Oliva, Vincenzo

Abstract Objectives To systematically evaluate the certainty of evidence for treatment strategies across age groups and mood phases in bipolar disorder, and develop an open access web platform to facilitate shared decision making. Design Living umbrella review, evaluation, analysis, and communication hub (U-REACH) project. Data sources PubMed, PsycInfo, and Cochrane library databases, from inception to 19 November 2024. Eligibility criteria for selecting studies Systematic reviews with network or pairwise meta-analyses of randomised controlled trials of pharmacological, nutraceutical, psychosocial, brain stimulation, or circadian rhythm based treatments, administered as monotherapy (without concurrent interventions), augmentation treatment (interventions added to an ongoing treatment regimen), or combination treatment (simultaneous initiation of two different interventions), examining any age group, bipolar disorder phase (ie, acute bipolar depression, mania or mixed episodes, or maintenance), treatment, control, or outcome. Results 77 studies met the inclusion criteria (21 network meta-analyses and 56 pairwise meta-analyses), including 116 unique pharmacological (n=74), brain stimulation (n=18), nutraceutical (n=13), psychosocial (n=8), and circadian rhythm based (n=3) treatments as monotherapy, augmentation, or combination therapy, along with five control interventions. These studies covered 133 unique outcomes (45 efficacy outcomes and 88 safety outcomes) resulting in 2510 meta-analyses with Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) ratings of the certainty of the evidence as high (n=236), moderate (n=827), low (n=986), and very low (n=461). A communication hub, the Evidence Based Interventions for Bipolar Disorder (EBI-BD) platform, was developed and the full results are freely available ( https://ebibd-database.org ), including the preference based tool (12 interventions and 17 safety outcomes). Interventions effective across outcomes varied by phases. For bipolar depression, effective interventions in adults were cariprazine, divalproex or valproate, fluoxetine, ketamine (augmentation), lamotrigine, lumateperone, lurasidone, olanzapine, olanzapine with fluoxetine, and quetiapine, whereas effective interventions in children and adolescents were lurasidone and olanzapine with fluoxetine. For mania episodes, effective interventions in adults were aripiprazole, asenapine, carbamazepine, cariprazine, divalproex or valproate, haloperidol, lithium, olanzapine, paliperidone, quetiapine (also augmentation), risperidone (also as augmentation), tamoxifen, and ziprasidone, whereas effective interventions in children and adolescents were aripiprazole, asenapine, olanzapine, quetiapine, and risperidone. For maintenance, interventions effective across outcomes in adults were aripiprazole (also the long acting injectable formulation), asenapine, divalproex or valproate, lithium, olanzapine, group psychoeducation (augmentation), quetiapine, and risperidone long acting injectable formulation. Interventions effective across phases were aripiprazole (also as augmentation and as a long acting injectable formulation), asenapine, cariprazine, cognitive behavioural therapy (augmentation), divalproex or valproate, lamotrigine, lithium, olanzapine (also as augmentation), paliperidone, quetiapine (also as augmentation), and risperidone (also as augmentation and the long acting injectable formulation) (adults). Treatment effects by neuroscience based nomenclature classes are also reported. Conclusions The EBI-BD tool can help clinicians make evidence based, personalised treatment decisions for bipolar disorder. This resource can inform clinical guidelines and provides a foundation for continuously improving bipolar disorder care as new evidence emerges. Trial registration Open Science Framework https://osf.io/pjmvn/ Readers’ note This is a living systematic review and may be updated in the next two years if additional evidence emerges.

Abstract Background Bipolar disorder (BD) is associated with impairments in facial emotion recognition (FER), affecting social functioning and quality of life. Understanding FER deficits in BD is crucial for tailoring interventions and improving treatment outcomes. This systematic review and meta-analysis aims to evaluate FER differences among individuals with BD, unaffected first-degree relatives (FDRs), and healthy controls (HCs), exploring predictors related to patient and study characteristics. Methods We systematically searched PubMed/MEDLINE, Scopus, EMBASE, and PsycINFO databases from inception to March 28, 2024. Random-effects meta-analyses were conducted to explore differences in accuracy and reaction time during FER identification and discrimination tasks. Results A total of 100 studies were included, comprising 4920 individuals with BD (females = 56%, mean age = 34.1 ± 9.1), 676 FDRs (females = 55%, mean age = 36.1 ± 12), and 4909 HCs (females = 53.2%, mean age = 32.5 ± 9.5). Compared to HCs, adults with BD exhibited significantly lower accuracy (SMD = −0.47; 95% CIs = −0.56, −0.38) and higher reaction time (SMD = 0.57; 95%CIs = 0.33, 0.81) during facial emotion identification tasks. During facial emotion discrimination tasks, adults with BD had significantly lower accuracy than HCs (SMD = −0.59; 95%CIs = −0.78, −0.4), but similar speed. No significant differences were observed between BD and FDRs. Meta-regressions identified several predictors of FER performance, including manic symptom severity, stimulus duration, and presence of practice before task. Conclusions FER deficits appear to be a core feature of BD and require specialized, systematic assessment. Identifying these deficits may help guide interventions aimed at improving affective cognition and social outcomes in individuals with BD.

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding.