Prediction of suicide attempts in a prospective cohort study with a nationally representative sample of the US population.

BACKGROUND: There is still little knowledge of objective suicide risk stratification. METHODS: This study aims to develop models using machine-learning approaches to predict suicide attempt (1) among survey participants in a nationally representative sample and (2) among participants with lifetime major depressive episodes. We used a cohort called the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) that was conducted in two waves and included a nationally representative sample of the adult population in the United States. Wave 1 involved 43 093 respondents and wave 2 involved 34 653 completed face-to-face reinterviews with wave 1 participants. Predictor variables included clinical, stressful life events, and sociodemographic variables from wave 1; outcome included suicide attempt between wave 1 and wave 2. RESULTS: The model built with elastic net regularization distinguished individuals who had attempted suicide from those who had not with an area under the ROC curve (AUC) of 0.89, balanced accuracy 81.86%, specificity 89.22%, and sensitivity 74.51% for the general population. For participants with lifetime major depressive episodes, AUC was 0.89, balanced accuracy 81.64%, specificity 85.86%, and sensitivity 77.42%. The most important predictor variables were a diagnosis of borderline personality disorder, post-traumatic stress disorder, and being of Asian descent for the model in all participants; and previous suicide attempt, borderline personality disorder, and overnight stay in hospital because of depressive symptoms for the model in participants with lifetime major depressive episodes. Random forest and artificial neural networks had similar performance. CONCLUSIONS: Risk for suicide attempt can be estimated with high accuracy.

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force.

OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. METHOD: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. RESULTS: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. CONCLUSION: Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

Smaller left anterior cingulate cortex in non-bipolar relatives of patients with bipolar disorder

Objective: Bipolar disorder (BD) is highly heritable. The present study aimed at identifying brain morphometric features that could represent markers of BD vulnerability in non-bipolar relatives of bipolar patients. Methods: In the present study, structural magnetic resonance imaging brain scans were acquired from a total of 93 subjects, including 31 patients with BD, 31 non-bipolar relatives of BD patients, and 31 healthy controls. Volumetric measurements of the anterior cingulate cortex (ACC), lateral ventricles, amygdala, and hippocampus were completed using the automated software FreeSurfer. Results: Analysis of covariance (with age, gender, and intracranial volume as covariates) indicated smaller left ACC volumes in unaffected relatives as compared to healthy controls and BD patients (p = 0.004 and p = 0.037, respectively). No additional statistically significant differences were detected for other brain structures. Conclusion: Our findings suggest smaller left ACC volume as a viable biomarker candidate for BD.

Smaller left anterior cingulate cortex in non-bipolar relatives of patients with bipolar disorder.

OBJECTIVE: Bipolar disorder (BD) is highly heritable. The present study aimed at identifying brain morphometric features that could represent markers of BD vulnerability in non-bipolar relatives of bipolar patients. METHODS: In the present study, structural magnetic resonance imaging brain scans were acquired from a total of 93 subjects, including 31 patients with BD, 31 non-bipolar relatives of BD patients, and 31 healthy controls. Volumetric measurements of the anterior cingulate cortex (ACC), lateral ventricles, amygdala, and hippocampus were completed using the automated software FreeSurfer. RESULTS: Analysis of covariance (with age, gender, and intracranial volume as covariates) indicated smaller left ACC volumes in unaffected relatives as compared to healthy controls and BD patients (p = 0.004 and p = 0.037, respectively). No additional statistically significant differences were detected for other brain structures. CONCLUSION: Our findings suggest smaller left ACC volume as a viable biomarker candidate for BD.

Neuroprogression and illness trajectories in bipolar disorder.

INTRODUCTION: The longitudinal course of bipolar disorder is highly variable, and a subset of patients seems to present a progressive course associated with brain changes and functional impairment. Areas covered: We discuss the theory of neuroprogression in bipolar disorder. This concept considers the systemic stress response that occurs within mood episodes and late-stage deficits in functioning and cognition as well as neuroanatomic changes. We also discuss treatment refractoriness that may take place in some cases of bipolar disorder. We searched PubMed for articles published in any language up to June 4th, 2016. We found 315 abstracts and included 87 studies in our review. Expert commentary: We are of the opinion that the use of specific pharmacological strategies and functional remediation may be potentially useful in bipolar patients at late-stages. New analytic approaches using multimodal data hold the potential to help in identifying signatures of subgroups of patients who will develop a neuroprogressive course.