title: Screening for depression in epilepsy: a model of an enhanced screening tool.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institute of Applied Statistics, Johannes Kepler University, Linz, Austria.
authors.forename: Mihael
authors.initials: M
authors.lastname: Drinovac
authors.affiliation: Institute of Applied Statistics, Johannes Kepler University, Linz, Austria.
authors.forename: Helga
authors.initials: H
authors.lastname: Wagner
authors.affiliation: 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.
authors.forename: Niruj
authors.initials: N
authors.lastname: Agrawal
authors.affiliation: Epilepsy Group, Atkinson Morley Regional Neurosciences Centre, St George's Hospital, London, UK; St George's University of London, London, UK.
authors.forename: Hannah R
authors.initials: HR
authors.lastname: Cock
authors.affiliation: Department of Cancer Studies and Molecular Medicine, University of Leicester, Leicester, UK; Department of Psycho-oncology, Leicestershire Partnership NHS Trust, Leicester, UK.
authors.forename: Alex J
authors.initials: AJ
authors.lastname: Mitchell
authors.affiliation: St George's University of London, London, UK; Department of Neurology, Wagner-Jauregg Neuroscience Centre, Kepler University Hospital, Linz, Austria. Electronic address: neurologiesekr.wj@gespag.at.
authors.forename: Tim J
authors.initials: TJ
authors.lastname: von Oertzen
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D003863:Depression / Q000175:diagnosis* / Q000453:epidemiology; D004827:Epilepsy / Q000453:epidemiology / Q000523:psychology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011569:Psychiatric Status Rating Scales / Q000592:standards*; D011594:Psychometrics / Q000295:instrumentation* / Q000379:methods; D055815:Young Adult
keywords: Comorbidity; Depression; Epilepsy; Human; Questionnaire; Screening