A comparison of the 33-item Hypomania Checklist with the 33-item Hypomania Checklist-external assessment for the detection of bipolar disorder in adolescents

Background Adolescents with bipolar disorder (BD) are often misdiagnosed as having major depressive disorder (MDD), which delays appropriate treatment and leads to adverse outcomes. The aim of this study was to compare the performance of the 33-item Hypomania Checklist (HCL-33) with the 33-item Hypomania Checklist- external assessment (HCL-33-EA) in adolescents with BD or MDD. Methods 147 adolescents with BD and 113 adolescents with MDD were consecutively recruited. The HCL-33 and HCL-33-EA were completed by patients and their carers, respectively. The sensitivity, positive predictive value (PPV), specificity, negative predictive value (NPV), and area under the curve (AUC) were calculated and compared between the two instruments, using cut-off values based on the Youden’s index. Results The total scores of the HCL-33 and HCL-33-EA were positively and significantly correlated (rs = 0.309, P < 0.001). Compared to the HCL-33, the HCL-33-EA had higher sensitivity and NPV (HCL-33: sensitivity = 0.58, NPV = 0.53; HCL-33-EA: sensitivity = 0.81, NPV = 0.60), while the HCL-33 had higher specificity and PPV (HCL-33: specificity = 0.61, PPV = 0.66; HCL-33-EA: specificity = 0.37, PPV = 0.63). Conclusion Both the HCL-33 and HCL-33-EA seem to be useful for screening depressed adolescents for BD. The HCL-33-EA would be more appropriate for distinguishing BD from MDD in adolescents due to its high sensitivity in Chinese clinical settings.

How to make online mood-monitoring in bipolar patients a success? A qualitative exploration of requirements

Background The Life-Chart Method (LCM) is an effective self-management treatment option in bipolar disorder (BD). There is insufficient knowledge about the consumers’ needs and desires for an e-monitoring solution. The first step towards a new mood monitoring application is an extended inventory among consumers and professionals. Methods The aim of the current study was: to identify opinions about online mood monitoring of patients with BD and professionals and to identify preferences on design, technical features and options facilitating optimal use and implementation of online mood monitoring. This study used a qualitative design with focus-groups. Participants were recruited among patients and care providers. Three focus-groups were held with eight consumers and five professionals. Results The focus-group meetings reveal a shared consciousness of the importance of using the Life-Chart Method for online mood monitoring. There is a need for personalization, adjustability, a strict privacy concept, an adjustable graphic report, and a link to early intervention strategies in the design. Due to the fact that this is a qualitative study with a relative small number of participants, so it remains unclear whether the results are fully generalizable. We can’t rule out a selection bias. Conclusions This study demonstrates the importance of involving stakeholders in identifying a smartphone-based mood charting applications’ requirements. Personalization, adjustability, privacy, an adjustable graphic report, and a direct link to early intervention strategies are necessary requirements for a successful design. The results of this value specification are included in the follow-up of this project.

Antidepressant efficacy of low-frequency repetitive transcranial magnetic stimulation in antidepressant-nonresponding bipolar depression: a single-blind randomized sham-controlled trial

Background To examine the antidepressant efficacy and response predictors of R-DLPFC-LF rTMS for antidepressant-nonresponding BD. Methods We conducted a single-blind randomized sham-controlled trial for 54 (28 sham, 26 active) patients with antidepressant-nonresponding BD (baseline MADRS ≥ 20). Patients received 15 daily sessions of active or sham neuronavigated rTMS (Figure-of-8 coil, five 1 Hz 60 s 110% RMT trains). Outcome measures included depressive response (≥ 50% MADRS reduction, CGI ≤ 2) and remission (MADRS < 7, CGI = 1) rates, treatment emergent hypo/mania (YMRS), depressive and anxiety symptoms (HAM-A). Results 48 patients (25 sham, 23 active) completed treatment, with 3 drop-outs each in active and sham groups. Active rTMS did not produce superior response or remission rates at endpoint or 6 or 12 weeks (ps > 0.05). There was no significant group * time interaction (ps > 0.05) in a multivariate ANOVA with MADRS, HAMA and YMRS as dependent variables. Exploratory analysis found MADRS improvement to be moderated by baseline anxiety (p = 0.02) and melancholia (p = 0.03) at week 3, and depressive onset at weeks 6 (p = 0.03) and 12 (p = 0.04). In subjects with below-mean anxiety (HAMA < 20.7, n = 24), MADRS improvement from active rTMS was superior to sham at week 3 (ITT, t = 2.49, p = 0.04, Cohen’s d = 1.05). No seizures were observed. Groups did not differ in treatment-emergent hypomania (p = 0.1). Limitations Larger sample size might be needed to power subgroup analyses. Moderation analyses were exploratory. Single-blind design. Unblinding before follow-up assessments due to ethical reasons. Conclusions 1-Hz 110% RMT (5 × 60 s trains) R-DLPFC-LF rTMS was not effective for antidepressant non-responding BD but may be further investigated at increased dosage and/or in BD patients with low anxiety. Trial registration CCRB Clinical Trials Registry, CUHK, CUHK_CCT00440. Registered 04 December 2014, https://www2.ccrb.cuhk.edu.hk/registry/public/279

Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states

Background Voice features have been suggested as objective markers of bipolar disorder (BD). Aims To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD. Methods Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n = 78.733), UR (n = 8004), and HC (n = 20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms. Results Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC = 0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC = 0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC = 0.67 (SD 0.11). Conclusions Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD.

The German research consortium for the study of bipolar disorder (BipoLife): a magnetic resonance imaging study protocol

Background Bipolar disorder is one of the most severe mental disorders. Its chronic course is associated with high rates of morbidity and mortality, a high risk of suicide and poor social and occupational outcomes. Despite the great advances over the last decades in understanding mental disorders, the mechanisms underlying bipolar disorder at the neural network level still remain elusive. This has severe consequences for clinical practice, for instance by inadequate diagnoses or delayed treatments. The German research consortium BipoLife aims to shed light on the mechanisms underlying bipolar disorders. It was established in 2015 and incorporates ten university hospitals across Germany. Its research projects focus in particular on individuals at high risk of bipolar disorder, young patients in the early stages of the disease and patients with an unstable highly relapsing course and/or with acute suicidal ideation. Methods Functional and structural magnetic resonance imaging (MRI) data was acquired across nine sites within three different studies. Obtaining neuroimaging data in a multicenter setting requires among others the harmonization of the acquisition protocol, the standardization of paradigms and the implementation of regular quality control procedures. The present article outlines the MRI imaging protocols, the acquisition parameters, the imaging paradigms, the neuroimaging quality assessment procedures and the number of recruited subjects. Discussion The careful implementation of a MRI study protocol as well as the adherence to well-defined quality assessment procedures is one key benchmark in the evaluation of the overall quality of large-scale multicenter imaging studies. This article contributes to the BipoLife project by outlining the rationale and the design of the MRI study protocol. It helps to set the necessary standards for follow-up analyses and provides the technical details for an in-depth understanding of follow-up publications.

Mania and bipolar depression: complementing not opposing poles—a post-hoc analysis of mixed features in manic and hypomanic episodes

Background Depending on the classification system used, 5–40% of manic subjects present with concomitant depressive symptoms. This post-hoc analysis evaluates the hypothesis that (hypo)manic subjects have a higher burden of depression than non-(hypo)manic subjects. Methods Data from 806 Bipolar I or II participants of the Stanley Foundation Bipolar Network (SFBN) were analyzed, comprising 17,937 visits. A split data approach was used to separate evaluation and verification in independent samples. For verification of our hypotheses, we compared mean IDS-C scores ratings of non-manic, hypomanic and manic patients. Data were stored on an SQL-server and extracted using standard SQL functions. Linear correlation coefficients and pivotal tables were used to characterize patient groups. Results Mean age of participants was 40 ± 12 years (range 18–81). 460 patients (57.1%) were female and 624 were diagnosed as having bipolar I disorder (77.4%) and 182 with bipolar II (22.6%). Data of 17,937 visits were available for analyses, split into odd and even patient numbers and stratified into three groups by YMRS-scores: not manic < 12, hypomanic < 21, manic < 30. Average IDS-C sum scores in manic or hypomanic states were significantly higher (p < .001) than for non-manic states. (Hypo)manic female patients were likely to show more depressive symptoms than males (p < .001). Similar results were obtained when only the core items of the YMRS or only the number of depressive symptoms were considered. Analyzing the frequency of (hypo)manic mixed states applying a proxy of the DSM-5 mixed features specifier extracted from the IDS-C, we found that almost 50% of the (hypo)manic group visits fulfilled DSM-5 mixed features specifier criteria. Conclusion Subjects with a higher manic symptom load are also significantly more likely to experience a higher number of depressive symptoms. Mania and depression are not opposing poles of bipolarity but complement each other.

The course of bipolar disorder in pregnant versus non-pregnant women

Background and rationale Although it has been suggested that pregnancy may influence the course of bipolar disorder (BD), studies show contradictory results. Until now, no studies included a finegrained validated method to report mood symptoms on a daily basis, such as the lifechart method (LCM). The aim of the present study is to investigate the course of BD during pregnancy by comparing LCM scores of pregnant and non-pregnant women. Methods Study design: Comparison of LCM scores of two prospective observational BD cohort studies, a cohort of pregnant women (n = 34) and a cohort of non-pregnant women of childbearing age (n = 52). Main study parameters are: (1) proportions of symptomatic and non-symptomatic days; (2) symptom severity, frequency, and duration of episodes; (3) state sequences, longitudinal variation of symptom severity scores. Results No differences in clinical course variables (symptomatic days, average severity scores, frequency, and duration of episodes in BD were found between pregnant and non-pregnant women. With a combination of State Sequence Analysis (SSA) and cluster analysis on the sequences of daily mood scores three comparable clusters were found in both samples: euthymic, moderately ill and severely ill. The distribution differences between pregnant and non-pregnant women were significant, with a majority of the pregnant women (68%) belonging to the moderately ill cluster and a majority of the non-pregnant women (46%) to the euthymic cluster. In pregnant women the average daily variation in mood symptoms as assessed with Shannon’s entropy was less than in non-pregnant women (respectively 0.43 versus 0.56). Conclusions Although the use of daily mood scores revealed no difference in overall course of BD in pregnant versus non-pregnant women, more pregnant than non-pregnant women belonged to the moderately ill cluster, and during pregnancy the variation in mood state was less than in non-pregnant women. Further research is necessary to clarify these findings.

Characterization of affective lability across subgroups of psychosis spectrum disorders

Background Affective lability is elevated and associated with increased clinical burden in psychosis spectrum disorders. The extent to which the level, structure and dispersion of affective lability varies between the specific disorders included in the psychosis spectrum is however unclear. To have potential value as a treatment target, further characterization of affective lability in these populations is necessary. The main aim of our study was to investigate differences in the architecture of affective lability in different psychosis spectrum disorders, and if putative differences remained when we controlled for current symptom status. Methods Affective lability was measured with The Affective Lability Scale Short Form (ALS-SF) in participants with schizophrenia (SZ, n = 76), bipolar I disorder (BD-I, n = 105), bipolar II disorder (BD-II, n = 68) and a mixed psychosis-affective group (MP, n = 48). Multiple analyses of covariance were conducted to compare the ALS-SF total and subdimension scores of the diagnostic groups, correcting for current psychotic, affective and anxiety symptoms, substance use and sex. Double generalized linear models were performed to compare the dispersion of affective lability in the different groups. Results Overall group differences in affective lability remained significant after adjusting for covariates (p = .001). BD-II had higher affective lability compared to SZ and BD-I (p = .004), with no significant differences between SZ and BD-I. There were no significant differences in the contributions of ALS-SF dimensions to the total affective lability or in dispersion of affective lability between the groups. Conclusions This study provides the construct of affective lability in psychosis spectrum disorders with more granular details that may have implications for research and clinical care. It demonstrates that despite overlap in core symptom profiles, BD-I is more similar to SZ than it is to BD-II concerning affective lability and the BD groups should consequently be studied apart. Further, affective lability appears to be characterized by fluctuations between depressive- and other affective states across different psychosis spectrum disorders, indicating that affective lability may be related to internalizing problems in these disorders. Finally, although the level varies between groups, affective lability is evenly spread and not driven by extremes across psychosis spectrum disorders and should be assessed irrespective of diagnosis.

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.

Long-term outcome of postpartum psychosis: a prospective clinical cohort study in 106 women

Objective We aimed to investigate the outcome of postpartum psychosis over a four-year follow-up, and to identify potential clinical markers of mood/psychotic episodes outside of the postpartum period. Methods One hundred and six women with a diagnosis of first-onset mania or psychosis during the postpartum period were included in this prospective longitudinal study. Women were categorized into either (1) recurrence of non-postpartum mood/psychotic episodes or (2) mania/psychosis limited to the postpartum period. We summarize the longitudinal course of the illness per group. We used a logistic regression model to identify clinical predictors of recurrence of mood/psychotic episodes outside of the postpartum period. Results Over two thirds of the women included in this study did not have major psychiatric episodes outside of the postpartum period during follow-up. The overall recurrence rate of mood/psychotic episodes outside the postpartum period was ~ 32%. Of these women, most transitioned to a bipolar disorder diagnosis. None of the women fulfilled diagnostic criteria for schizophrenia or schizophreniform disorder. No clinical markers significantly predicted recurrence outside of the postpartum period. Conclusions For the majority of women with first-onset postpartum psychosis, the risk of illness was limited to the period after childbirth. For the remaining women, postpartum psychosis was part of a mood/psychotic disorder with severe non-postpartum recurrence, mainly in the bipolar spectrum. No clinical predictors for risk of severe episodes outside the postpartum period emerged. Our findings add to previous evidence suggesting a fundamental link between postpartum psychosis and bipolar disorder, which may represent two distinct diagnoses within the same spectrum.