Combining information from multiple sources for the diagnosis of autism spectrum disorders for toddlers and young preschoolers from 12 to 47 months of age

In the past few years, research has flourished concerning detection of ASD symptoms in the first 3 years of life due to the belief that earlier provision of services and treatments is associated with better outcomes. With increasing needs for early detention of ASD, new diagnostic algorithms for toddlers and young preschoolers from 12 to 47 months of age have recently been developed for the Autism Diagnostic Interview-Revised (ADI-R; Rutter, Le Couteur, & Lord, 2003), a standardized, semi-structured, investigator-based interview for caregivers (Kim & Lord, in press). In addition, algorithms for the Autism Diagnostic Observation Schedule (ADOS; Lord, Rutter, DiLavore, & Risi, 2001) have been revised to improve the diagnostic validity of the instrument, a standardized, semi-structured, clinician-administered observation of communication, social interaction, and play (Gotham et al., 2007), including two revised algorithms for children who use 5 or more single words or even less language and for children under age 5 using phrase speech. New algorithms have also been developed for the ADOS-Toddler module for children under 30 months of age (Luyster et al., 2009). Past studies showed enhanced diagnostic validity when information from both the ADI-R and ADOS are used together. There has not yet been a systematic attempt to examine the combined use of these instruments using the newly developed and revised algorithms. Thus, the present study focuses on the validity of the combined use of the ADI-R and ADOS using the new and revised algorithms with children under age 4. With a best estimate clinical diagnosis treated as the gold standard, previous work primarily with older children and adolescents found that using data from multiple sources (i.e. clinicians, caregivers, and teachers) enhances accuracy for the diagnosis of ASD. For example, the Social Responsiveness Scale resulted in high diagnostic specificity for children and adolescents with ASD when information from both parent and teacher reports were combined (Constantino et al., 2007). Bishop and Baird (2001) reported improved validity of the Children’s Communication Checklist when information from both parents and professionals were used for 151 children with pervasive developmental disorders (PDD) or other developmental disorders between 5 to 17 years of age. Corsello et al. (2007) reported enhanced diagnostic validity by combining information across instruments, either the Social Communication Questionnaire (SCQ; Rutter, Bailey, & Lord, 2003) or the ADI-R with the ADOS for the diagnosis of children with ASD between age 2 and 16 years. Risi et al. (2006) found a better balance of sensitivity and specificity when the ADI-R and ADOS were used in combination compared to when each instrument was used alone. For example, the combined use of these instruments resulted in sensitivity and specificity of 82% and 86% for children with autism compared to children with non-spectrum disorders over age 3 years. For younger children, sensitivity and specificity for the same diagnostic comparison using both instruments were 81% and 87%, respectively. In contrast, when each instrument was used alone, specificities ranged from 59% to 72%, with sensitivities remaining above 80%. Most of the results were obtained primarily from older children, even though a study by Le Couteur et al. (2008) also found that combining information from both instruments using preexisting algorithms provided improved diagnostic accuracy for preschoolers with ASD compared to either instrument in isolation. However, using newly developed and revised algorithms, the present study is the first study examining the validity of the combined use of the ADI-R and ADOS for toddlers as young as 12 months of age. In very young children, diagnostic differentiation between non-autism ASD (e.g. PDD-NOS) and autism is less stable than for older children and adolescents (Lord et al., 1999; Szatmari et al., 2002; Wiggins, Robins, Adamson, Bakeman, & Henrich, in press). Consequently, as the newest ADI-R and ADOS-T algorithms for toddlers and young preschoolers have been developed (Luyster et al., 2009; Kim & Lord, in press), a shift has occurred from having separate autism and other ASD cutoffs to using only a single classification of ASD. In addition, in order to formally acknowledge the less clear stability of diagnoses in younger children, these algorithms provide ranges of concern (little-to-no, mild-to-moderate, or moderate-to-severe concern), to be used in clinical monitoring and follow-up. However, groupings are necessary for several different purposes. Thus, the new ADI-R algorithms also provide two cutoffs, one for research (more restrictive; higher specificity with lower sensitivity) and one for clinical purposes (more inclusive; higher sensitivity with lower specificity). Past studies examining validity of the ADI-R and ADOS have found that parent reports and clinician observations do not always agree. Agreement between these instruments has varied across samples and analytic techniques. In a sample of 797 ASD and 163 non-spectrum cases over 36 months of age, Risi et al. (2006) found that the Pearson r correlation between ADI-R and ADOS algorithm totals was 0.57. Correlations differed by domains in the study by Le Couteur et al. (2008), ranging from 0.51 to 0.71 for a sample of 77 preschoolers with ASD and 24 with other developmental disorders. Agreement between the instruments using Kappa ranged from 0.48 to 0.62. In another study (de Bildt et al., 2004), correlations ranged from 0.52 to 0.54 between the ADI-R and ADOS algorithm totals for 123 children aged 5 to 20 years with ASD and intellectual disability and 62 with intellectual disability only. In contrast, Ventola et al. (2006) compared the application of full ADI-R and ADOS diagnostic criteria to each other and clinical diagnosis in a sample of 36 ASD and 9 non-spectrum cases aged 16 to 31 months. Significant levels of agreement were found between the ADOS and clinical judgment (κ=0.59, p<.001) but agreement between the ADI-R and clinical judgment (κ=0.15, ns) and between the ADI-R and the ADOS (κ=0.07, ns) was poor. Because the combined use of the ADI-R and ADOS has shown better diagnostic validity than either individual instrument, it is recommended that clinicians and researchers use information from both instruments when making diagnoses. However, due to constraints in time, cost, or expertise, often only one of the instruments is actually used. Relatively little is known about ways to maximize validity in this case. One approach would be to determine scores on the instruments associated with a very high (or low) probability of receiving the classification of ASD on the “alternative instrument” (referred to as “positive (or negative) screening estimate” hereafter). For instance, if a child’s score reaches a positive screening estimate on the ADI-R, a clinician could presumably omit the ADOS assuming that the probability of the child receiving an ASD classification on the ADOS would be very high. The same strategy could be used with a negative screening estimate. Another approach is to conduct similar analyses using best estimate clinical (BEC) diagnoses based on all available information as the gold standard and then to determine if there are scores on each instrument that result in 100% specificity for ASD. That is, we can examine what score on each instrument successfully excludes all cases determined to not have ASD (henceforth referred to as “high specificity case scores”) and then describe the sensitivities of these scores. For example, if a child meets or exceeds a high specificity case score on the ADOS, a clinician evaluating the child could assume that the chance of the child receiving a BEC diagnosis of ASD would be very high and choose to omit the ADI-R. In sum, the purpose of this study is to examine the combined use of the ADI-R and ADOS for children under age 4 using the new and revised algorithms. Often, a misdiagnosis that results in a child failing to receive necessary services is the greatest concern. On the other hand, over-diagnosis has negative consequences for individual children, public health strategies and research. Consequently, we present data supporting alternative methods for using both research and clinical cutoffs from the new ADI-R algorithms. Agreement between the two instruments is also evaluated by examining the overlap between the ADI-R and ADOS-T ranges of concern and correlations between algorithm totals. A final goal was to evaluate sequential assessment strategies using positive/negative screening estimates and high specificity case scores that could allow the empirically-validated use of a single instrument when information from a second instrument would very likely be redundant.