A risk calculator to predict suicide attempts among individuals with early-onset bipolar disorder

Machine learning applied to the prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: A systematic review

BACKGROUND

Bipolar disorder (BD) is associated with increased morbidity/mortality. Adverse outcome prediction might help with the management of patients with BD.

METHODS

We systematically reviewed the performance of machine learning (ML) studies in predicting adverse outcomes (relapse or recurrence, hospital admission, and suicide-related events) in patients with BD. Demographic, clinical, and neuroimaging-related poor outcome predictors were also reviewed. Three databases (PubMed, Scopus, and Web of Science) were explored from inception to July 2023.

RESULTS

Eighteen studies, accounting for >30,000 patients, were included. Support vector machine, decision trees, random forest, and logistic regression were the most frequently used ML algorithms. ML models' area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity ranged from 0.71 to 0.98, 72.7-92.8 %, and 59.0-95.2 % for relapse/recurrence prediction (4 studies (3 on relapses and 1 on recurrences). The corresponding values were 0.78-0.88, 21.4-100 %, and 77.0-99.7 % for hospital admissions (3 studies, 21,266 patients), and 0.71-0.99, 44.4-97.9 %, and 38.9-95.0 % for suicide-related events (10 studies, 5558 patients). Also, one study addressed a combination of the interest outcomes. Adverse outcome predictors included early onset BD, BD type I, comorbid psychiatric or substance use disorder, circadian rhythm disruption, hospitalization characteristics, and neuroimaging parameters, including increased dynamic amplitude of low-frequency fluctuation, decreased frontolimbic functional connectivity and aberrant dynamic functional connectivity in corticostriatal circuitry.

CONCLUSIONS

ML models can predict adverse outcomes of BD with relatively acceptable performance measures. Future studies with larger samples and nested cross-validation validation should be conducted to reach more reliable results.

Validation of a youth suicide risk calculator in an adult sample with bipolar disorder

BACKGROUND

Bipolar disorder (BD) conveys the highest risk of suicide of all mental disorders. We sought to externally validate a risk calculator (RC) of suicide attempts developed in youth with BD from the Course and Outcome of Bipolar Youth (COBY) study in an adult sample.

METHODS

A prospective cohort of adults with BD from the National Institute of Mental Health Collaborative Depression Study (CDS; N = 427; mean (+/- SD) age at intake (36 +/- 13 years)) was secondarily analyzed to validate the COBY RC for one-year risk of suicide attempts/deaths. Nine of the ten predictor variables from the COBY RC were available in the CDS and used: age, age of mood disorder onset, first and second (partial) degree family history of suicide, history of psychotic symptoms, substance use disorder, prior suicide attempt, socioeconomic status, and non-suicidal self-injury (prospectively, incompletely at baseline).

RESULTS

Over a mean (SD) follow-up of 19 (10) years, 29 % of the CDS sample attempted suicide. The RC predicted suicide attempts/deaths over one-year follow-up with an area under the receiver operating characteristic curve (AUC) of 0.78 (95 % CI 0.75-0.80). The RC performed slightly better in those with a younger age of mood disorder onset.

LIMITATIONS

Clinical samples may limit generalizability; the RC does not assess more acute suicide risk.

CONCLUSIONS

One-year risk of suicide attempts/deaths can be predicted with acceptable accuracy in youth and adults with BD, comparable to commonly used RCs to predict cardiovascular risk. This RC may help identify higher-risk individuals with BD for personalized treatment and research. https://cobysuicideattemptsrc.shinyapps.io/Shiny.

Molecular Changes Associated with Suicide

Suicide is a serious global public health problem, with a worrying recent increase in suicide rates in both adolescent and adult populations. However, it is essential to recognize that suicide is preventable. A myriad of factors contributes to an individual's vulnerability to suicide. These factors include various potential causes, from psychiatric disorders to genetic and epigenetic alterations. These changes can induce dysfunctions in crucial systems such as the serotonergic, cannabinoid, and hypothalamic-pituitary-adrenal axes. In addition, early life experiences of abuse can profoundly impact an individual's ability to cope with stress, ultimately leading to changes in the inflammatory system, which is a significant risk factor for suicidal behavior. Thus, it is clear that suicidal behavior may result from a confluence of multiple factors. This review examines the primary risk factors associated with suicidal behavior, including psychiatric disorders, early life adversities, and epigenetic modifications. Our goal is to elucidate the molecular changes at the genetic, epigenetic, and molecular levels in the brains of individuals who have taken their own lives and in the plasma and peripheral mononuclear cells of suicide attempters and how these changes may serve as predisposing factors for suicidal tendencies.

Suicide Assessment and Prevention in Bipolar Disorder: How Current Evidence Can Inform Clinical Practice

Bipolar disorder is associated with a considerable risk of suicide, and this fact must be incorporated into management of all patients with the condition. This article highlights the importance of a more nuanced understanding of the factors associated with the increased risk of suicidal behavior in people diagnosed as having bipolar disorder and interventions that could mitigate it. Several sociodemographic, clinical, environmental, and other variables have been associated with suicide attempts or deaths in bipolar disorder. Youths with bipolar disorder are a particularly vulnerable group, and their trajectory of illness could be modified by early interventions. Several medications have been studied regarding their relationship to suicide risk in bipolar disorder, and interventional psychiatry is a newer area of research focus. Finally, community-based approaches can be incorporated into a comprehensive approach to suicide prevention. This article summarizes the current understanding of key variables that can help inform a clinical risk assessment of individuals and interventions that can be employed in suicide prevention in bipolar disorder.

Conceptualizing eating disorder recovery research: Current perspectives and future research directions

BACKGROUND

How we research eating disorder (ED) recovery impacts what we know (perceive as fact) about it. Traditionally, research has focused more on the "what" of recovery (e.g., establishing criteria for recovery, reaching consensus definitions) than the "how" of recovery research (e.g., type of methodologies, triangulation of perspectives). In this paper we aim to provide an overview of the ED field's current perspectives on recovery, discuss how our methodologies shape what is known about recovery, and suggest a broadening of our methodological "toolkits" in order to form a more complete picture of recovery. BODY: This paper examines commonly used methodologies in research, and explores how incorporating different perspectives can add to our understanding of the recovery process. To do this, we (1) provide an overview of commonly used methodologies (quantitative, qualitative), (2) consider their benefits and limitations, (3) explore newer approaches, including mixed-methods, creative methods (e.g., Photovoice, digital storytelling), and multi-methods (e.g., quantitative, qualitative, creative methods, psycho/physiological, behavioral, laboratory, online observations), and (4) suggest that broadening our methodological "toolkits" could spur more nuanced and specific insights about ED recoveries. We propose a potential future research model that would ideally have a multi-methods design, incorporate different perspectives (e.g., expanding recruitment of diverse participants, including supportive others, in study co-creation), and a longitudinal course (e.g., capturing cognitive and emotional recovery, which often comes after physical). In this way, we hope to move the field towards different, more comprehensive, perspectives on ED recovery.

CONCLUSION

Our current perspectives on studying ED recovery leave critical gaps in our knowledge about the process. The traditional research methodologies impact our conceptualization of recovery definitions, and in turn limit our understanding of the phenomenon. We suggest that we expand our range of methodologies, perspectives, and timeframes in research, in order to form a more complete picture of what is possible in recovery; the multiple aspects of an individual's life that can improve, the greater number of people who can recover than previously believed, and the reaffirmation of hope that, even after decades, individuals can begin, and successfully continue, their ED recovery process.