Literature DB >> 31747488

A Comparison of Suicide Risk Scales in Predicting Repeat Suicide Attempt and Suicide: A Clinical Cohort Study.

Åsa U Lindh1,2, Marie Dahlin2, Karin Beckman2, Lotta Strömsten3, Jussi Jokinen3, Stefan Wiktorsson4, Ellinor Salander Renberg3, Margda Waern4, Bo Runeson2.   

Abstract

OBJECTIVE: To compare the predictive accuracy of the Suicide Intent Scale (SIS), the Suicide Assessment Scale (SUAS), the Karolinska Interpersonal Violence Scale (KIVS), and the Columbia-Suicide Severity Rating Scale (C-SSRS) for suicide attempts and suicides within 3 and 12 months of an episode of self-harm.
METHODS: This prospective multicenter cohort study included patients (N = 804) aged 18-95 years with a recent episode of self-harm assessed in psychiatric emergency settings from April 2012 to April 2016. Suicide attempts and suicides were identified in medical records and in the National Cause of Death Register. Receiver operating characteristic curves were constructed, and accuracy statistics were calculated. A sensitivity of at least 80% combined with a specificity of at least 50% were considered minimally acceptable.
RESULTS: At least 1 suicide attempt was recorded for 216 participants during follow-up, and 19 participants died by suicide. The SUAS and C-SSRS were better than chance in classifying the 114 suicide attempts occurring within the first 3 months; a C-SSRS score ≥ 27 yielded a sensitivity/specificity of 79.8%/51.5% (P < .001). During 1-year follow-up, the SUAS and C-SSRS also performed better than chance, but no cutoff on either instrument gave a sensitivity/specificity of ≥ 80%/≥ 50%. The SIS was the only instrument that could classify suicides correctly. At 3 months, the area under the curve (AUC) was 0.94 (95% CI, 0.89-0.99), and a score ≥ 21 predicted suicide with a sensitivity/specificity of 100%/81.9%, based on only 4 suicides. At 1-year follow-up, the AUC was 0.74 (95% CI, 0.61-0.87), and a score ≥ 17 predicted suicide with a sensitivity/specificity of 72.2%/57.9%.
CONCLUSIONS: Instruments that predicted nonfatal repeat suicide attempts did not predict suicide and vice versa. With the possible exception of the prediction of suicide by the SIS in a short time frame, the specificity of these instruments was low, giving them a limited relevance in the prediction of suicidal behaviors. © Copyright 2019 Physicians Postgraduate Press, Inc.

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Year:  2019        PMID: 31747488     DOI: 10.4088/JCP.18m12707

Source DB:  PubMed          Journal:  J Clin Psychiatry        ISSN: 0160-6689            Impact factor:   4.384


  5 in total

Review 1.  Suicide Risk Assessment and Prevention: Challenges and Opportunities.

Authors:  Eileen P Ryan; Maria A Oquendo
Journal:  Focus (Am Psychiatr Publ)       Date:  2020-04-23

2.  Investigating Predictive Factors of Suicidal Re-attempts in Adolescents and Young Adults After a First Suicide Attempt, a Prospective Cohort Study. Study Protocol of the SURAYA Project.

Authors:  Erika Abrial; Benoît Chalancon; Edouard Leaune; Jérôme Brunelin; Martine Wallon; Frédéric Moll; Nadine Barakat; Benoit Hoestlandt; Anthony Fourier; Louis Simon; Charline Magnin; Marianne Hermand; Emmanuel Poulet
Journal:  Front Psychiatry       Date:  2022-06-29       Impact factor: 5.435

3.  Clinical Differences Between Single and Multiple Suicide Attempters, Suicide Ideators, and Non-suicidal Inpatients.

Authors:  Isabella Berardelli; Alberto Forte; Marco Innamorati; Benedetta Imbastaro; Benedetta Montalbani; Salvatore Sarubbi; Gabriele Pasquale De Luca; Martina Mastrangelo; Gaia Anibaldi; Elena Rogante; David Lester; Denise Erbuto; Gianluca Serafini; Mario Amore; Maurizio Pompili
Journal:  Front Psychiatry       Date:  2020-12-15       Impact factor: 4.157

Review 4.  Artificial intelligence and suicide prevention: a systematic review.

Authors:  Alban Lejeune; Aziliz Le Glaz; Pierre-Antoine Perron; Johan Sebti; Enrique Baca-Garcia; Michel Walter; Christophe Lemey; Sofian Berrouiguet
Journal:  Eur Psychiatry       Date:  2022-02-15       Impact factor: 5.361

5.  Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis.

Authors:  Danielle Hopkins; Debra J Rickwood; David J Hallford; Clare Watsford
Journal:  Front Digit Health       Date:  2022-08-02
  5 in total

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