New Risk Model Could Improve Safety for Addiction Patients in Crisis
Swedish researchers developed the first predictive tool designed specifically for people with substance use disorders to identify those at highest risk of self-harm. The finding matters because current clinical assessments rely on general population data, potentially missing critical warning signs in this vulnerable group—a gap that healthcare systems and insurers can now address with targeted interventions.
Originaltitel: Self‐harm in individuals with substance use disorders: Predictive factors and risk model
BACKGROUND AND AIMS: Substance use disorders are associated with an elevated risk of self-harm. Currently, clinical and structured assessment of self-harm risk typically relies on evidence from the general population samples. The aim of this study was to develop a risk model for self-harm that incorporates predictors specific to individuals with substance use disorders. METHODS: Using national registers, we identified a population-based cohort of 449 720 individuals with substance use disorders in Sweden between 2006 and 2020. We tested independence and strength of a range of socio-demographic and clinical factors, obtained through linkage of population-based registers, with a Cox proportional hazards model, and estimated the risk of self-harm. For the risk model, 361 120 individuals were allocated to the development sample and 88 600 to external validation based on different geographical regions. We assessed self-harm risk over five predetermined follow-up periods-within 7 days, 1 month, 3 months, 6 months and 12 months-following a healthcare contact for substance use disorders. RESULTS: In the development sample, self-harm rates ranged from 0.6% to 3.5%, and in the validation sample from 0.5% to 3.6%. Ten risk factors were retained in the final risk model. Strongest associations with subsequent self-harm were for clinical factors: previous self-harm [hazard ratio (HR) = 3.17, 95% confidence interval (CI) = 3.08-3.26] and comorbidity of mental disorders (HR = 2.63, 95% CI = 2.50-2.72). Recent psychotropic medication use, including antidepressant (HR = 1.29, 95% CI = 1.23-1.38) and antipsychotic treatments (HR = 1.34, 95% CI = 1.24-1.44), was associated with increased risk, even after adjusting for psychiatric comorbidity, likely reflecting greater clinical severity and complexity. Across follow-up periods, performance was good in terms of discrimination, with area under the curve (AUCs) ranging from 0.73 (95% CI = 0.71-0.76) to 0.79 (95% CI = 0.78-0.80). In relation to calibration, expected-to-observed risk ratios were 1.00 to 1.04 and Brier scores 0.01 to 0.04 across follow-up periods. We used the model to generate a simple web-based risk calculator [Oxford Self-hArM after substance use disorders (OxSAMS)]. CONCLUSIONS: Modifiable clinical factors appear to have the strongest associations with increased risk of self-harm in people with substance use disorders. Structured tools, taking account of the different strengths of those factors, could inform clinical decision-making and provide a baseline assessment for training and research.