All-cause and cause-specific mortality among people with bipolar disorder: a large-scale systematic review and meta-analysis.

OBJECTIVE: Bipolar disorder (BD) is associated with premature mortality. All-cause and specific mortality risks in this population remain unclear, and more studies are still needed to further understand this issue and guide individual and public strategies to prevent mortality in bipolar disorder Thus, a systematic review and meta-analysis of studies assessing mortality risk in people with BD versus the general population was conducted. The primary outcome was all-cause mortality, whilst secondary outcomes were mortality due to suicide, natural, unnatural, and specific-causes mortality. RESULTS: Fifty-seven studies were included (BD; n = 678,353). All-cause mortality was increased in people with BD (RR = 2.02, 95% CI: 1.89-2.16, k = 39). Specific-cause mortality was highest for suicide (RR = 11.69, 95% CI: 9.22-14.81, k = 25). Risk of death due to unnatural causes (RR = 7.29, 95% CI: 6.41-8.28, k = 17) and natural causes (RR = 1.90, 95% CI: 1.75-2.06, k = 17) were also increased. Among specific natural causes analyzed, infectious causes had the higher RR (RR = 4,38, 95%CI: 1.5-12.69, k = 3), but the analysis was limited by the inclusion of few studies. Mortality risk due to respiratory (RR = 3.18, 95% CI: 2.55-3.96, k = 6), cardiovascular (RR = 1.76, 95% CI: 1.53-2.01, k = 27), and cerebrovascular (RR = 1.57, 95% CI: 1.34-1.84, k = 13) causes were increased as well. No difference was identified in mortality by cancer (RR = 0.99, 95% CI: 0.88-1.11, k = 16). Subgroup analyses and meta-regression did not affect the findings. CONCLUSION: Results presented in this meta-analysis show that risk of premature death in BD is not only due to suicide and unnatural causes, but somatic comorbidities are also implicated. Not only the prevention of suicide, but also the promotion of physical health and the prevention of physical conditions in individuals with BD may mitigate the premature mortality in this population. Notwithstanding this is to our knowledge the largest synthesis of evidence on BD-related mortality, further well-designed studies are still warranted to inform this field.

Emotion regulation in pediatric bipolar disorder: A meta-analysis of published studies.

BACKGROUND: Emotion regulation is a relatively recent topic in psychiatry, and has only recently begun to be tested across Pediatric Bipolar Disorder (PBD). To date, no meta-analysis has investigated the presence of emotion regulation deficits in PBD patients. OBJECTIVES: The aim of this study is to understand where the literature stands on this topic, as well as how different researchers are measuring and grasping the concept of emotion regulation in pediatric bipolar disorders. METHODS: A systematic search of trials using the terms ("Pediatric Bipolar Disorder") AND ("Emotion Regulation" OR "Affect Regulation" OR "Mood Lability" OR "Mood Instability" OR "Irritability") was conducted using PubMed, Google Scholar, ResearchGate, Web of Science and Psych Info databases. Of the initial 366 articles identified, 8 met eligibility criteria for the meta-analysis and were included in this study. RESULTS: There is a statistically significant difference in Accuracy in Emotion Regulation tasks, with a tendency for lower accuracy in PBD patients; however, both groups did not differ statistically regarding Response Time. CONCLUSION: Our data suggests that PBD patients do present emotion regulation deficits, particularly regarding facial emotion recognition and affective language interference tasks mediated by cognitive assignments. These results have important implications in developing novel psychotherapeutic interventions for this population.

Exploring machine learning to predict depressive relapses of bipolar disorder patients.

BACKGROUND: Bipolar disorder (BD) is a chronic mood disorder characterized by recurrent episodes of mania or hypomania and depression, expressed by changes in energy levels and behavior. However, most of relapse studies use evidence-based approaches with statistical methods. With the advance of the precision medicine this study aims to use machine learning (ML) approaches as a possible predictor in depressive relapses in BD. METHOD: Four accepted and well used ML algorithms (Support Vector Machines, Random Forests, Naïve Bayes, and Multilayer Perceptron) were applied to the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) dataset in a cohort of 800 patients (507 patients presented depressive relapse and 293 did not), who became euthymic during the study and were followed for one year. RESULTS: The ML algorithms presented reasonable performance in the prediction task, ranging from 61 to 80% in the F-measure. The Random Forest algorithm obtained a higher average of performance (Relapse Group 68%; No Relapse Group 74%). The three most important mood symptoms observed in the relapse visit (Random Forest) were: interest; depression mood and energy. LIMITATIONS: Social and psychological parameters such as marital status, social support system, personality traits, might be an important predictor in depressive relapses, although we did not compute this data in our study. CONCLUSIONS: Our findings indicate that applying precision medicine models by means of machine learning in BD studies could be feasible as a sensible approach to better support medical decision-making in the BD treatment and prevention of future relapses.