Suicidality and mood disorders in psychiatric emergency patients: Results from SBQ-R.
Camille Brousseau-ParadisAlain LesageCaroline LarueRéal LabelleCharles-Edouard GiguèreJessica Rassynull nullPublished in: International journal of mental health nursing (2023)
Patients with mood disorders are at high risk of suicidality, and emergency departments (ED) are essential in the management of this risk. This study aims to (1) describe the suicidal thoughts and behaviours of patients with mood disorders who come to ED; (2) assess the psychometric properties of the Suicidal Behaviours Questionnaire-Revised (SBQ-R) in a psychiatric ED; and (3) determine the best predictors of suicidality for these patients. A total of 300 participants with mood disorders recruited for the Signature Bank of the Institut universitaire en santé mentale de Montréal (IUSMM) were retained. Suicidality was assessed using the SBQ-R. Other clinical and demographic details were recorded. Bivariate analyses, correlations and multivariate regression analyses were conducted. SBQ-R's internal consistency, construct and convergent validities were also tested. In the Patient Health Questionnaire-9 (PHQ-9), 53.3% of the sample stated they had suicidal or self-harm thoughts in the last 2 weeks. The mean score obtained at the SBQ-R was 8.3. Multivariate analysis found that SBQ-R scores were associated with depressive symptoms and substance use, especially alcohol, accounting for 44.3% of the model variance. Cronbach's alpha was 0.81 [0.78, 0.84] and factor loadings for items 1-4 were 0.68, 0.88, 0.54, and 0.85, respectively. The confirmatory factor analysis indicated that the model fit the data well. The SBQ-R is a brief and valid instrument that can easily be used in busy emergency departments to assess suicide risk. Depressive symptoms and alcohol use shall also be assessed, as they are determinants of increased risk of suicidality.
Keyphrases
- depressive symptoms
- emergency department
- end stage renal disease
- psychometric properties
- sleep quality
- bipolar disorder
- newly diagnosed
- public health
- chronic kidney disease
- ejection fraction
- healthcare
- mental health
- peritoneal dialysis
- social support
- patient reported
- data analysis
- cross sectional
- case report
- health information
- big data
- machine learning
- electronic health record