Negatsch Vincent, Voulgaris Alexander, Seidel Peter, Roehle Robert, Opitz-Welke Annette
Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Forensic Psychiatry, Berlin, Germany.
Universitätsklinikum Hamburg-Eppendorf, Institute of Sexual Medicine and Forensic Psychiatry, Hamburg, Germany.
Front Psychiatry. 2019 Apr 23;10:264. doi: 10.3389/fpsyt.2019.00264. eCollection 2019.
Although there is evidence that individuals who suffer from severe mental disorders are at higher risk for aggressive behavior, only a minority eventually become violent. In 2017, Fazel et al. developed a risk calculator (Oxford Mental Illness and Violence tool, OxMIV) to identify the risk of violent crime in patients with mental disorders. For the first time, we tested the predictive validity of the OxMIV in the department of psychiatry at the prison hospital in Berlin, Germany, and presented findings from our internal validation. We designed a cohort study with 474 patients aged 16-65 years old who met the inclusion criteria of schizophrenia-spectrum or bipolar disorder and classified the patients into two groups: a violent group with 191 patients and a nonviolent group with 283 patients. Violence was defined as the aggressive behavior of a patient with the necessity of special observation. We obtained all the required information retrospectively through patient files, applied the OxMIV tool on each subject, and compared the results of both groups. Sensitivity, specificity, and positive/negative predictive values were determined. We used logistic regression including variable selection and internal validation to identify relevant predictors of aggressive behavior in our cohort. The OxMIV score was significantly higher in the violent group [median 4.21%; Interquartile range (IQR) 8.51%] compared to the nonviolent group (median 1.77%; IQR 2.01%; p < 0.0001). For the risk of violent behavior, using the 5% cutoff for "increased risk," the sensitivity was 44%, and the specificity was 89%, with a positive predictive value of 72% and a negative predictive value of 70%. Applying logistic regression, four items were statistically significant in predicting violent behavior: previous violent crime (adjusted odds ratio 5.29 [95% CI 3.10-9.05], p < 0.0001), previous drug abuse (1.80 [1.08-3.02], p = 0.025), and previous alcohol abuse (1.89 [1.21-2.95], p = 0.005). The item recent antidepressant treatment (0.28 [0.17-0.47]. p < 0.0001) had a statistically significant risk reduction effect. In our opinion, the risk assessment tool OxMIV succeeded in predicting violent behavior in imprisoned psychiatric patients. As a result, it may be applicable for identification of patients with special needs in a prison environment and, thus, improving prison safety.
尽管有证据表明患有严重精神障碍的个体出现攻击行为的风险更高，但最终只有少数人会变得暴力。2017年，法泽尔等人开发了一种风险计算器（牛津精神疾病与暴力工具，OxMIV），以识别精神障碍患者暴力犯罪的风险。我们首次在德国柏林监狱医院的精神科测试了OxMIV的预测效度，并展示了我们内部验证的结果。我们设计了一项队列研究，纳入了474名年龄在16至65岁之间、符合精神分裂症谱系或双相情感障碍纳入标准的患者，并将患者分为两组：191名患者的暴力组和283名患者的非暴力组。暴力被定义为患者需要特别观察的攻击行为。我们通过患者档案回顾性地获取了所有所需信息，对每个受试者应用OxMIV工具，并比较了两组的结果。确定了敏感性、特异性以及阳性/阴性预测值。我们使用包括变量选择和内部验证的逻辑回归来识别我们队列中攻击行为的相关预测因素。与非暴力组（中位数1.77%；四分位间距[IQR]2.01%；p<0.0001）相比，暴力组的OxMIV评分显著更高（中位数4.21%；四分位间距[IQR]8.51%）。对于暴力行为的风险，使用“风险增加”的5%临界值，敏感性为44%，特异性为89%，阳性预测值为72%，阴性预测值为70%。应用逻辑回归，有四项在预测暴力行为方面具有统计学意义：既往暴力犯罪（调整后的优势比5.29[95%CI 3.10 - 9.05]，p<0.0001）、既往药物滥用（1.80[1.08 - 3.02]，p = 0.025）以及既往酒精滥用（1.89[1.21 - 2.95]，p = 0.005）。近期抗抑郁治疗这一项（0.28[0.17 - 0.47]，p<0.0001）具有统计学意义的风险降低效果。在我们看来，风险评估工具OxMIV成功地预测了被监禁精神科患者的暴力行为。因此，它可能适用于识别监狱环境中有特殊需求的患者，从而提高监狱安全性。
