Taiwan consensus on biological treatment of bipolar disorder during the acute, maintenance, and mixed phases: The 2022 update

Poor subjective sleep quality and trait impulsivity in patients with bipolar disorder

BACKGROUND

Sleep disturbance and impulsivity are key components of mood vulnerability in bipolar disorder (BD), but few studies have assessed the association between these two symptoms among patients with BD.

METHODS

Forty-seven euthymic patients with bipolar I disorder (BDI) or bipolar II disorder (BDII) and 58 age- and sex-matched healthy controls were enrolled in this cross-sectional study. Trait impulsivity was measured using the Barratt Impulsiveness Scale Version 11 (BIS-11), which yielded 3 second-order factors: attention, motor, and non-planning. Subjective sleep quality was assessed using the self-reported Pittsburgh Sleep Quality Index (PSQI). General linear models (GLMs) were used to assess the associations between subjective poor sleep and trait impulsivity with multiple testing corrections.

RESULTS

Patients with BD scored higher in BIS-11 and PSQI than healthy controls. PSQI total scores positively correlated with BIS-11 total scores, while sleep disturbance and daytime dysfunction were associated with attentional impulsiveness after controlling for covariates. Participants with higher PSQI total scores (>10) had higher scores in BIS-11 total, attention, and non-planning than those with low PSQI scores (≤5).

CONCLUSION

These findings support the hypothesis that poor sleep quality might lead to impulsivity and add to the growing evidence that improving sleep quality may be a therapeutic target for patients with BD.

Role of adjunctive cariprazine for treatment-resistant depression in patients with major depressive disorder: A systematic review and meta-analysis of randomized controlled trials

INTRODUCTION

Cariprazine is an orally active dopamine D3-preferring D3/D2 receptor and serotonin 5-HT1A receptor partial agonist, being considered as a treatment for refractory MDD. Therefore, we aim to perform the first meta-analysis of current literature, to collate changes in depression from baseline and assess tolerability of adjunctive cariprazine in MDD populace.

METHODS

PubMed, Embase, Google Scholar, ClinicalTrials.Gov, and Cochrane Library were searched from inception till 1st September 2023. RCTs of adult patients with refractory MDD under adjunctive cariprazine vs. placebo were included. Primary outcomes included improvement in MADRS, CGI-S, and HAM-D 17 scores. Secondary outcomes included treatment-emergent adverse events. The statistical analysis was performed using generic inverse variance with random-effects model. The overall risk ratios (RR) were calculated for dichotomous outcomes.

RESULTS

A total of five RCTs were analysed, enrolling 2013 participants (cariprazine: 959 participants, Placebo: 1054). Supplementation of ADT with cariprazine demonstrated a significant improvement in MADRAS, CGI-S and HAMD-17 scores from baseline (LSMD: -1.88, 95% CI [-2.94, -0.83], p=0.0005), (LSMD: -0.18, 95% CI [-0.29, -0.07], p=0.002), and (LSMD: -0.96, 95% CI [-1.70, -0.21], p=0.01) respectively. Treatment with adjunctive cariprazine therapy demonstrated significantly increased incidence of akathisia, nausea, dizziness, fatigue, restlessness, somnolence, and tremors when compared with placebo.

CONCLUSION

Our meta-analysis provides evidence supporting the efficacy of adjunctive cariprazine in patients with refractory MDD. However, it is essential to consider the safety profile of cariprazine, particularly the increased risk of adverse events. The vigilant monitoring and management of these side effects should be integrated into clinical practice to minimize discontinuation rates and optimize patient outcomes.

Valproic acid monitoring: Serum prediction using a machine learning framework from multicenter real-world data

BACKGROUND

Our study employs machine learning to predict serum valproic acid (VPA) concentrations, aiming to contribute to the development of non-invasive assays for therapeutic drug monitoring.

METHODS

Medical records from 2002 to 2019 were obtained from the Taiwan Chang Gung Research Database. Using various machine learning algorithms, we developed predictive models to classify serum VPA concentrations into two categories (1-50 μg/ml or 51-100 μg/ml) and predicted the exact concentration value. The models were trained on 5142 samples and tested on 644 independent samples. Accuracy was the main metric used to evaluate model performance, with a tolerance of 20 μg/ml for continuous variables. Furthermore, we identified important features and developed simplified models with fewer features.

RESULTS

The models achieved an average accuracy of 0.80-0.86 for binary outcomes and 0.72-0.88 for continuous outcome. Ten top features associated with higher serum VPA levels included higher VPA last and daily doses, bipolar disorder or schizophrenia spectrum disorder diagnoses, elevated levels of serum albumin, calcium, and creatinine, low platelet count, low percentage of segmented white blood cells, and low red cell distribution width-coefficient of variation. The simplified models had an average accuracy of 0.82-0.86 for binary outcome and 0.70-0.86 for continuous outcome.

LIMITATIONS

The study's predictive model lacked external test data from outside the hospital for validation.

CONCLUSIONS

Machine learning models have the potential to integrate real-world data and predict VPA concentrations, providing a promising tool for reducing the need for frequent monitoring of serum levels in clinical practice.

Comparing different non-invasive brain stimulation interventions for bipolar depression treatment: A network meta-analysis of randomized controlled trials

Non-invasive brain stimulation (NIBS) is a promising treatment for bipolar depression. We systematically searched for randomized controlled trials on NIBS for treating bipolar depression (INPLASY No: 202340019). Eighteen articles (N = 617) were eligible for network meta-analysis. Effect sizes were reported as standardized mean differences (SMDs) or odds ratios (ORs) with 95% confidence intervals (CIs). Anodal transcranial direct current stimulation over F3 plus cathodal transcranial direct current stimulation over F4 (a-tDCS-F3 +c-tDCS-F4; SMD = -1.18, 95%CIs = -1.66 to -0.69, N = 77), high-definition tDCS over F3 (HD-tDCS-F3; -1.17, -2.00 to -0.35, 25), high frequency deep transcranial magnetic stimulation (HF-dTMS; -0.81, -1.62 to -0.001, 25), and high frequency repetitive TMS over F3 plus low frequency repetitive TMS over F4 (HF-rTMS-F3 +LF-rTMS-F4; -0.77, -1.43 to -0.11, 38) significantly improved depressive symptoms compared to sham controls. Only a-tDCS-F3 +c-tDCS-F4 (OR = 4.53, 95%CIs = 1.51-13.65) and HF-rTMS-F3 +LF-rTMS-F4 (4.69, 1.02-21.56) showed higher response rates. No active NIBS interventions exhibited significant differences in dropout or side effect rates, compared with sham controls.