Lifetime and point prevalence of psychotic symptoms in adults with bipolar disorders: a systematic review and meta-analysis

Psychotic symptoms, that we defined as delusions or hallucinations, are common in bipolar disorders (BD). This systematic review and meta-analysis aims to synthesise the literature on both lifetime and point prevalence rates of psychotic symptoms across different BD subtypes, including both BD type I (BDI) and BD type II (BDII). We performed a systematic search of Medline, PsycINFO, Embase and Cochrane Library until 5 August 2021. Fifty-four studies (N = 23 461) of adults with BD met the predefined inclusion criteria for evaluating lifetime prevalence, and 24 studies (N = 6480) for evaluating point prevalence. Quality assessment and assessment of publication bias were performed. Prevalence rates were calculated using random effects meta-analysis, here expressed as percentages with a 95% confidence interval (CI). In studies of at least moderate quality, the pooled lifetime prevalence of psychotic symptoms in BDI was 63% (95% CI 57.5–68) and 22% (95% CI 14–33) in BDII. For BDI inpatients, the pooled lifetime prevalence was 71% (95% CI 61–79). There were no studies of community samples or inpatient BDII. The pooled point prevalence of psychotic symptoms in BDI was 54% (95 CI 41–67). The point prevalence was 57% (95% CI 47–66) in manic episodes and 13% (95% CI 7–23.5) in depressive episodes. There were not enough studies in BDII, BDI depression, mixed episodes and outpatient BDI. The pooled prevalence of psychotic symptoms in BDI may be higher than previously reported. More studies are needed for depressive and mixed episodes and community samples. Prospero registration number: CRD 42017052706

Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action

Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is much smaller than its economic impact appears to demand. There is a consensus that the accurate identification of the underlying pathophysiology for BD is fundamental to realize major health benefits through better treatment and preventive regimens. However, to achieve these goals requires coordinated action and innovative approaches to boost the discovery of the neurobiological underpinnings of BD, and rapid translation of research findings into development and testing of better and more specific treatments. To this end, we here propose that only a large-scale coordinated action can be successful in integrating international big-data approaches with real-world clinical interventions. This could be achieved through the creation of a Global Bipolar Disorder Foundation, which could bring government, industry and philanthropy together in common cause. A global initiative for BD research would come at a highly opportune time given the seminal advances promised for our understanding of the genetic and brain basis of the disease and the obvious areas of unmet clinical need. Such an endeavour would embrace the principles of open science and see the strong involvement of user groups and integration of dissemi

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data