The prediction of suicide in severe mental illness: development and validation of a clinical prediction rule (OxMIS)
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Colleges, School and Institutes
Assessment of suicide risk in individuals with severe mental illness is currently inconsistent, and based on tools developed for other purposes. We aimed to develop and validate a predictive model for suicide using data from linked population-based registers in individuals with severe mental illness. A national cohort of 75 158 Swedish individuals aged 15-65 with a diagnosis of severe mental illness (schizophrenia-spectrum disorders, and bipolar disorder) with 574 018 clinical patient episodes between 2001 and 2008, split into development (58 771 patients, 494 suicides) and external validation (16 387 patients, 139 suicides) samples. A multivariable derivation model was developed to determine the strength of pre-specified routinely collected socio-demographic and clinical risk factors, and then tested in external validation. We measured discrimination and calibration for prediction of suicide at 1 year using specified risk cut-offs. A 17-item clinical risk prediction model for suicide was developed and showed moderately good measures of discrimination (c-index 0.71) and calibration. For risk of suicide at 1 year, using a pre-specified 1% cut-off, sensitivity was 55% (95% confidence interval [CI] 47% to 63%) and specificity was 75% (95% CI 74% to 75%). Positive and negative predictive values were 2% and 99%, respectively. The model was used to generate a simple freely available web-based probability-based risk calculator (Oxford Mental Illness and Suicide tool or OxMIS) without categorical cut-offs. A scalable prediction score for suicide in individuals with severe mental illness is feasible. If validated in other samples and linked to effective interventions, using a probability score may assist clinical decision-making.
|Number of pages||10|
|Publication status||Published - 25 Feb 2019|