Dataset Information

Routinized categorization of suicide risk into actionable strata: Establishing the validity of an existing suicide risk assessment framework in an outpatient sample.

ABSTRACT:

Objective

The Suicide Risk Assessment and Management Decision Tree (DT) is a clinician-administered assessment that leads to risk categorizations that correspond with actionable strata. This study investigated the construct validity and test-retest reliability of the DT risk categories across two time points.

Method

Outpatients (N = 731) completed a battery of self-report measures. Spearman's correlations were used to examine the relationships between DT suicide risk level and suicidal symptoms, theory-based risk factors, psychiatric correlates, and DT suicide risk level at Timepoint 2. Correlations were analyzed for significant differences to examine the divergent validity of the DT.

Results

Results, overall, were in line with hypotheses, with the exception of depression and thwarted belongingness.

Conclusions

Findings provide evidence for the reliability, convergent validity, and discriminant validity of the DT. This clinician-administered suicide risk assessment may be useful for standardization of the assessment and management of suicide risk in outpatient clinical settings.

SUBMITTER: Gallyer AJ

PROVIDER: S-EPMC7666059 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Routinized categorization of suicide risk into actionable strata: Establishing the validity of an existing suicide risk assessment framework in an outpatient sample.

Gallyer Austin J AJ Chu Carol C Klein Kelly M KM Quintana Jazmine J Carlton Corinne C Dougherty Sean P SP Joiner Thomas E TE

Journal of clinical psychology 20200625 12

<h4>Objective</h4>The Suicide Risk Assessment and Management Decision Tree (DT) is a clinician-administered assessment that leads to risk categorizations that correspond with actionable strata. This study investigated the construct validity and test-retest reliability of the DT risk categories across two time points.<h4>Method</h4>Outpatients (N = 731) completed a battery of self-report measures. Spearman's correlations were used to examine the relationships between DT suicide risk level and sui ...[more]

PMID: 32585052

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Project description:ObjectivePatients with fibromyalgia (FM) are 10 times more likely to die by suicide than the general population. The purpose of this study was to externally validate published models predicting suicidal ideation and suicide attempts in patients with FM and to identify interpretable risk and protective factors for suicidality unique to FM.MethodsThis was a case-control study of large-scale electronic health record data collected from 1998 to 2017, identifying FM cases with validated Phenotype KnowledgeBase criteria. Model performance was measured through discrimination, including the receiver operating area under the curve (AUC), sensitivity, and specificity, and through calibration, including calibration plots. Risk factors were selected by L1 penalized regression with bootstrapping for both outcomes. Secondary utilization analyses converted time-based billing codes to equivalent minutes to estimate face-to-face provider contact.ResultsWe identified 8,879 patients with FM, with 34 known suicide attempts and 96 documented cases of suicidal ideation. External validity was good for both suicidal ideation (AUC 0.80) and attempts (AUC 0.82) with excellent calibration. Risk factors specific to suicidal ideation included polysomatic symptoms such as fatigue (odds ratio [OR] 1.29 [95% confidence interval (95% CI) 1.25-1.32]), dizziness (OR 1.25 [95% CI 1.22-1.28]), and weakness (OR 1.17 [95% CI 1.15-1.19]). Risk factors specific to suicide attempt included obesity (OR 1.18 [95% CI 1.10-1.27]) and drug dependence (OR 1.15 [95% CI 1.12-1.18]). Per utilization analyses, those patients with FM and no suicidal ideation spent 3.5 times more time in follow-up annually, and those without documented suicide attempts spent more than 40 times more time face-to-face with providers annually.ConclusionThis is the first study to successfully apply machine learning to reliably detect suicidality in patients with FM, identifying novel risk factors for suicidality and highlighting outpatient engagement as a protective factor against suicide.

Project description:IntroductionSuicide risk assessment based on self-report questionnaires is considered as problematic because risk states are dynamic and at-risk individuals may conceal suicidal intentions for several reasons. Therefore, recent research efforts increasingly focus on implicit risk markers such as the suicide attentional bias (SAB) measured with the Suicide Stroop Task (SST). However, most SST studies failed to demonstrate a SAB in individuals with suicide risk and repeatedly demonstrated insufficient psychometrics of the SST. This study aimed to investigate a SAB using a modified SST (M-SST) and to test its psychometric properties.MethodWe compared n = 61 healthy controls and a high-risk inpatient sample of n = 40 suicide ideators and n = 40 suicide attempters regarding interference scores of positive, negative and suicide-related words. Interference scores were calculated by subtracting the mean reaction time (mean RT) of the neutral words from the mean RT of the suicide-related words (mean RT Suicide -mean RT Neutral), resulting in a suicide-specific interference score. Similarly, interference scores were calculated for the positive and negative words by subtracting the mean RT of neutral words from the mean RT of positive and negative words.ResultsA Group × Interference ANOVA showed a significant interaction effect (p <.001, ηp2 = .09), indicating that group effects significantly vary across interference type. Post hoc comparisons revealed that both ideators and attempters demonstrated greater interferences only for suicide-related words compared to healthy controls, indicating a SAB in patients, while a difference between ideators and attempters was lacking. The suicide interference score classified with an AUC = 0.73, 95% CI [0.65 - 0.82], p <.001, between controls and patients with STBs. The M-SST demonstrated good internal consistency and convergent validity.DiscussionThe study adds evidence to the assumptions of the Cognitive Model of Suicide, viewing a SAB as a cognitive marker of suicide vulnerability independently of the engagement in suicidal behavior. The results' clinical implications are discussed in the context of recommended intervention strategies during an acute suicidal state. Future studies with the M-SST should include non-suicidal patient controls to investigate whether a SAB is uniquely related to suicidality.

Project description:ObjectiveThe authors sought to develop and validate models using electronic health records to predict suicide attempt and suicide death following an outpatient visit.MethodAcross seven health systems, 2,960,929 patients age 13 or older (mean age, 46 years; 62% female) made 10,275,853 specialty mental health visits and 9,685,206 primary care visits with mental health diagnoses between Jan. 1, 2009, and June 30, 2015. Health system records and state death certificate data identified suicide attempts (N=24,133) and suicide deaths (N=1,240) over 90 days following each visit. Potential predictors included 313 demographic and clinical characteristics extracted from records for up to 5 years before each visit: prior suicide attempts, mental health and substance use diagnoses, medical diagnoses, psychiatric medications dispensed, inpatient or emergency department care, and routinely administered depression questionnaires. Logistic regression models predicting suicide attempt and death were developed using penalized LASSO (least absolute shrinkage and selection operator) variable selection in a random sample of 65% of the visits and validated in the remaining 35%.ResultsMental health specialty visits with risk scores in the top 5% accounted for 43% of subsequent suicide attempts and 48% of suicide deaths. Of patients scoring in the top 5%, 5.4% attempted suicide and 0.26% died by suicide within 90 days. C-statistics (equivalent to area under the curve) for prediction of suicide attempt and suicide death were 0.851 (95% CI=0.848, 0.853) and 0.861 (95% CI=0.848, 0.875), respectively. Primary care visits with scores in the top 5% accounted for 48% of subsequent suicide attempts and 43% of suicide deaths. C-statistics for prediction of suicide attempt and suicide death were 0.853 (95% CI=0.849, 0.857) and 0.833 (95% CI=0.813, 0.853), respectively.ConclusionsPrediction models incorporating both health record data and responses to self-report questionnaires substantially outperform existing suicide risk prediction tools.

Dataset Information

Routinized categorization of suicide risk into actionable strata: Establishing the validity of an existing suicide risk assessment framework in an outpatient sample.

Objective

Method

Results

Conclusions

Publications

Routinized categorization of suicide risk into actionable strata: Establishing the validity of an existing suicide risk assessment framework in an outpatient sample.

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