Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Transl Psychiatry. 2019 Feb 25;9(1):98. doi: 10.1038/s41398-019-0428-3.
Assessment of suicide risk in individuals with severe mental illness is currently inconsistent, and based on clinical decision-making with or without 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] 47-63%) and specificity was 75% (95% CI 74-75%). 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.
目前，对患有严重精神疾病个体的自杀风险评估不一致，其依据是基于临床决策，无论是否使用专为其他目的开发的工具。我们旨在使用来自瑞典基于人群的登记处的个人数据，开发和验证用于自杀预测的模型。该队列包括 75158 名年龄在 15-65 岁之间患有严重精神疾病（精神分裂症谱系障碍和双相情感障碍）的个体，在 2001 年至 2008 年期间，他们有 574018 例临床患者发作，分为开发（58771 名患者，494 例自杀）和外部验证（16387 名患者，139 例自杀）样本。建立了一个多变量推导模型来确定预先指定的常规收集的社会人口统计学和临床风险因素的强度，然后在外部验证中进行测试。我们使用指定的风险截止值来衡量预测 1 年内自杀的区分度和校准度。开发了一个包含 17 个项目的临床自杀风险预测模型，该模型显示出适度良好的区分度（c 指数为 0.71）和校准度。对于 1 年内自杀的风险，使用预先指定的 1%截止值，灵敏度为 55%（95%置信区间 [CI]：47-63%），特异性为 75%（95% CI：74-75%）。阳性和阴性预测值分别为 2%和 99%。该模型用于生成一个简单的免费基于网络的基于概率的风险计算器（牛津精神疾病和自杀工具或 OxMIS），不使用分类截止值。对患有严重精神疾病的个体进行自杀的可扩展预测评分是可行的。如果在其他样本中得到验证并与有效的干预措施相结合，使用概率评分可能有助于临床决策。
