Modeling clinical outcome of children with autistic spectrum disorders.

OBJECTIVES: Autistic spectrum disorders (ASD) have variable developmental outcomes, for reasons that are not entirely clear. The objective of this study was to test the clinical observation that initial developmental parameters (degree of atypicality and level of intelligence) are a major predictor of outcome in children with ASD and to develop a statistical method for modeling outcome on the basis of these parameters.

METHODS: A retrospective chart review was conducted of a child development program at a tertiary center for the evaluation of children with developmental disabilities. All children who had ASD, were seen by J.C. between July 1997 and December 2002, met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for autism or pervasive developmental disorder (referred to hereafter as ASD), had undergone at least 1 administration of the Childhood Autism Rating Scale (CARS), and had at least 1 determination of developmental quotient (DQ) or IQ (N = 91) were studied. The sample was 92.3% male and 80.2% white.

METHODS: The DSM-IV was used to confirm that each patient met criteria for a diagnosis of autism or pervasive developmental disorder. The CARS was used to quantify the severity of expression of ASD. Age at evaluation, CARS score, and DQ or IQ at each visit were extracted from the medical record. The 2 independent sample t test or the Mann-Whitney test was used for comparing CARS and age between 2 groups: first recorded DQ or IQ <0.70 (n = 58) versus first recorded DQ or IQ >or=0.70 (n = 33). Associations among CARS score, IQ or DQ, and age were examined using Pearson or Spearman correlation. A mixed-effect model was used for expressing the multivariate model. Length of follow-up (period) was calculated by subtracting age in months at initial evaluation from age in months at each follow-up evaluation. Therefore, at first evaluation, period = 0. Period was considered as a random effect because collection of repeated information from patients was not uniform. The predictive relationships among CARS, age at first evaluation, period, and DQ or IQ group (<0.70 and >or=0.70) were examined using a mixed-effects model. Variables that were expressed as percentage change between first and last measurements were analyzed using the t test or the Mann-Whitney test. Socioeconomic status was assessed using Hollingshead criteria.

RESULTS: All patients met DSM-IV criteria for ASD. Mean age at initial evaluation was 46.2 months (SD: 23.7; range: 20.0-167.3 months). Mean CARS score at initial evaluation was 36.1 (SD: 6.3; range: 21.5-48). Mean DQ or IQ at initial evaluation was 0.65 (SD: 0.20; range: 0.16-1.10). There was no significant difference in socioeconomic status between DQ/IQ groups. CARS scores among children with an initial DQ or IQ <0.70 showed no significant decrement with time. In contrast, CARS scores among children with an initial DQ or IQ >or=0.70 showed a significant decrement with time, which could be modeled by the formula CARS = 37.93 - [(0.12 x age in months at first visit) + (0.23 x period)]. The predicted CARS scores generated by this model correlated with the observed values (r = 0.71) and explained 50% of the variability in the CARS scores for this group.

CONCLUSIONS: These data provide preliminary validation of a statistical model for clinical outcome of ASD on the basis of 3 parameters: age, degree of atypicality, and level of intelligence. This model, if replicated in a prospective, population-based sample that is controlled for treatment modalities, will enhance our ability to offer a prognosis for the child with ASD and will provide a benchmark against which to judge the putative benefits of various treatments for ASD. Our model may also be useful in etiologic and epidemiologic studies of ASD, because different causes of ASD are likely to follow different developmental trajectories along these 3 parameters.