Predicting outcomes following cognitive behaviour therapy in child anxiety disorders: the influence of genetic, demographic and clinical information.
Hudson JL., Lester KJ., Lewis CM., Tropeano M., Creswell C., Collier DA., Cooper P., Lyneham HJ., Morris T., Rapee RM., Roberts S., Donald JA., Eley TC.
BACKGROUND: Within a therapeutic gene by environment (G × 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 × 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 × E data.