Screening for depression in epilepsy: A model of an enhanced screening tool

- The NDDI-E and Emotional Thermometers are screening tools for depression in epilepsy.
- We developed a new screening tool containing 5 items of verbal and visual analog scales.
- We applied two statistical models (logistic regression and random forest).
- This allows more reliable screening for depression based on fewer test items.
- As a result, fewer patients have to be explored for depression by the clinician.

ObjectiveDepression 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.MethodsData 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.ResultsBoth 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).ConclusionsFor 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.