Efficacy and acceptability of pharmacotherapy for comorbid anxiety symptoms in bipolar disorder: A systematic review and meta-analysis

Patient-rated scales improve the classification accuracy for patients with depression and anxiety disorder: a linear discriminant analysis

BACKGROUND

The current study aimed to investigate the performances of clinical scales rated by clinicians and patients as well as cognitive function tests in distinguishing patients with affective and anxiety disorders from healthy controls (HCs).

METHODS

We recruited a total of 122 subjects, comprising 24 patients with bipolar disorder (BD), 34 patients with major depressive disorder (MDD), 29 patients with anxiety disorder (AD), and 35 matched HCs. Three clinician-rated scales and five patient-rated scales were used to quantify clinical symptoms, while four cognitive tests were employed to measure cognitive functions in all subjects. Fisher's discriminant analysis (FDA) was employed to distinguish patients from HCs, as well as to discriminate patient sub-groups from each other. In the FDA model, the prior probability of each group was set as 0.5 in the two-group classification and 0.25 in the four-group classification.

RESULTS

The results showed that patient-rated scales achieved higher classification accuracies than clinician-rated scales in identifying MDD and AD from HCs. In contrast, cognitive tests exhibited the lowest accuracy.

CONCLUSIONS

These findings suggest that patient-rated scales might improve the classification accuracy for patients with MDD and AD.

The effect of lithium variation coefficient on the risk of attack in patients with bipolar disorder: A pilot study

BACKGROUND

This study examines the association between the coefficient of variation (%CV) of lithium levels and episode risk and frequency in bipolar patients maintaining serum lithium levels within the therapeutic range.

METHODS

We retrospectively reviewed patients with bipolar disorder under care from 2018 to 2022. Inclusion criteria were at least 2 years of follow-up, a minimum of three annual lithium level measurements within the therapeutic range. Patients were categorized based on seizure status. We calculated mean lithium levels, standard deviation (SD), and %CV.

RESULTS

The study included 75 patients (patients with-without episodes, 39-36). Demographic data revealed no significant differences. While mean lithium levels showed no significant disparity between groups, SD and %CV were notably higher in patients with episodes (P < .05). ROC analysis demonstrated AUC values of 0.722 (95% CI: 0.607-0.836 P = .001) for %CV and 0.709 (95% CI: 0.593-0.826; P = .002) for SD. The optimal %CV cutoff was 17.39, with 67% sensitivity and 69% specificity. A weak correlation was found between %CV and the number of episodes (P = .001, r = 0.376). The post-hoc power analysis for this study was 0.78.

CONCLUSIONS

Despite acceptable lithium levels, patients with recent episodes exhibited significant lithium level fluctuations. Integrating %CV with real-time lithium measurements during bipolar disorder follow-up may enhance clinical monitoring and seizure prediction.

The effect of second-generation antipsychotics on anxiety/depression in patients with schizophrenia: A systematic review and meta-analysis

OBJECTIVE

Despite the high prevalence of anxiety in schizophrenia, no established guideline exists for the management of these symptoms. We aimed to synthesize evidence on the effect of second-generation antipsychotics (SGAs) on anxiety in patients with schizophrenia.

METHODS

We systematically searched Medline, Embase, PsycInfo, Web of Science, PubMed, and Cochrane library to identify randomized controlled trials of SGAs that reporting anxiety measures in schizophrenia. The search was limited to English-language articles published before February 2024. Data were pooled using a random-effects model.

RESULTS

Among 48 eligible studies, 29 (n = 7712) were included in the meta-analyses comparing SGAs to placebo, haloperidol, or another SGAs for their effect on anxiety/depression. SGAs had a small effect on anxiety/depression versus placebo (SMD = -0.28 (95 % CI [-0.34, -0.21], p < .00001, I2 = 47 %, n = 5576)) associated with efficacy for positive (z = 5.679, p < .001) and negative symptoms (z = 4.490, p < .001). Furthermore, SGAs were superior to haloperidol (SMD = -0.44, 95 % CI [-0.75, -0.13], p = .005, n = 1068) with substantial study-level heterogeneity (I2 = 85 %). Excluding one study of quetiapine in first-episode patients (SMD = -3.05, n = 73), SGAs showed a small effect on anxiety/depression versus haloperidol without heterogeneity (SMD = -0.23, 95 % CI [-0.35, -0.12], p = 01; I2 = %0). Risperidone's effect on anxiety/depression was comparable to olanzapine (SMD = -0.02, 95 % CI [-0.24,0.20], p = .87, I2 = 45 %, n = 753) and amisulpride (SMD = 0.27, 95 % CI [-1.08,0.61], p = .13, I2 = 50 %, n = 315).

CONCLUSION

While SGAs showed a small effect on anxiety/depression, the findings are inconclusive due to scarcity of research on comorbid anxiety in schizophrenia, heterogeneity of anxiety symptoms, and the scales used to measure anxiety. Further studies employing specific anxiety scales are required to explore antipsychotics, considering their receptor affinity and augmentation with serotonin/norepinephrine reuptake inhibitors or benzodiazepines for managing anxiety in schizophrenia.

Machine learning and the prediction of suicide in psychiatric populations: a systematic review

Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.

Preventive interventions for individuals at risk of developing bipolar disorder: A systematic review and meta-analysis

BACKGROUND

This systematic review and meta-analysis aimed to explore whether early interventions can reduce affective symptoms and have long-term benefits among individuals at risk of bipolar disorder (BD).

METHODS

The PubMed, Embase, and Web of Science databases were searched. The primary outcome was continuous symptom scores before and after treatment. Random effects meta-analyses were conducted for each outcome arm studied and pooled mean difference estimates were calculated.

RESULTS

The search identified 10 controlled studies involving 425 participants and 6 single-arm studies involving 90 participants. For controlled trials, meta-analysis showed that the interventions led to greater reduction in clinical global score than placebo (standardized mean differences (SMD) = -0.96, 95 % CI:-1.32, -0.60), and supported a long-term longitudinal effect for pharmacotherapy (SMD = -0.42, 95 % CI: -0.79, -0.05). For single-arm trials, both pharmacotherapy and psychotherapy showed efficacy for depressive symptoms, while pharmacotherapy only showed efficacy for hypomania symptoms (effect size (ES) = -9.16, 95 % CI:-11.29, -7.04). Discontinuation of pharmacotherapy due to adverse effects did not show a difference.

LIMITATIONS

The primary limitations are the small number of RCTs and the influence of medication dosage.

CONCLUSIONS

Based on the limited available data, early interventions show efficacy for individuals at risk of BD. Psychological therapy might be more beneficial for depressive symptoms and have long-term benefits for hypomania. Pharmacotherapy may be appropriate in situations of severe hypomanic symptoms and the poor functioning. Large, well-designed, double-blind -controlled trials are needed to make solid conclusions about the efficacy of early interventions.

Diagnosis and management of bipolar disorders

Bipolar disorders (BDs) are recurrent and sometimes chronic disorders of mood that affect around 2% of the world's population and encompass a spectrum between severe elevated and excitable mood states (mania) to the dysphoria, low energy, and despondency of depressive episodes. The illness commonly starts in young adults and is a leading cause of disability and premature mortality. The clinical manifestations of bipolar disorder can be markedly varied between and within individuals across their lifespan. Early diagnosis is challenging and misdiagnoses are frequent, potentially resulting in missed early intervention and increasing the risk of iatrogenic harm. Over 15 approved treatments exist for the various phases of bipolar disorder, but outcomes are often suboptimal owing to insufficient efficacy, side effects, or lack of availability. Lithium, the first approved treatment for bipolar disorder, continues to be the most effective drug overall, although full remission is only seen in a subset of patients. Newer atypical antipsychotics are increasingly being found to be effective in the treatment of bipolar depression; however, their long term tolerability and safety are uncertain. For many with bipolar disorder, combination therapy and adjunctive psychotherapy might be necessary to treat symptoms across different phases of illness. Several classes of medications exist for treating bipolar disorder but predicting which medication is likely to be most effective or tolerable is not yet possible. As pathophysiological insights into the causes of bipolar disorders are revealed, a new era of targeted treatments aimed at causal mechanisms, be they pharmacological or psychosocial, will hopefully be developed. For the time being, however, clinical judgment, shared decision making, and empirical follow-up remain essential elements of clinical care. This review provides an overview of the clinical features, diagnostic subtypes, and major treatment modalities available to treat people with bipolar disorder, highlighting recent advances and ongoing therapeutic challenges.

A Recovery-Oriented Program for People with Bipolar Disorder through Virtual Reality-Based Cognitive Remediation: Results of a Feasibility Randomized Clinical Trial

BACKGROUND

Cognitive impairment is a frequent consequence of bipolar disorder (BD) that is difficult to prevent and treat. In addition, the quality of the preliminary evidence on the treatment of BD through Cognitive Remediation (CR) with traditional methods is poor. This study aims to evaluate the feasibility of a CR intervention with fully immersive Virtual Reality (VR) as an additional treatment for BD and offers preliminary data on its efficacy.

METHODS

Feasibility randomized controlled cross-over clinical study, with experimental condition lasting three months, crossed between two groups. Experimental condition: CR fully immersive VR recovery-oriented program plus conventional care; Control condition: conventional care. The control group began the experimental condition after a three months period of conventional care (waiting list). After the randomization of 50 people with BD diagnosis, the final sample consists of 39 participants in the experimental condition and 25 in the control condition because of dropouts.

RESULTS

Acceptability and tolerability of the intervention were good. Compared to the waitlist group, the experimental group reported a significant improvement regarding cognitive functions (memory: p = 0.003; attention: p = 0.002, verbal fluency: p = 0.010, executive function: p = 0.003), depressive symptoms (p = 0.030), emotional awareness (p = 0.007) and biological rhythms (p = 0.029).

CONCLUSIONS

The results are preliminary and cannot be considered exhaustive due to the small sample size. However, the evidence of efficacy, together with the good acceptability of the intervention, is of interest. These results suggest the need to conduct studies with larger samples that can confirm this data.

TRIAL REGISTRATION

ClinicalTrialsgov NCT05070065, registered in September 2021.

Comorbidities in Youth with Bipolar Disorder: Clinical Features and Pharmacological Management

BACKGROUND

Bipolar Disorder (BD) is a highly comorbid condition, and rates of cooccurring disorders are even higher in youth. Comorbid disorders strongly affect clinical presentation, natural course, prognosis, and treatment.

METHODS

This review focuses on the clinical and treatment implications of the comorbidity between BD and Attention-Deficit/Hyperactivity Disorder, disruptive behavior disorders (Oppositional Defiant Disorder and/or Conduct Disorder), alcohol and substance use disorders, Autism Spectrum Disorder, anxiety disorders, Obsessive-Compulsive Disorder, and eating disorders.

RESULTS

These associations define specific conditions which are not simply a sum of different clinical pictures, but occur as distinct and complex combinations with specific developmental pathways over time and selective therapeutic requirements. Pharmacological treatments can improve these clinical pictures by addressing the comorbid conditions, though the same treatments may also worsen BD by inducing manic or depressive switches.

CONCLUSION

The timely identification of BD comorbidities may have relevant clinical implications in terms of symptomatology, course, treatment and outcome. Specific studies addressing the pharmacological management of BD and comorbidities are still scarce, and information is particularly lacking in children and adolescents; for this reason, the present review also included studies conducted on adult samples. Developmentally-sensitive controlled clinical trials are thus warranted to improve the prognosis of these highly complex patients, requiring timely and finely personalized therapies.

The clinical characterization of the adult patient with bipolar disorder aimed at personalization of management

Bipolar disorder is heterogeneous in phenomenology, illness trajectory, and response to treatment. Despite evidence for the efficacy of multimodal-ity interventions, the majority of persons affected by this disorder do not achieve and sustain full syndromal recovery. It is eagerly anticipated that combining datasets across various information sources (e.g., hierarchical "multi-omic" measures, electronic health records), analyzed using advanced computational methods (e.g., machine learning), will inform future diagnosis and treatment selection. In the interim, identifying clinically meaningful subgroups of persons with the disorder having differential response to specific treatments at point-of-care is an empirical priority. This paper endeavours to synthesize salient domains in the clinical characterization of the adult patient with bipolar disorder, with the overarching aim to improve health outcomes by informing patient management and treatment considerations. Extant data indicate that characterizing select domains in bipolar disorder provides actionable information and guides shared decision making. For example, it is robustly established that the presence of mixed features - especially during depressive episodes - and of physical and psychiatric comorbidities informs illness trajectory, response to treatment, and suicide risk. In addition, early environmental exposures (e.g., sexual and physical abuse, emotional neglect) are highly associated with more complicated illness presentations, inviting the need for developmentally-oriented and integrated treatment approaches. There have been significant advances in validating subtypes of bipolar disorder (e.g., bipolar I vs. II disorder), particularly in regard to pharmacological interventions. As with other severe mental disorders, social functioning, interpersonal/family relationships and internalized stigma are domains highly relevant to relapse risk, health outcomes, and quality of life. The elevated standardized mortality ratio for completed suicide and suicidal behaviour in bipolar disorder invites the need for characterization of this domain in all patients. The framework of this paper is to describe all the above salient domains, providing a synthesis of extant literature and recommendations for decision support tools and clinical metrics that can be implemented at point-of-care.