A validation study of PHQ-9 suicide item with the Columbia Suicide Severity Rating Scale in outpatients with mood disorders at National Network of Depression Centers

Chung, Tong Han; Hanley, Kathleen Weiss; Le, Yen-Chi L.; Merchant, Alisha; Nascimento, Flavio; Figueiredo, Juliana Mendonca De; Wilcox, Holly C.; Coryell, William; Soares, Jair C.; Selvaraj, Sudhakar

doi:10.1016/j.jad.2022.09.131

Cited by 13 publications

(12 citation statements)

References 21 publications

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“…Participant response more than half the days or nearly every day was considered to reflect a positive response regarding possible suicidal ideation. This item has been found to be acceptable validation in various populations ( Louzon et al, 2016 ; Th et al, 2023 ).…”

Section: Methodsmentioning

confidence: 93%

Moral injury and suicidal ideation among health professionals: The role of mental health

Lei

et al. 2023

Journal of Affective Disorders

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Section: Methodsmentioning

confidence: 93%

Moral injury and suicidal ideation among health professionals: The role of mental health

Lei

et al. 2023

Journal of Affective Disorders

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“…Additionally, recent literature suggests these instruments may have inadequate predictive power in identifying who will later die by suicide. 22,23 Therefore, additional approaches should be developed and tested to expand the options for medical and mental health clinics to improve risk response decision-making.…”

Section: Discussionmentioning

confidence: 99%

User Feedback on the Use of a Natural Language Processing Application to Screen for Suicide Risk in the Emergency Department

Pease

Thompson

Wright-Berryman

et al. 2023

J Behav Health Serv Res

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Suicide is the 10th leading cause of death in the USA and globally. Despite decades of research, the ability to predict who will die by suicide is still no better than 50%. Traditional screening instruments have helped identify risk factors for suicide, but they have not provided accurate predictive power for reducing death rates. Over the past decade, natural language processing (NLP), a form of machine learning (ML), has been used to identify suicide risk by analyzing language data. Recent work has demonstrated the successful integration of a suicide risk screening interview to collect language data for NLP analysis from patients in two emergency departments (ED) of a large healthcare system. Results indicated that ML/NLP models performed well identifying patients that came to the ED for suicide risk. However, little is known about the clinician’s perspective of how a qualitative brief interview suicide risk screening tool to collect language data for NLP integrates into an ED workflow. This report highlights the feedback and observations of patient experiences obtained from clinicians using brief suicide screening interviews. The investigator used an open-ended, narrative interview approach to inquire about the qualitative interview process. Three overarching themes were identified: behavioral health workflow, clinical implications of interview probes, and integration of an application into provider patient experience. Results suggest a brief, qualitative interview method was feasible, person-centered, and useful as a suicide risk detection approach.

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“…A final limitation is our reliance on item 9 of the PHQ‐9. While item 9 is predictive of suicide risk and is a brief and efficient screen in primary care settings—an important consideration given our focus on CoCM—some studies suggest that other screening tools, such as the Columbia Suicide Severity Rating Scale, are more accurate (Chung et al., 2023; Na et al., 2018). To enhance future research on treating suicidality in CoCM, item 9 of the PHQ‐9 could be augmented with a more comprehensive suicide risk assessment.…”

Section: Discussionmentioning

confidence: 99%

Treating individuals with suicidal ideation in primary care: Patient‐level characteristics associated with follow‐up in the Collaborative Care Model

Candon,

Wolk,

Kattan Khazanov

et al. 2023

Suicide & Life Threat Behav

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IntroductionThe Collaborative Care Model (CoCM) is an evidence‐based approach which embeds behavioral health providers (BHPs) into primary care. Whether patients with suicidal ideation (SI) are willing to engage in CoCM is unclear.MethodsUsing Patient Health Questionnaire‐9 (PHQ‐9) administrative data from primary care practices within an urban academic health system, we identified patients with and without SI who were referred to a CoCM BHP. We compared engagement, defined as attendance at ≥1 CoCM visit, across groups.ResultsBetween 2018 and 2022, 7391 primary care patients were referred to a CoCM BHP. Eight hundred and ninety‐two of these patients reported SI on the PHQ‐9 (754 on “several days” during the previous 2 weeks and 138 on “more than half or most days”). Across groups, most patients engaged in CoCM. Patients reporting SI on several days engaged at a lower rate (61.4%) than those reporting SI on more than half or most days (65.9%). Both SI groups engaged at a lower rate than the 6499 patients who did not report SI (67.5%).ConclusionMost patients referred to a CoCM BHP engaged in ≥1 visit. Rates were lower for patients with SI, with the lowest rate among those reporting SI on several days.

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A validation study of PHQ-9 suicide item with the Columbia Suicide Severity Rating Scale in outpatients with mood disorders at National Network of Depression Centers

Cited by 13 publications

References 21 publications

Moral injury and suicidal ideation among health professionals: The role of mental health

Moral injury and suicidal ideation among health professionals: The role of mental health

User Feedback on the Use of a Natural Language Processing Application to Screen for Suicide Risk in the Emergency Department

Treating individuals with suicidal ideation in primary care: Patient‐level characteristics associated with follow‐up in the Collaborative Care Model

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