Background: The Stanley Foundation Bipolar Treatment Outcome Network (SFBN) recruited more than 900 outpatients from 1995 to 2002 from 4 sites in the United States (US) and 3 in the Netherlands and Germany (abbreviated as Europe). When funding was discontinued, the international group of investigators continued to work together as the Bipolar Collaborative Network (BCN), publishing so far 87 peer-reviewed manuscripts. On the 25th year anniversary of its founding, publication of a brief summary of some of the major findings appeared appropriate. Important insights into the course and treatment of adult outpatients with bipolar disorder were revealed and some methodological issues and lessons learned will be discussed.
Results: The illness is recurrent and pernicious and difficult to bring to a long-term remission. Virtually all aspects of the illness were more prevalent in the US compared to Europe. This included vastly more patients with early onset illness and those with more psychosocial adversity in childhood; more genetic vulnerability; more anxiety and substance abuse comorbidity; more episodes and rapid cycling; and more treatment non-responsiveness.
Conclusions: The findings provide a road map for a new round of much needed clinical treatment research studies. They also emphasize the need for the formation of a new network focusing on child and youth onset of mood disorders with a goal to achieve early precision diagnostics for intervention and prevention in attempting to make the course of bipolar illness more benign.
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http://dx.doi.org/10.1186/s40345-020-00218-w | DOI Listing |
Introduction: The St. Göran Bipolar Project (SBP) is a longitudinal outpatient study investigation aimed at identifying predictive factors associated with long-term outcomes in individuals with bipolar disorder. These outcomes include cognitive function, relapse rate, treatment responses, and functional outcomes.
View Article and Find Full Text PDFEur Arch Psychiatry Clin Neurosci
December 2024
Department of Psychiatry, University of Muenster, Muenster, Germany.
Schizophrenia (SCZ), bipolar (BD) and major depression disorder (MDD) are severe psychiatric disorders that are challenging to treat, often leading to treatment resistance (TR). It is crucial to develop effective methods to identify and treat patients at risk of TR at an early stage in a personalized manner, considering their biological basis, their clinical and psychosocial characteristics. Effective translation of theoretical knowledge into clinical practice is essential for achieving this goal.
View Article and Find Full Text PDFPsychiatry Res
December 2024
Group of Epidemiology of Mental Disorders and Ageing, Sant Joan de Déu Research Institute, Esplugues de Llobregat, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.
Introduction: This study investigated the risk of SARS-CoV-2 infection and severe COVID-19 outcomes among different mental health diagnoses and the role of sex in these associations.
Methods: Using electronic records from Catalonia, we identified adults receiving mental health care from 2017-2019 with diagnoses of non-affective psychosis (NAP), bipolar disorder (BD), depressive disorder (DEP), stress-related disorders, neurotic/somatoform disorders (NSD), and substance misuse (SUB) (exposed). The outcomes assessed were SARS-CoV-2 infection, COVID-19 hospitalization, and COVID-19-related death, compared to matched individuals without these mental disorders (unexposed).
Brain Behav Immun
December 2024
Scientific Initiative for Neuropsychiatric and Psychopharmacological Studies (SINAPS), University Psychiatric Hospital Campus Duffel (UPCD), Rooienberg 19, 2570 Duffel, Belgium; Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Campus Drie Eiken, S.003, Universiteitsplein 1, 2610 Wilrijk, Belgium.
In a letter critiquing our manuscript, Takefuji highlights general pitfalls in machine learning, without directly engaging with our study. The comments provide generic advice rather than a specific critique of our methods or findings. Despite raising important topics, the concerns reflect standard risks in machine learning, which we were aware of and explicitly addressed in our analyses.
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