Predicting outcomes following cognitive behaviour therapy in child anxiety disorders: the influence of genetic, demographic and clinical information

Abstract

Background: Within a therapeutic gene by environment (G 9 E) framework, we recently demonstrated that variation in the Serotonin Transporter Promoter Polymorphism; 5HTTLPR and marker rs6330 in Nerve Growth Factor gene; NGF is associated with poorer outcomes following cognitive behaviour therapy (CBT) for child anxiety disorders. The aim of this study was to explore one potential means of extending the translational reach of G 9 E data in a way that may be clinically informative. We describe a 'risk-index' approach combining genetic, demographic and clinical data and test its ability to predict diagnostic outcome following CBT in anxious children. Method: DNA and clinical data were collected from 384 children with a primary anxiety disorder undergoing CBT. We tested our risk model in five cross-validation training sets. Results: In predicting treatment outcome, six variables had a minimum mean beta value of 0.5:5HTTLPR, NGF rs6330, gender, primary anxiety severity, comorbid mood disorder and comorbid externalising disorder. A risk index (range 0-8) constructed from these variables had moderate a predictive ability (AUC = .62-.69) in this study. Children scoring high on this index (5-8) were approximately three times as likely to retain their primary anxiety disorder at follow-up as compared with those children scoring 2 or less. Conclusion: Significant genetic, demographic and clinical predictors of outcome following CBT for anxiety-disordered children were identified. Combining these predictors within a risk index could be used to identify which children are less likely to be diagnosis-free following CBT alone and require longer or enhanced treatment. The 'risk-index' approach represents one means of harnessing the translational potential of G 9 E data.

Long Term Follow up in Children with Anxiety Disorders Treated with Acceptance and Commitment Therapy or Cognitive Behavioral Therapy: Outcomes and Predictors

“…Furthermore, the unique contribution of age to ITT post CSR variance was small. These findings are in line with other studies that have failed to identify age as a consistent predictor of outcomes [25,26].…”

“…Age was not significantly related to treatment outcomes when comparing treatment groups, with the same pattern of results observed for younger children and adolescents. This result is consistent with the acute phase findings [14], as well as those of Ginsburg, et al [25] and Hudson, et al [26], challenging motivational and engagement issues frequently perceived to be present in adolescents [49].…”

“…These findings give some support to those of previous studies that found that children with comorbid disorders were more likely to have poorer outcomes following anxiety treatment with CBT [26]. Antidepressant treatment has not been studied closely apart from the CAMS study [25], hence its predictive value for long term outcomes is unclear.…”

“…naturalistic design). Another group [26] examined the influence of genetic, demographic and clinical information on outcomes following cognitive behavioral therapy in child anxiety disorders. They found similar results to Ginsburg, et al [25] in terms of pre-treatment severity and gender as predictors.…”

Refining Clinical Judgment of Treatment Response and Symptom Remission Identification in Childhood Anxiety Using a Signal Detection Analysis on the Pediatric Anxiety Rating Scale

“…Consistent with existing definitions (e.g., Tolin et al 2005;Lewin et al 2011;Storch et al 2011;Caporino et al 2013;Farris et al 2013;Jeon et al 2013;Johnco et al 2015), participants were classified as treatment responders if they scored ''much'' or ''very much'' improved on the CGI-I at posttreatment. Consistent with others (e.g., Caporino et al 2013;Hudson et al 2013Hudson et al , 2015Johnco et al 2015), remission was defined as loss of primary diagnosis on the ADIS-IV (CSR£3) at posttreatment. For each proposed PARS cutoff value (percent reduction of symptoms in 5% intervals, and posttreatment raw scores), we examined the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), efficiency (percentage agreement with the gold standard test), and j statistics to determine optimal cutoff values.…”