Post‐discharge suicides of inpatients with bipolar disorder in Finland

Isometsä, Erkki; Sund, Reijo; Pirkola, Sami

doi:10.1111/bdi.12237

Cited by 35 publications

(23 citation statements)

References 34 publications

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“…To our knowledge, no comparable studies exist on suicide deaths in BD, so whether patterns of incidence of suicides in BD are similar remains uncertain. However, large‐scale register‐based studies confirm marked temporal variations in risk of suicide after discharge from a psychiatric hospitalization, and among BD inpatients the risk of postdischarge suicide also varies by the type of index episode . Furthermore, the results of a psychological autopsy study of suicides in BD are consistent with our incidence findings .…”

Section: Discussionsupporting

confidence: 87%

Incidence and predictors of suicide attempts in bipolar I and II disorders: A 5‐year follow‐up study

Pallaskorpi

Suominen²,

Ketokivi

et al. 2017

Bipolar Disorders

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Funding information theFinnishMedicalFoundationObjectives: Fewlong-termstudiesonbipolardisorder(BD)haveinvestigatedtheincidenceandriskfactorsofsuicideattempts(SAs)specificallyrelatedtoillnessphases.WeexaminedtheincidenceofSAsduringdifferentphasesofBDinalong-termprospectivecohortofbipolarI(BD-I)andbipolarII(BD-II)patients,andriskfactorsspecificallyforSAsduringmajordepressiveepisodes(MDEs). MDEs, severity of depression, and cluster C personality disorders were significant predictors. Methods

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Section: Discussionsupporting

confidence: 87%

Incidence and predictors of suicide attempts in bipolar I and II disorders: A 5‐year follow‐up study

Pallaskorpi

Suominen²,

Ketokivi

et al. 2017

Bipolar Disorders

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“…A large national Finnish study of suicides post-hospitalization for BD found that intra-episodic suicide attempt was a significant predictor of suicide death after hospitalization for bipolar depression (HR = 8.05) or mixed episodes (HR = 11.1) (Isometsä et al, 2014). In a Swedish prospective cohort study of 6086 BD subjects, past suicide attempt was significantly associated with suicide attempt during follow-up among both men (OR = 3.93) and women (OR = 4.24) (Tidemalm et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

A review of factors associated with greater likelihood of suicide attempts and suicide deaths in bipolar disorder: Part II of a report of the International Society for Bipolar Disorders Task Force on Suicide in Bipolar Disorder

Schaffer

Isometsä

Azorin

et al. 2015

Aust N Z J Psychiatry

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Objectives Many factors influence the likelihood of suicide attempts or deaths in persons with bipolar disorder. One key aim of the International Society for Bipolar Disorders Task Force on Suicide was to summarize the available literature on the presence and magnitude of effect of these factors. Methods A systematic review of studies published from 1 January 1980 to 30 May 2014 identified using keywords ‘bipolar disorder’ and ‘suicide attempts or suicide’. This specific paper examined all reports on factors putatively associated with suicide attempts or suicide deaths in bipolar disorder samples. Factors were subcategorized into: (1) sociodemographics, (2) clinical characteristics of bipolar disorder, (3) comorbidities, and (4) other clinical variables. Results We identified 141 studies that examined how 20 specific factors influenced the likelihood of suicide attempts or deaths. While the level of evidence and degree of confluence varied across factors, there was at least one study that found an effect for each of the following factors: sex, age, race, marital status, religious affiliation, age of illness onset, duration of illness, bipolar disorder subtype, polarity of first episode, polarity of current/recent episode, predominant polarity, mood episode characteristics, psychosis, psychiatric comorbidity, personality characteristics, sexual dysfunction, first-degree family history of suicide or mood disorders, past suicide attempts, early life trauma, and psychosocial precipitants. Conclusion There is a wealth of data on factors that influence the likelihood of suicide attempts and suicide deaths in people with bipolar disorder. Given the heterogeneity of study samples and designs, further research is needed to replicate and determine the magnitude of effect of most of these factors. This approach can ultimately lead to enhanced risk stratification for patients with bipolar disorder.

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“…Predictor variables were selected using a priori knowledge. Notably, the majority of clinical variables selected were related to psychiatric comorbidities (Almeida et al, 2012; Bhui et al, 2012; Blackmore et al, 2008; Blanco et al, 2012; Bostwick and Pankratz, 2000; Bottlender et al, 2000; de Araújo et al, 2015; Foley et al, 2006; Galfalvy et al, 2006; Goes et al, 2012; Goldstein et al, 2012; Gonzalez-Pinto et al, 2006; Holma et al, 2014; Isometsä et al, 2014; Johnson et al, 1991; Katz et al, 2011; Lenze et al, 2000; Morina et al, 2013; Neves et al, 2009; Oquendo et al, 2010; Schaffer et al, 2014a, 2014b; Simon et al, 2004; Soloff et al, 2000; Stein, 2006; Torres et al, 2011; Webb et al, 2014), given the recent findings that effects of mental disorders on the risk of suicide attempt were exerted almost exclusively through a general psychopathology factor representing the shared effect across all mental disorders (Hoertel et al, 2015). As a result, we implemented a set of algorithms that are able to integrate the information from multiple variables to subsequently identify an individual patient's probability or risk of being a suicide attempter.…”

Section: Introductionmentioning

confidence: 99%

Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach

Passos¹,

Mwangi

Cao

et al. 2016

Journal of Affective Disorders

164

114

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Objective A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to estimate the probability of an individual patient attempting suicide. Method A total of 144 patients with mood disorders were included. Clinical variables associated with suicide attempts among patients with mood disorders and demographic variables were used to ‘train’ a machine learning algorithm. The resulting algorithm was utilized in identifying novel or ‘unseen’ individual subjects as either suicide attempters or non-attempters. Three machine learning algorithms were implemented and evaluated. Results All algorithms distinguished individual suicide attempters from non-attempters with prediction accuracy ranging between 65%-72% (p<0.05). In particular, the relevance vector machine (RVM) algorithm correctly predicted 103 out of 144 subjects translating into 72% accuracy (72.1% sensitivity and 71.3% specificity) and an area under the curve of 0.77 (p<0.0001). The most relevant predictor variables in distinguishing attempters from non-attempters included previous hospitalizations for depression, a history of psychosis, cocaine dependence and post-traumatic stress disorder (PTSD) comorbidity. Conclusion Risk for suicide attempt among patients with mood disorders can be estimated at an individual subject level by incorporating both demographic and clinical variables. Future studies should examine the performance of this model in other populations and its subsequent utility in facilitating selection of interventions to prevent suicide.

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Post‐discharge suicides of inpatients with bipolar disorder in Finland

Cited by 35 publications

References 34 publications

Incidence and predictors of suicide attempts in bipolar I and II disorders: A 5‐year follow‐up study

Incidence and predictors of suicide attempts in bipolar I and II disorders: A 5‐year follow‐up study

A review of factors associated with greater likelihood of suicide attempts and suicide deaths in bipolar disorder: Part II of a report of the International Society for Bipolar Disorders Task Force on Suicide in Bipolar Disorder

Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach

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