BMJ Mental Health (Oct 2023)

Risk of death by suicide following self-harm presentations to healthcare: development and validation of a multivariable clinical prediction rule (OxSATS)

  • Thomas R Fanshawe,
  • Henrik Larsson,
  • Paul Lichtenstein,
  • Yasmina Molero,
  • Brian M D’Onofrio,
  • Michael Sharpe,
  • Jane Walker,
  • Bo Runeson,
  • Maria D L A Vazquez-Montes

DOI
https://doi.org/10.1136/bmjment-2023-300673
Journal volume & issue
Vol. 26, no. 1

Abstract

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Background Assessment of suicide risk in individuals who have self-harmed is common in emergency departments, but is often based on tools developed for other purposes.Objective We developed and validated a predictive model for suicide following self-harm.Methods We used data from Swedish population-based registers. A cohort of 53 172 individuals aged 10+ years, with healthcare episodes of self-harm, was split into development (37 523 individuals, of whom 391 died from suicide within 12 months) and validation (15 649 individuals, 178 suicides within 12 months) samples. We fitted a multivariable accelerated failure time model for the association between risk factors and time to suicide. The final model contains 11 factors: age, sex, and variables related to substance misuse, mental health and treatment, and history of self-harm. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis guidelines were followed for the design and reporting of this work.Findings An 11-item risk model to predict suicide was developed using sociodemographic and clinical risk factors, and showed good discrimination (c-index 0.77, 95% CI 0.75 to 0.78) and calibration in external validation. For risk of suicide within 12 months, using a 1% cut-off, sensitivity was 82% (75% to 87%) and specificity was 54% (53% to 55%). A web-based risk calculator is available (Oxford Suicide Assessment Tool for Self-harm or OxSATS).Conclusions OxSATS accurately predicts 12-month risk of suicide. Further validations and linkage to effective interventions are required to examine clinical utility.Clinical implications Using a clinical prediction score may assist clinical decision-making and resource allocation.