diagnosis) controls (HCs) was conducted, spanning 9 Chinese health facilities participating in the Comprehensive Assessment and Follow-up Descriptive Study on Bipolar Disorder (CAFÉ-BD). The BDCC was formed by selected items from the ADE according to their importance as calculated by a random forest machine learning algorithm. Five classical machine learning algorithms, namely, a random forest algorithm, support vector regression (SVR), the least absolute shrinkage and selection operator (LASSO), linear discriminant analysis (LDA) and logistic regression, were used to retrospectively analyze the aforementioned cohort data to shorten the ADE. Regarding the area under the receiver operating characteristic (ROC) curve (AUC), the BDCC had high AUCs of 0.948, 0.921, and 0.923 for the diagnosis of MDD, BPD, and HC, respectively, despite containing only 15% (17/113) of the items from the ADE. Traditional scales can be shortened using machine learning analysis. By shortening the ADE using a random forest algorithm, we generated the BDCC, which can be more easily applied in clinical practice to effectively enhance both BPD and MDD diagnosis.

Clinical and genetic validity of quantitative bipolarity

Research has yet to provide a comprehensive understanding of the genetic basis of bipolar disorder (BP). In genetic studies, defining the phenotype by diagnosis may miss risk-allele carriers without BP. The authors aimed to test whether quantitatively detected subclinical symptoms of bipolarity identifies a heritable trait that infers risk for BP. The Quantitative Bipolarity Scale (QBS) was administered to 310 Old Order Amish or Mennonite individuals from multigenerational pedigrees; 110 individuals had psychiatric diagnoses (20 BP, 61 major depressive disorders (MDD), 3 psychotic disorders, 26 other psychiatric disorders). Familial aggregation of QBS was calculated using the variance components method to derive heritability and shared household effects. The QBS score was significantly higher in BP subjects (31.5 ± 3.6) compared to MDD (16.7 ± 2.0), other psychiatric diagnoses (7.0 ± 1.9), and no psychiatric diagnosis (6.0 ± 0.65) (all p < 0.001). QBS in the whole sample was significantly heritable (h² = 0.46 ± 0.15, p < 0.001) while the variance attributed to the shared household effect was not significant (p = 0.073). When subjects with psychiatric illness were removed, the QBS heritability was similar (h² = 0.59 ± 0.18, p < 0.001). These findings suggest that quantitative bipolarity as measured by QBS can separate BP from other psychiatric illnesses yet is significantly heritable with and without BP included in the pedigrees suggesting that the quantitative bipolarity describes a continuous heritable trait that is not driven by a discrete psychiatric diagnosis. Bipolarity trait assessment may be used to supplement the diagnosis of BP in future genetic studies and could be especially useful for capturing subclinical genetic contributions to a BP phenotype.

Challenges in diagnosis and treatment of comorbid eating disorders and mood disorders

PURPOSE

This paper explores complex challenges in diagnosis and treatment of patients with comorbid eating disorders and mood disorders. An integrative review of the literature examines overlapping symptoms, phenotypes, neural circuits, and genetic factors for these disorders. A case study illustrates how the integration of knowledge from research and practice can inform a diagnostic formulation and multifaceted treatment plan.

CONCLUSIONS

Patients with complex clinical presentations inspire a search for research and clinical literature that helps build the neuroscience foundations of advanced practice.

PRACTICE IMPLICATIONS

Sharing clinical challenges and processes of discovery is a form of scholarship that promotes the evolution of advanced psychiatric nursing practice.

The Diagnosis and Management of Bipolar I and II Disorders: Clinical Practice Update

Bipolar disorders, including bipolar I disorder (BP-I) and bipolar II disorder (BP-II), are common, potentially disabling, and, in some cases, life-threatening conditions. Bipolar disorders are characterized by alternating episodes of mania or hypomania and depression, or mixtures of manic and depressive features. Bipolar disorders present many diagnostic and therapeutic challenges for busy clinicians. Adequate management of bipolar disorders requires pharmacotherapy and psychosocial interventions targeted to the specific phases of illness. Effective treatments are available for each illness phase, but mood episode relapses and incomplete responses to treatment are common, especially for the depressive phase. Mood symptoms, psychosocial functioning, and suicide risk must, therefore, be continually reevaluated, and, when necessary, the plan of care must be adjusted during long-term treatment. Many patients will require additional treatment of comorbid psychiatric and substance use disorders and management of a variety of commonly co-occurring chronic general medical conditions.

Bipolar diagnosis in China: Evaluating diagnostic confidence using the Bipolarity Index

BACKGROUND

Diagnosis of bipolar disorder is inherently difficult. The goal of this study was to examine the utility and psychometric properties of the Bipolarity Index (BPx) in a population of patients treated in China.

METHODS

At nine Chinese health facilities participating in CAFÉ-BD, clinicians completed a standardized affective disorder evaluation for consecutive patients (N=615) with a clinical diagnosis of MDD and BPD and scored the Bipolarity Index. The investigators constructed ROC curves to determine the optimal cut off points to discriminate subjects in three clinical diagnostic groups: bipolar disorder (BPD), major depressive disorder (MDD) and healthy (no psychiatric diagnosis) controls (HC). This study is registered with ClinicalTrials.gov, number NCT02015143.

RESULTS

1) The cut-off score between the MDD and BPD groups was 42.0, with a sensitivity of 0.957 and specificity of 0.881 (Z=63.064, P<0.001); the cut-off score between the MDD and BPD II groups was 34.0, with a sensitivity of 0.810 and specificity of 0.855 (Z=20.174, P<0.001); and the cut-off score between the BPD II and BPD I groups was 57.0, with a sensitivity of 0.680 and specificity of 0.772 (Z=9.636, P<0.001). 2) Five domains contributed to the discrimination results. State-related domains (episode characteristics and course of illness) made greater contributions than trait-related domains (age of onset, family history, and treatment response).

LIMITATIONS

The data are purely descriptive. The BPD II sample and the family history dataset were small.

CONCLUSIONS

Our finding indicates good reliability and validity for the Chinese version of the BPx, which encourages its use as a measure of diagnostic confidence for bipolar spectrum disorders. Further prospective study is necessary to determine if the BPx is useful in identifying subgroups among MDD subjects at high risk for conversion to BPD.