Mean rates of bipolar disorder were higher than commonly acknowledged and not significantly different in US compared to non-US samples, nor was there evidence of an increase in rates of bipolar disorder in the community over time. Differences in diagnostic criteria were a main driver of different rates across studies.

Objectives Over the past two decades, there has been tremendous growth in research regarding bipolar disorder (BD) among children and adolescents (ie, pediatric BD [PBD]). The primary purpose of this article is to distill the extant literature, dispel myths or exaggerated assertions in the field, and disseminate clinically relevant findings. Methods An international group of experts completed a selective review of the literature, emphasizing areas of consensus, identifying limitations and gaps in the literature, and highlighting future directions to mitigate these gaps. Results Substantial, and increasingly international, research has accumulated regarding the phenomenology, differential diagnosis, course, treatment, and neurobiology of PBD. Prior division around the role of irritability and of screening tools in diagnosis has largely abated. Gold-standard pharmacologic trials inform treatment of manic/mixed episodes, whereas fewer data address bipolar depression and maintenance/continuation treatment. Adjunctive psychosocial treatment provides a forum for psychoeducation and targets primarily depressive symptoms. Numerous neurocognitive and neuroimaging studies, and increasing peripheral biomarker studies, largely converge with prior findings from adults with BD. Conclusions As data have accumulated and controversy has dissipated, the field has moved past existential questions about PBD toward defining and pursuing pressing clinical and scientific priorities that remain. The overall body of evidence supports the position that perceptions about marked international (US vs elsewhere) and developmental (pediatric vs adult) differences have been overstated, although additional research on these topics is warranted. Traction toward improved outcomes will be supported by continued emphasis on pathophysiology and novel therapeutics.

No abstract

Evidence-based assessment (EBA) streamlines literature reviewing and organizing clinical assessment by targeting the vital few topics, "satisficing," and focusing on three major phases of clinical activity: prediction of diagnoses or other criteria, prescription of treatment or moderating factors, and process measurement. EBA is an organizing framework for applying a dozen steps to guide treatment. Technology is changing clinical assessment by increasing the efficiency and accuracy of scoring and feedback, as well as innovations that make more intensive assessment feasible. Fully implementing EBA suggests changes in training and requires a practice overhaul in exchange for greater efficiency, more accurate decisions, incrementally better outcomes, and increased service accessibility that could enable psychological science to help more people.

Although most patients who experience a first-episode of psychosis achieve remission of positive psychotic symptoms, relapse is common. Existing relapse evaluation strategies are limited by their reliance on direct and timely contact with professionals, and accurate reporting of symptoms. A method by which to objectively identify early relapse warning signs could facilitate swift intervention. We collected 52,815 Facebook posts across 51 participants with recent onset psychosis (mean age = 23.96 years; 70.58% male) and applied anomaly detection to explore linguistic and behavioral changes associated with psychotic relapse. We built a one-class classification model that makes patient-specific personalized predictions on risk to relapse. Significant differences were identified in the words posted to Facebook in the month preceding a relapse hospitalization compared to periods of relative health, including increased usage of words belonging to the swear (p < 0.0001, Wilcoxon signed rank test), anger (p < 0.001), and death (p < 0.0001) categories, decreased usage of words belonging to work (p = 0.00579), friends (p < 0.0001), and health (p < 0.0001) categories, as well as a significantly increased use of first (p < 0.0001) and second-person (p  < 0.001) pronouns. We additionally observed a significant increase in co-tagging (p < 0.001) and friending (p < 0.0001) behaviors in the month before a relapse hospitalization. Our classifier achieved a specificity of 0.71 in predicting relapse. Results indicate that social media activity captures objective linguistic and behavioral markers of psychotic relapse in young individuals with recent onset psychosis. Machine-learning models were capable of making personalized predictions of imminent relapse hospitalizations at the patient-specific level.