Examining suicide assessment measures for research use: Using item response theory to optimize psychometric assessment for research on suicidal ideation in major depressive disorder

McCall, W. Vaughn; Porter, Ben; Pate, Ashley R; Bolstad, Courtney J.; Drapeau, Christopher W; Krystal, Andrew D.; Benca, Ruth M.; Rumble, Meredith E.; Nadorff, Michael R.

doi:10.1111/sltb.12791

Cited by 16 publications

(11 citation statements)

References 33 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The C-SSRS is a measure used to identify and assess individuals at risk for suicide, with a special focus on ascertaining levels of recent and lifetime ideation and behavior. Evidence around its psychometric properties has been mixed ( 54 – 58 ). Twelve studies located in the current review tested or implemented a version of the Columbia Suicide Severity Rating Scale (C-SSRS).…”

Section: Resultsmentioning

confidence: 99%

“…The C-SSRS is a measure used to identify and assess individuals at risk for suicide, with a special focus on ascertaining levels of recent and lifetime ideation and behavior. Evidence around its psychometric properties has been mixed (54)(55)(56)(57)(58) (31)] is up to seven items in length. Patients are asked in the past month "have you wished you were dead or wished you could go to sleep and not wake up" (Yes/No) or "actually had any thoughts of killing yourself?"…”

Section: Columbia-suicide Severity Rating Scale (Cssrs)mentioning

confidence: 99%

See 1 more Smart Citation

Suicide Screening Tools for Pediatric Emergency Department Patients: A Systematic Review

Scudder

Rosin²,

Nelson³

et al. 2022

Front. Psychiatry

View full text Add to dashboard Cite

BackgroundAccording to the Centers for Disease Control and Prevention, suicidality and suicidal behavior among youth continues to increase significantly each year. Many of those who die by suicide interact with health services in the year before death. This systematic review sought to identify and describe empirically tested screening tools for suicidality in youth presenting to Emergency Departments (ED).Objective(1) To identify and compare existing tools used to screen for suicidality in children and adolescents who present to the ED and (2) to ascertain the prevalence of suicidality in pediatric populations found with these tools.MethodsWe searched Ovid Medline, CINAHL, Scopus, and Cochrane databases for primary research studies that identified and evaluated screening tools for suicide risk in pediatric ED patients. A total of 7,597 publications published before August 25, 2021 met search criteria and were screened by two independent reviewers based on our inclusion and exclusion criteria, with any conflicts resolved via consensus meetings or an independent reviewer. A total of 110 papers were selected for full text review, of which 67 were excluded upon further inspection. Covidence was used to extract and synthesize results.Results43 articles were eligible for inclusion. Most studies (n = 33) took place in general pediatric EDs; the quality was generally high. Patients ranged from 4-24 years old, with most screening tested in patients 12 years and older. The most researched tools were the Ask-Suicide Screening Questions (ASQ) (n = 15), Columbia-Suicide Severity Rating Scale (C-SSRS) (n = 12), Suicidal Ideation Questionnaire (SIQ) (n = 11), and the Risk of Suicide Questionnaire (RSQ) (n = 7). Where screening was applied to all patients, about one-fifth of pediatric ED patients screened positive; where suicide screening was applied to psychiatric patients only, over half screened positive. Positive screens were more likely to be female and older than negative screens and they were more likely to be assessed and admitted.ConclusionSeveral validated screening tools exist for the purpose of screening pediatric populations in EDs for suicidality. Such tools may help to support early detection and appropriate intervention for youth at risk of suicide.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=276328, identifier: 276328

show abstract

Section: Resultsmentioning

confidence: 99%

“…The C-SSRS is a measure used to identify and assess individuals at risk for suicide, with a special focus on ascertaining levels of recent and lifetime ideation and behavior. Evidence around its psychometric properties has been mixed (54)(55)(56)(57)(58) (31)] is up to seven items in length. Patients are asked in the past month "have you wished you were dead or wished you could go to sleep and not wake up" (Yes/No) or "actually had any thoughts of killing yourself?"…”

Section: Columbia-suicide Severity Rating Scale (Cssrs)mentioning

confidence: 99%

Suicide Screening Tools for Pediatric Emergency Department Patients: A Systematic Review

Scudder

Rosin²,

Nelson³

et al. 2022

Front. Psychiatry

View full text Add to dashboard Cite

show abstract

“…Regarding the SI assessment, we relied on SSI measure because it is part of the clinical evaluation and management of inpatients in the mood disorder unit. Recently, McCall et al (2021) showed that the most common disagreement of dichotomized SI evaluation occurred when SSI was administered compared to other two measures. However, the authors stated that all measures available were equally able to identify low and high levels of SI, and the choice of the SI instrument may be related to time and contextual variables.…”

Section: Discussionmentioning

confidence: 99%

The role of clinical and Personological features in predicting high lethality suicide attempts: A study among mood disorder patients

Borroni

Franchini

Brioschi

et al. 2022

Personality & Mental Health

View full text Add to dashboard Cite

Suicidal attempts (SA) represent heterogeneous behaviours ranging in their seriousness from fatal and near‐fatal (high‐lethality) cases to those that do not require medical attention (low lethality). These considerations stress the need to identify high‐risk individuals for high lethality SA in order to target suicide preventive interventions. The present study aims at evaluating the role of sociodemographic and clinical variables and examining personality pathological features in predicting high lethality SA. The sample was composed by 94 patients who were consecutively admitted to the Mood Disorders Unit of the San Raffaele Turro Hospital in Milan. The results of binary logistic regression analyses showed that previous SA and current suicide ideation play a role in predicting serious SA. Considering the DSM‐5 personality dysfunctional domains assessed by the Personality Inventory for DSM‐5, our logistic regression analyses suggested that high lethality SA was associated with Detachment PID‐5 domain. Finally, binary hierarchical regression analysis showed that Detachment domain remained a significant predictor of serious SA over and above the effect of previous SA and suicide ideation. As a whole, our results highlight the importance of a multidimensional approach to develop adequate assessment, effective treatments and prevention of high lethality SA risk.

show abstract

“…Among them, the Columbia-Suicide Severity Rating Scale (C-SSRS) is considered a gold standard for the clinical field ( 10 , 11 ). However, the gold standard for suicide research remains unclear ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

Detection of Depression and Suicide Risk Based on Text From Clinical Interviews Using Machine Learning: Possibility of a New Objective Diagnostic Marker

et al. 2022

View full text Add to dashboard Cite

BackgroundDepression and suicide are critical social problems worldwide, but tools to objectively diagnose them are lacking. Therefore, this study aimed to diagnose depression through machine learning and determine whether it is possible to identify groups at high risk of suicide through words spoken by the participants in a semi-structured interview.MethodsA total of 83 healthy and 83 depressed patients were recruited. All participants were recorded during the Mini-International Neuropsychiatric Interview. Through the suicide risk assessment from the interview items, participants with depression were classified into high-suicide-risk (31 participants) and low-suicide-risk (52 participants) groups. The recording was transcribed into text after only the words uttered by the participant were extracted. In addition, all participants were evaluated for depression, anxiety, suicidal ideation, and impulsivity. The chi-square test and student’s T-test were used to compare clinical variables, and the Naive Bayes classifier was used for the machine learning text model.ResultsA total of 21,376 words were extracted from all participants and the model for diagnosing patients with depression based on this text confirmed an area under the curve (AUC) of 0.905, a sensitivity of 0.699, and a specificity of 0.964. In the model that distinguished the two groups using statistically significant demographic variables, the AUC was only 0.761. The DeLong test result (p-value 0.001) confirmed that the text-based classification was superior to the demographic model. When predicting the high-suicide-risk group, the demographics-based AUC was 0.499, while the text-based one was 0.632. However, the AUC of the ensemble model incorporating demographic variables was 0.800.ConclusionThe possibility of diagnosing depression using interview text was confirmed; regarding suicide risk, the diagnosis accuracy increased when demographic variables were incorporated. Therefore, participants’ words during an interview show significant potential as an objective and diagnostic marker through machine learning.

show abstract

Examining suicide assessment measures for research use: Using item response theory to optimize psychometric assessment for research on suicidal ideation in major depressive disorder

Cited by 16 publications

References 33 publications

Suicide Screening Tools for Pediatric Emergency Department Patients: A Systematic Review

Suicide Screening Tools for Pediatric Emergency Department Patients: A Systematic Review

The role of clinical and Personological features in predicting high lethality suicide attempts: A study among mood disorder patients

Detection of Depression and Suicide Risk Based on Text From Clinical Interviews Using Machine Learning: Possibility of a New Objective Diagnostic Marker

Contact Info

Product

Resources

About