International Journal of Bipolar Disorders最新文献

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.

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.

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.

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.