Drinovac Mihael, Wagner Helga, Agrawal Niruj, Cock Hannah R, Mitchell Alex J, von Oertzen Tim J
Institute of Applied Statistics, Johannes Kepler University, Linz, Austria.
Department of Neuropsychiatry, St George's Hospital, London, UK; Epilepsy Group, Atkinson Morley Regional Neurosciences Centre, St George's Hospital, London, UK; St George's University of London, London, UK.
Epilepsy Behav. 2015 Mar;44:67-72. doi: 10.1016/j.yebeh.2014.12.014. Epub 2015 Jan 24.
Depression is common but frequently underdiagnosed in people with epilepsy. Screening tools help to identify depression in an outpatient setting. We have published validation of the NDDI-E and Emotional Thermometers (ET) as screening tools for depression (Rampling et al., 2012). In the current study, we describe a model of an optimized screening tool with higher accuracy.
Data from 250 consecutive patients in a busy UK outpatient epilepsy clinic were prospectively collected. Logistic regression models and recursive partitioning techniques (classification trees, random forests) were applied to identify an optimal subset from 13 items (NDDI-E and ET) and provide a framework for the prediction of class membership probabilities for the DSM-IV-based depression classification.
Both logistic regression models and classification trees (random forests) suggested the same choice of items for classification (NDDI-E item 4, NDDI-E item 5, ET-Distress, ET-Anxiety, ET-Depression). The most useful regression model includes all 5 mentioned variables and outperforms the NDDI-E as well as the ET with respect to AUC (NDDI-E: 0.903; ET7: 0.889; logistic regression: 0.943). A model developed using random forests, grown by restricting the possible splitting of variables to these 5 items using only subsets of the original data for single classification, performed similarly (AUC: 0.949).
For the first time, we have created a model of a screening tool for depression containing both verbal and visual analog scales, with characteristics supporting that this will be more precise than previous tools. Collection of a new data sample to assess out-of-sample performance is necessary for confirmation of the predictive performance.
抑郁症在癫痫患者中很常见，但经常被漏诊。筛查工具有助于在门诊环境中识别抑郁症。我们已发表了关于将神经精神疾病抑郁问卷简版（NDDI-E）和情绪温度计（ET）作为抑郁症筛查工具的验证研究（拉姆平等人，2012年）。在本研究中，我们描述了一种具有更高准确性的优化筛查工具模型。
前瞻性收集了英国一家繁忙的门诊癫痫诊所250例连续患者的数据。应用逻辑回归模型和递归划分技术（分类树、随机森林）从13个项目（NDDI-E和ET）中识别出最佳子集，并为基于《精神疾病诊断与统计手册》第四版（DSM-IV）的抑郁症分类预测类别归属概率提供框架。
逻辑回归模型和分类树（随机森林）都建议选择相同的项目进行分类（NDDI-E项目4、NDDI-E项目5、ET-痛苦、ET-焦虑、ET-抑郁）。最有用的回归模型包括所有5个上述变量，在曲线下面积（AUC）方面优于NDDI-E和ET（NDDI-E：0.903；ET7：0.889；逻辑回归：0.943）。使用随机森林开发的模型，通过仅使用原始数据的子集对单个分类将变量的可能分割限制为这5个项目来构建，表现相似（AUC：0.949）。
我们首次创建了一种包含言语和视觉模拟量表的抑郁症筛查工具模型，其特征表明该模型将比以前的工具更精确。为了确认预测性能，有必要收集新的数据样本以评估样本外性能。
