Development of a Diagnostic Algorithm for the PDD Behavior Inventory Based on Classification Trees

Friday, May 13, 2016: 5:30 PM-7:00 PM
Hall A (Baltimore Convention Center)
I. L. Cohen1, X. Liu2, M. Hudson3, B. Z. Karmel4 and J. M. Gardner5, (1)1050 Forest Hill Rd, New York State Institute for Basic Research in Developmental Disabilities, Staten Island, NY, (2)Queen's University, Kingston, ON, Canada, (3)asd-carc, Kingston, ON, Canada, (4)Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, (5)Infant Development, New York State Institute for Basic Research in Developmental Disabilities, Staten Island, NY
Background:  The PDD Behavior Inventory (PDDBI) is a reliable and valid assessment tool that has been shown to yield excellent sensitivity and specificity for children whose diagnoses were confirmed by the ADOS-G and ADI-R (Cohen et al. 2010).  Recently, we have shown that the PDDBI can differentiate children with ASD from those with Attention Deficit Hyperactivity Disorder using a Classification Tree, a non-parametric type of machine learning (Cohen, 2013). Due to these results and need for Level 2 screeners for ASD, we explored the use of Classification Trees as a means of developing a diagnostic algorithm for the PDDBI.  

Objectives:  To develop a reliable and valid diagnostic algorithm for the PDDBI.

Methods: To date, 649 parent and 202 teacher PDDBI forms have been collected from the NYS Institute for Basic Research (IBR), and from Queens University.  Approximately 83% of cases have been diagnosed with "ASD" with the remainder (“OTHER”) having ASD ruled out as a diagnosis after extensive clinical evaluation or identified by parents as unaffected siblings or who were toddlers taking part in a longitudinal investigation of at-risk infants.

The Classification and Regression Trees module (Statistica, Version 12) was used to develop the algorithm. Inputs included the Repetitive, Ritualistic and Pragmatic Problems Composite T-score, the Approach-Withdrawal Problems Composite T-score, the Expressive Social Communication Abilities Composite T-score, the Autism Composite T-Score, the Social Discrepancy score, and the Semantic-Pragmatic Problems Discrepancy score. Sixty percent of the dataset was used for training and 20% for testing during the development process. The remaining 20% (the “validation set” not used in the model development process) helped validate the final model.

Results:  A number of models were explored and yielded similar results.  The selected model divided the ASD sample into two parts, a “typical” ASD group and a “high social-functioning” ASD group while the OTHER sample was divided into three groups, a relatively unaffected group, and two smaller sets: 1) a “rigid” group, and 2) a “severe behavior problem” group.

The two ASD groups differed on IQ, Vineland, and ADOS severity scores; parent reports of seizures; and association with a gene polymorphism linked to autism severity (Cohen et al. 2003).  

Sensitivity and Specificity were 83% and 87% for the training set, 86% and 81% for the test set, and 82% and 81% for the validation set. Sensitivity and specificity were 80% each for cases <4 years and 85% and 89%, respectively, for cases >4 years.

Overall agreement between parent and teacher global algorithm diagnoses (ASD vs OTHER) was 78% (Kappa = 0.47) and was 66% (Kappa = 0.52) for the more fine grain groupings. Using only cases in which parent and teacher forms yielded identical groupings, increased sensitivity and specificity to 90% each.

Conclusions:  Results confirm previous studies suggesting two forms of ASD, a classic presentation (often associated with intellectual delays and seizures), and a group with better social and language skills having a more optimal outcome.  These results  suggest that the PDDBI can serve as a useful Level 2 screener.