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Abstract Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician’s office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen’s κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

Bipolar depression remains a clinical challenge with a quarter of patients failing to respond to initial conventional treatments. Although ketamine has been extensively studied in unipolar depression, its role in bipolar disorder remains inconclusive. The aim of our scoping review was to comprehensively synthesize the current clinical literature around ketamine use in bipolar depression. A total of 10 clinical studies (5 randomized controlled trials and 5 open label studies) were selected. The preliminary evidence, albeit weak, suggests that ketamine is a promising treatment and calls for further interest from the research community. Overall, ketamine treatment appeared to be tolerable with minimal risk for manic/hypomanic switching and showed some effectiveness across parameters of depression and suicidality. Moreover, ketamine is a potential treatment agent in patients with treatment-resistant bipolar depression with promising data extracted from extant controlled trials and real-world effectiveness studies. Future studies are needed to identify ketamine’s role in acute and maintenance treatment phases of bipolar depression. Moreover, future researchers should study the recurrence prevention and anti-suicidal effects of ketamine in the treatment of bipolar depression.

Introduction: Early recognition of bipolar disorder improves the prognosis and decreases the burden of the disease. However, there is a significant delay in diagnosis. Multiple risk factors for bipolar disorder have been identified and a population at high-risk for the disorder has been more precisely defined. These advances have allowed the development of risk calculators to predict individual risk of conversion to bipolar disorder. This review aims to identify the risk calculators for bipolar disorder and assess their clinical applicability. Methods: A systematic review of original studies on the development of risk calculators in bipolar disorder was performed. The studies’ quality was evaluated with the Newcastle-Ottawa Quality Assessment Form for Cohort Studies and according to recommendations of the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis Initiative. Results: Three studies met the inclusion criteria; one developed a risk calculator of conversion from major depressive episode to bipolar disorder; one of conversion to new-onset bipolar spectrum disorders in offspring of parents with bipolar disorder; and the last one of conversion in youths with bipolar disorder not-otherwise-specified. Conclusions: The calculators reviewed in this article present good discrimination power for bipolar disorder, although future replication and validation of the models is needed.