Modeling Clinical Outcome of Children With Autistic Spectrum Disorders

The Children's Hospital of Philadelphia, Filadelfia, Pennsylvania, United States
PEDIATRICS (Impact Factor: 5.47). 07/2005; 116(1):117-22. DOI: 10.1542/peds.2004-1118
Source: PubMed


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.
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.
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.
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.
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.

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    • "IQ is a standardized, norm-referenced measure of human intelligence that is frequently reported as an outcome measure of ASD interventions (Eldevik et al. 2009; Perry et al. 2011; Virues-Ortega 2010). While IQ does not encompass the full complexity of ASD, it has been shown to account for some of the heterogeneity seen in this condition (Munson et al. 2008) and has been incorporated into models of clinical outcomes (Coplan and Jawad 2005). "
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    • "vary widely with respect to intellectual ability (Fombonne, 2005), and IQ has been found to be one of the most robust predictors of response to intervention (e.g., Harris & Handleman, 2000; Smith, 1999) and long-term outcomes in these individuals (Billstedt & Gillberg, 2005; Howlin, Goode, Hutton, & Rutter, 2004). IQ also plays a central role in the manifestation of core and associated symptoms in autism spectrum disorders (Bishop, Richler, & Lord, 2006; Borden & Ollendick, 1994; Carpentieri & Morgan, 1996; Coplan & Jawad, 2005; Estes, Dawson, Sterling, & Munson, 2007; Fein et al., 1999; Sevin et al., 1995; Volkmar, Cohen, Bregman, Hooks, & Stevenson, 1989), and it is associated with risk of comorbid conditions such as epilepsy (Gillberg & Steffenburg, 1987). Recently, some authors have proposed that IQ profiles could be used to organize children with autism spectrum disorders into different phenotypic subtypes (Joseph, Tager-Flusberg, & Lord, 2002; Munson et al., 2008). "
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    • "Thirdly, the fact that we did not use the conventional psychometric batteries to assess DQ constitutes a limitation on comparison with previous studies where IQ was the main variable used to assess outcome. The rationale for our strategy was, firstly, that the heterogeneity of chronological age and developmental age in our sample limited the systematic use of the same instrument for the whole sample; secondly, that conventional psychometric batteries are based on typical development and their items are not appropriate for heterochronic development; and thirdly, that verbal-item relevance is limited for very young children with PDD because of their behavioral and speech disorders (Coplan and Jawad, 2005). "
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