Department of Intensive Care Medicine, St Elisabeth Hospital, , Tilburg, The Netherlands.
J Neurol Neurosurg Psychiatry. 2014 Apr;85(4):431-4. doi: 10.1136/jnnp-2013-304920. Epub 2013 Jun 6.
We developed and validated a risk score to predict delirium after stroke which was derived from our prospective cohort study where several risk factors were identified.
Using the β coefficients from the logistic regression model, we allocated a score to values of the risk factors. In the first model, stroke severity, stroke subtype, infection, stroke localisation, pre-existent cognitive decline and age were included. The second model included age, stroke severity, stroke subtype and infection. A third model only included age and stroke severity. The risk score was validated in an independent dataset.
The area under the curve (AUC) of the first model was 0.85 (sensitivity 86%, specificity 74%). In the second model, the AUC was 0.84 (sensitivity 80%, specificity 75%). The third model had an AUC of 0.80 (sensitivity 79%, specificity 73%). In the validation set, model 1 had an AUC of 0.83 (sensitivity 78%, specificity 77%). The second had an AUC of 0.83 (sensitivity 76%, specificity 81%). The third model gave an AUC of 0.82 (sensitivity of 73%, specificity 75%). We conclude that model 2 is easy to use in clinical practice and slightly better than model 3 and, therefore, was used to create risk tables to use as a tool in clinical practice.
A model including age, stroke severity, stroke subtype and infection can be used to identify patients who have a high risk to develop delirium in the early phase of stroke.
我们开发并验证了一种预测中风后谵妄的风险评分，该评分源自我们的前瞻性队列研究，其中确定了几个风险因素。
使用逻辑回归模型的β系数，我们为风险因素的值分配了一个分数。在第一个模型中，纳入了中风严重程度、中风亚型、感染、中风部位、预先存在的认知减退和年龄。第二个模型包括年龄、中风严重程度、中风亚型和感染。第三个模型仅包括年龄和中风严重程度。该风险评分在独立数据集上得到了验证。
第一个模型的曲线下面积（AUC）为 0.85（敏感性 86%，特异性 74%）。在第二个模型中，AUC 为 0.84（敏感性 80%，特异性 75%）。第三个模型的 AUC 为 0.80（敏感性 79%，特异性 73%）。在验证集中，模型 1 的 AUC 为 0.83（敏感性 78%，特异性 77%）。第二个模型的 AUC 为 0.83（敏感性 76%，特异性 81%）。第三个模型的 AUC 为 0.82（敏感性 73%，特异性 75%）。我们得出结论，模型 2 在临床实践中易于使用，略优于模型 3，因此被用于创建风险表，作为临床实践中的工具。
包括年龄、中风严重程度、中风亚型和感染在内的模型可用于识别中风早期发生谵妄风险较高的患者。
