The role of suicide ideation in assessing near-term suicide risk: A machine learning approach

McMullen, Lauren; Parghi, Neelang; Rogers, Megan L.; Yao, Heng; Bloch-Elkouby, Sarah; Galynker, Igor

doi:10.1016/j.psychres.2021.114118

Cited by 8 publications

(10 citation statements)

References 61 publications

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“…Twenty-one articles were included in the systematic review (see Table 3). Three pairs of studies refer to the same sample (Galynker et al, 2017;Li et al, 2018;McMullen et al, 2021;Parghi et al, 2021;Rogers, Vespa, et al, T A B L E 3 (Continued) ; five samples overlap, three referring to the sample used by Hawes et al (2017), and two referring to the sample used by Calati et al (2020); and in five studies, samples were taken from a larger, prospective study, as such there might be some overlap of the included samples (Bafna et al, 2022;Flint et al, 2021;Pia et al, 2020;; see Table 3).…”

Section: Resultsmentioning

confidence: 99%

“…Twenty‐one articles were included in the systematic review (see Table 3). Three pairs of studies refer to the same sample (Galynker et al., 2017; Li et al., 2018; McMullen et al., 2021; Parghi et al., 2021; Rogers, Vespa, et al., 2021; Rogers, Bloch‐Elkouby, & Galynker, 2022); five samples overlap, three referring to the sample used by Hawes et al. (2017), and two referring to the sample used by Calati et al.…”

Section: Resultsmentioning

confidence: 99%

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Suicide Crisis Syndrome: A systematic review

Melzer,

Forkmann,

Teismann

2024

Suicide & Life Threat Behav

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BackgroundThe objective of this systematic review is to describe the scientific evidence for the Suicide Crisis Syndrome (SCS), a presuicidal cognitive and affective state consisting of five symptomatic dimensions: entrapment, affective disturbance, loss of cognitive control, hyperarousal, and social withdrawal. The aim of this article is to summarize the emerging literature on the SCS and to assess the extent to which a uniform syndrome can be assumed.MethodsA systematic literature search was conducted in three different databases (PubMed, PsycInfo, and Google Scholar) using the search terms “Suicide Crisis Inventory,” “Suicide Crisis Syndrome,” “Narrative Crisis Model of Suicide,” and “Suicide Trigger State.”ResultsIn total, 37 articles from 2010 to 2022 were identified by search criteria. Twenty‐one articles published between 2017 and 2022 were included in the systematic review. All but three studies were conducted in the United States and examined clinical samples of adult high‐risk psychiatric in‐ and outpatients. Sample sizes ranged from N = 170 to 4846. The findings confirm the unidimensional structure of the proposed disorder and support the predictive validity for short‐term suicidal behavior above and beyond suicidal ideation.ConclusionDespite the promising predictive validity of the SCS, a precise prediction of future suicidal behavior remains difficult.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Suicide Crisis Syndrome: A systematic review

Melzer,

Forkmann,

Teismann

2024

Suicide & Life Threat Behav

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“…Cancer patients’ SI is a huge but largely preventable public health concern [ 41 ]. Identifying high risk patients is the first step to suicide prevention and also a recommended preventive strategy [ 42 , 43 ]. Only if patients with a higher probability of SI could be identified and then psychosocial interventions could be applied to them in advance.…”

Section: Discussionmentioning

confidence: 99%

Risk factor analysis and nomogram construction for predicting suicidal ideation in patients with cancer

et al. 2022

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Background Suicidal ideation in cancer patients is a critical challenge. At present, few studies focus on factors associated with suicidal ideation, and predictive models are still lacking. This study aimed at investigating the risk factors for suicidal ideation among cancer patients, and developed a predictive nomogram to screen high risk cancer patients for early prevention and intervention. Methods A questionnaire survey was conducted among cancer patients between May 2021 and January 2022. The factors associated with suicidal ideation were used to construct a multivariate logistic regression model, which was visualized as a predictive nomogram to evaluate the risk of suicidal ideation. Areas under the curve, calibration plot, decision curve analysis, and internal and external validation were used to validate the discrimination, calibration and clinical usefulness of the model. Results A total of 820 patients with cancer were recruited for this study and 213 (25.98%) developed suicidal ideation. Levels of demoralization, depression and cancer staging, marital status, residence, medical financial burden, and living condition were influence factors for suicidal ideation. Comparing nomogram with Self-rating Idea of Suicide Scale (SIOSS), the nomogram had a satisfactory discrimination ability with an AUC of 0.859 (95% CI: 0.827–0.890) and 0.818 (95% CI: 0.764–0.873) in the training and validation sets, respectively. The calibration plot and decision curve analysis revealed that this nomogram was in good fitness and could be beneficial in clinical applications. Conclusions Suicidal ideation is common in cancer patients. Levels of demoralization, depression and cancer staging were independent predictors of suicidal ideation. The nomogram is an effective and simple tool for predictive suicidal ideation in cancer patients.

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“…In previous work, ML analysis was successfully applied to psychometric measures [35,47]. Predictive validity of the SCI was analyzed using logistic regression (LR), random forest (RM), and gradient boosting (GB) algorithms.…”

Section: Machine Learning and Shapley Additive Explanation (Shap) Ana...mentioning

confidence: 99%

Machine learning approach for the development of a crucial tool in suicide prevention: The Suicide Crisis Inventory-2 (SCI-2) Short Form

De Luca,

Parghi,

El Hayek

et al. 2024

PLoS ONE

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The Suicide Crisis Syndrome (SCS) describes a suicidal mental state marked by entrapment, affective disturbance, loss of cognitive control, hyperarousal, and social withdrawal that has predictive capacity for near-term suicidal behavior. The Suicide Crisis Inventory-2 (SCI-2), a reliable clinical tool that assesses SCS, lacks a short form for use in clinical settings which we sought to address with statistical analysis. To address this need, a community sample of 10,357 participants responded to an anonymous survey after which predictive performance for suicidal ideation (SI) and SI with preparatory behavior (SI-P) was measured using logistic regression, random forest, and gradient boosting algorithms. Four-fold cross-validation was used to split the dataset in 1,000 iterations. We compared rankings to the SCI–Short Form to inform the short form of the SCI-2. Logistic regression performed best in every analysis. The SI results were used to build the SCI-2-Short Form (SCI-2-SF) utilizing the two top ranking items from each SCS criterion. SHAP analysis of the SCI-2 resulted in meaningful rankings of its items. The SCI-2-SF, derived from these rankings, will be tested for predictive validity and utility in future studies.

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The role of suicide ideation in assessing near-term suicide risk: A machine learning approach

Cited by 8 publications

References 61 publications

Suicide Crisis Syndrome: A systematic review

Suicide Crisis Syndrome: A systematic review

Risk factor analysis and nomogram construction for predicting suicidal ideation in patients with cancer

Machine learning approach for the development of a crucial tool in suicide prevention: The Suicide Crisis Inventory-2 (SCI-2) Short Form

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