B4344 - Individualized Prediction and Intervention-targeting for Children with Depression Anxiety and ADHD Predictive and Causal Data - 05/06/2023

B number: 
Principal applicant name: 
Glenn Saxe | NYU Langone (USA)
Constantin Aliferis, MD, PhD, FACMI, Sisi Ma, PhD, Thomas Kirsh, BS, Linmin Wang, MS, Michelle Papp
Title of project: 
Individualized Prediction and Intervention-targeting for Children with Depression, Anxiety, and ADHD: Predictive and Causal Data
Proposal summary: 

We propose research to advance precision in predicting individual prognostic trajectory and individual response to intervention for three common and debilitating categories of mental disorders in childhood: (1) Depressive Disorders, (2) Anxiety Disorders, and (3) Attention Deficit Hyperactivity Disorder. Our research will advance this precision by elucidating the etiologic heterogeneity of samples of children within these three diagnostic groups and determining clinical signatures corresponding to such heterogeneity. Such signatures will guide the development of a set of assessment and clinical decision-support tools that we will evaluate – integrating behavioral tests/measures – for classifying children with depression, anxiety disorders, and ADHD into sub-groups strongly informing on individual prognosis and probable responsiveness to specific categories of intervention.

Impact of research: 
Our research will evaluate the clinical signatures available in current EHR systems for predictive outcomes and treatment responsiveness in children with i. Depressive Disorders, ii. Anxiety Disorders, and iii. ADHD; determine how behavioral tests available in large research cohorts improve the prediction of outcomes and treatment responsiveness for children from the three diagnostic groups; and determine additional intervention targets for children that could not be classified for their probable response to such interventions. We intend to use the clinical signatures determined in this research to produce and evaluate a set of practical and safety-oriented Assessment and Decision Support Tools (ADSTs) - that will include the assessment and use of specific behavioral tasks and passive behavioral data - to sub-classify children with Depressive Disorders, Anxiety Disorders, and ADHD into categories informing their individual prognosis and probable response to specific intervention (including novel interventions).
Date proposal received: 
Saturday, 27 May, 2023
Date proposal approved: 
Monday, 5 June, 2023
Statistics/methodology, Behaviour - e.g. antisocial behaviour, risk behaviour, etc., Computer simulations/modelling/algorithms, Statistical methods, Childhood - childcare, childhood adversity, Genetics, Methods - e.g. cross cohort analysis, data mining, mendelian randomisation, etc., Psychology - personality