Gilliam Frank G, Barry John J, Hermann Bruce P, Meador Kimford J, Vahle Victoria, Kanner Andres M
Department of Neurology, The Neurological Institute, Columbia University, New York, USA.
Lancet Neurol. 2006 May;5(5):399-405. doi: 10.1016/S1474-4422(06)70415-X.
Depression is a common comorbid disorder in epilepsy but is not routinely assessed in neurology clinics. We aimed to create a rapid yet accurate screening instrument for major depression in people with epilepsy.
We developed a set of 46 items to identify symptoms of depression that do not overlap with common comorbid cognitive deficits or adverse effects of antiepileptic drugs. This preliminary instrument and several reliable and valid instruments for diagnosis of depression on the basis of criteria from the Diagnostic and Statistical Manual IV, depression symptom severity, health status, and toxic effects of medication were applied to 205 adult outpatients with epilepsy. We used discriminant function analysis to identify the most efficient set of items for classification of major depression, which we termed the neurological disorders depression inventory for epilepsy (NDDI-E). Baseline data for 229 demographically similar patients enrolled in two other clinical studies were used for verification of the original observations.
The discriminant function model for the NDDI-E included six items. Internal consistency reliability of the NDDI-E was 0.85 and test-retest reliability was 0.78. An NDDI-E score of more than 15 had a specificity of 90%, sensitivity of 81%, and positive predictive value of 0.62 for a diagnosis of major depression. Logistic regression showed that the model of association of major depression and the NDDI-E was not affected by adverse effects of antiepileptic medication, whereas models for depression and generic screening instruments were. The severity of depression symptoms and toxic effects of drugs independently correlated with subjective health status, explaining 72% of variance. Results from a separate verification sample also showed optimum sensitivity, specificity, and predictive power at a cut score of more than 15.
Major depression in people with epilepsy can be identified by a brief set of symptoms that can be differentiated from common adverse effects of antiepileptic drugs. The NDDI-E could enable rapid detection and improve management of depression in epilepsy in accordance with internationally recognised guidelines.
抑郁症是癫痫常见的共病性疾病,但在神经科门诊中并未常规评估。我们旨在创建一种快速且准确的癫痫患者重度抑郁症筛查工具。
我们制定了一套46项条目,以识别与常见共病认知缺陷或抗癫痫药物不良反应不重叠的抑郁症状。该初步工具以及基于《精神疾病诊断与统计手册》第四版标准、抑郁症状严重程度、健康状况和药物毒性作用的几种可靠且有效的抑郁症诊断工具,应用于205名成年癫痫门诊患者。我们使用判别函数分析来确定用于重度抑郁症分类的最有效条目集,我们将其称为癫痫神经障碍抑郁量表(NDDI - E)。另外两项临床研究中纳入的229名人口统计学特征相似患者的基线数据用于验证原始观察结果。
NDDI - E的判别函数模型包含6项条目。NDDI - E的内部一致性信度为0.85,重测信度为0.78。NDDI - E得分超过15分对重度抑郁症诊断的特异性为90%,敏感性为81%,阳性预测值为0.62。逻辑回归显示,重度抑郁症与NDDI - E的关联模型不受抗癫痫药物不良反应的影响,而抑郁症与通用筛查工具的模型则受影响。抑郁症状的严重程度和药物毒性作用与主观健康状况独立相关,解释了72%的方差。来自另一个验证样本的结果也显示,在得分超过15分时具有最佳的敏感性、特异性和预测能力。
癫痫患者的重度抑郁症可通过一组简短的症状来识别,这些症状可与抗癫痫药物的常见不良反应相区分。NDDI - E能够根据国际公认的指南实现快速检测并改善癫痫患者抑郁症的管理。