Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Oxford Mental Illness and Suicide tool (OxMIS) is a standardised, scalable, and transparent instrument for suicide risk assessment in people with severe mental illness (SMI) based on 17 sociodemographic, criminal history, familial, and clinical risk factors. However, alongside most prediction models in psychiatry, external validations are currently lacking. We utilised a Finnish population sample of all persons diagnosed by mental health services with SMI (schizophrenia-spectrum and bipolar disorders) between 1996 and 2017 (n = 137,112). To evaluate the performance of OxMIS, we initially calculated the predicted 12-month suicide risk for each individual by weighting risk factors by effect sizes reported in the original OxMIS prediction model and converted to a probability. This probability was then used to assess the discrimination and calibration of the OxMIS model in this external sample. Within a year of assessment, 1.1% of people with SMI (n = 1475) had died by suicide. The overall discrimination of the tool was good, with an area under the curve of 0.70 (95% confidence interval: 0.69–0.71). The model initially overestimated suicide risks in those with elevated predicted risks of >5% over 12 months (Harrell’s Emax = 0.114), which applied to 1.3% (n = 1780) of the cohort. However, when we used a 5% maximum predicted suicide risk threshold as is recommended clinically, the calibration was excellent (ICI = 0.002; Emax = 0.005). Validating clinical prediction tools using routinely collected data can address research gaps in prediction psychiatry and is a necessary step to translating such models into clinical practice.

Original publication




Journal article


Translational Psychiatry


Springer Nature

Publication Date