Risk of repeat self-harm among individuals presenting to healthcare services: development and validation of a clinical risk assessment model (OxSET)

Background: A self-harm episode is a major risk factor for repeat self-harm. Existing tools to assess and predict repeat self-harm have major methodological limitations, and few are externally validated.

Objective: To develop and validate a risk assessment model of repeat self-harm up to 6 months after an episode of non-fatal self-harm that resulted in an emergency visit to hospital or specialised care.

Methods: Using Swedish national registers, we identified 53 172 people aged≥10 years who self-harmed during 2008-2012. We allocated 37 523 individuals to development (2820 or 7.5% repeat self-harm incidents within 6 months) and 15 649 to geographic validation (1373 repeat episodes) samples, based on region of residence. In a temporal validation of people who self-harmed during 2018-2019, we identified 25 036 individuals (2886 repeat episodes). We fitted a multivariable accelerated failure time model to predict risk of repeat self-harm.

Findings: In the external validations (n=40 685), rates of repeat self-harm were 8.8%-11.5% over 6 months. The final model retained 17 factors. Calibration and discrimination were similar in both validation samples, with observed-to-expected ratio=1.15 (95% CI=1.09 to 1.21) and c-statistic=0.72 (95% CI=0.70 to 0.73) in the geographical validation. At 6 months and a 10% risk cut-off, sensitivity was 51.5% (95% CI=48.8% to 54.2%) and specificity was 80.7% (95% CI=80.1% to 81.4%) in geographic validation; corresponding values were 56.9% (95% CI=55.1% to 58.7%) and 76.0% (95% CI=75.5% to 76.6%) in temporal validation. Discrimination was slightly worse at the 1-month prediction horizon (c-statistics of 0.66-0.68).

Conclusions: Using mostly routinely collected data, simple risk assessment models and tools can provide acceptable levels of accuracy for repeat of self-harm.

Clinical implications: This risk model (OXford SElf-harm repeat tool) may assist clinical decision-making.