Research Center O3, Department of Psychiatry, Erasmus MC, Rotterdam, The Netherlands.
Early Interv Psychiatry. 2012 Nov;6(4):415-22. doi: 10.1111/j.1751-7893.2011.00330.x. Epub 2012 Jan 25.
The study aims to examine the predictive power of static and dynamic risk factors assessed at admission to an acute psychiatric ward and to develop a prediction model evaluating the risk of seclusion and restraint.
Over 20 months, data on demographic and clinical characteristics, psychosocial functioning, level of insight, uncooperativeness, and use of coercive measures were collected prospectively on 520 patients at admission. Logistic regression analysis was used to develop a prediction model. The magnitude of the predictive power of this model was estimated using receiver operating characteristic analysis.
The prediction model contained one static predictor (involuntary commitment) and two dynamic predictors (psychological impairment and uncooperativeness), with a high predictive power (receiver operating characteristic area under the curve = 0.83). The final risk model classified 72% of the patients correctly, with a higher sensitivity rate (80%) than specificity rate (71%).
Early assessment of patients' psychological impairment and uncooperativeness can help clinicians to recognize patients at risk for coercive measures and approach them on time with preventive and less restrictive interventions. Although this simple, highly predictive model accurately predicts the risk of seclusion or restraint, further validation studies are needed before it can be adopted into routine clinical practice.
本研究旨在检验入院时评估的静态和动态风险因素对精神科急症病房的预测能力，并开发一种评估约束和隔离风险的预测模型。
在 20 多个月的时间里，前瞻性地收集了 520 名入院患者的人口统计学和临床特征、社会心理功能、洞察力水平、不合作程度以及强制性措施使用情况的数据。使用逻辑回归分析来开发预测模型。使用接收者操作特征分析来估计该模型的预测能力大小。
该预测模型包含一个静态预测因子（非自愿承诺）和两个动态预测因子（心理障碍和不合作），具有较高的预测能力（接收者操作特征曲线下面积为 0.83）。最终的风险模型正确分类了 72%的患者，其敏感性（80%）高于特异性（71%）。
早期评估患者的心理障碍和不合作程度有助于临床医生识别有约束风险的患者，并及时采取预防和限制较少的干预措施。虽然这个简单的、高度预测性的模型可以准确预测约束或隔离的风险，但在将其应用于常规临床实践之前，还需要进一步的验证研究。
