One of the primary diagnostic criteria for the diagnosis of autism spectrum disorders (ASD) is the presence of a language delay or impairment. Children with ASD are now being identified at significantly younger ages, and prior research has consistently found that early language skills in this population are heterogeneous and an important predictor for later outcome. The goal of this study was to systematically investigate language in toddlers with ASD and to identify early correlates of receptive and expressive language in this population. The study included 164 toddlers with ASD between the ages of 18 and 33 months who were evaluated on several cognitive, language and behavioral measures. Results suggested good agreement among different measures of early language, including direct assessment and parent report measures. Significant concurrent predictors of receptive language included gestures, non-verbal cognitive ability and response to joint attention. For expressive language, the most significant predictors were non-verbal cognitive ability, gestures and imitation. These findings have important implications for intervention programs targeting this population.

The Autism Diagnostic Observation Schedule (ADOS; Lord et al., 2000) is widely accepted as a "gold standard" diagnostic instrument, but it is of restricted utility with very young children. The purpose of the current project was to modify the ADOS for use in children under 30 months of age. A modified ADOS, the ADOS Toddler Module (or Module T), was used in 360 evaluations. Participants included 182 children with best estimate diagnoses of ASD, non-spectrum developmental delay or typical development. A final set of protocol and algorithm items was selected based on their ability to discriminate the diagnostic groups. The traditional algorithm "cutoffs" approach yielded high sensitivity and specificity, and a new range of concern approach was proposed.Keywords autism spectrum disorders; diagnosis; ADOS; infants; toddlers Almost ten years ago, the standardization of a revised Autism Diagnostic Observation Schedule (ADOS), a semi-structured assessment for the diagnosis of autism spectrum disorders (ASD) (Lord, Rutter, DiLavore & Risi, 1999) was described. The ADOS has gradually become an integral part of many research and clinical protocols of children suspected of having an autism spectrum disorder (ASD). Due to the growing understanding of symptoms in the first two years of life and the desire of researchers and clinicians to have standardized instruments for use with infants and young toddlers, there is a need for diagnostic tools that are appropriate for very young children. This paper presents a new Toddler Module of the ADOS. The Toddler Module retains the original spirit and many of the original tasks of the ADOS, but is intended for use in children under 30 months of age who have nonverbal mental ages of at least 12 months. The scope of this report is to provide a summary of the new measure, the procedures used to develop it, a description of the standardization sample and relevant psychometrics.In introducing this new module, it is valuable to review the structure of the previously published ADOS. The ADOS evaluates social interaction, communication and play through a series of planned "presses" (Lord et al., 1989) in the context of a naturalistic social interaction. Some of the presses are intended to offer a high level of structure for the participant, while others are intended to provide less structure. All presses, however, afford contexts for both initiations and responses, which are then coded in a standardized manner. An algorithm, which sums the scores of particular items from the measure, yields a classification indicative of autism, ASD or nonspectrum conditions. This classification can then be used by a clinician or researcher as one part of a comprehensive diagnostic process.The first ADOS was introduced in the late 1980s and was intended for children who had a spoken language age equivalent of at least 36 months. A revision was published in 2000 that reflected the need for the measure to be applicable to a wider range of chronological and developmental ages. The 2000 version provided f...

Context Clinical best estimate diagnoses of specific autism spectrum disorders (autistic disorder, pervasive developmental disorder-not otherwise specified, Asperger’s disorder) have been used as the diagnostic gold standard, even when information from standardized instruments is available. Objective To determine if the relationships between behavioral phenotypes and clinical diagnoses of different autism spectrum disorders vary across 12 university-based sites. Design Multi-site observational study collecting clinical phenotype data (diagnostic, developmental and demographic) for genetic research. Classification trees were employed to identify characteristics that predicted diagnosis across and within sites. Setting Participants were recruited through 12 university-based autism service providers into a genetic study of autism. Participants 2102 probands (1814 males) between 4 and 18 years of age (M age=8.93, SD=3.5 years) who met autism spectrum criteria on the Autism Diagnostic Interview–Revised and Autism Diagnostic Observation Schedule and had a clinical diagnosis of an autism spectrum disorder. Main Outcome Measures Best estimate clinical diagnoses predicted by standardized scores from diagnostic, cognitive, and behavioral measures. Results Though distributions of scores on standardized measures were similar across sites, significant site differences emerged in best estimate clinical diagnoses of specific autism spectrum disorders. Relationships between clinical diagnoses and standardized scores, particularly verbal IQ, language level and core diagnostic features, varied across sites in weighting of information and cut-offs. Conclusions Clinical distinctions among categorical diagnostic subtypes of autism spectrum disorders were not reliable even across sites with well-documented fidelity using standardized diagnostic instruments. Results support the move from existing sub-groupings of autism spectrum disorders to dimensional descriptions of core features of social affect and fixated, repetitive behaviors, together with characteristics such as language level and cognitive function.

The Autism Diagnostic Interview-Revised (ADI-R) is one of the most commonly used instruments for assisting in the behavioral diagnosis of autism. The exam consists of 93 questions that must be answered by a care provider within a focused session that often spans 2.5 hours. We used machine learning techniques to study the complete sets of answers to the ADI-R available at the Autism Genetic Research Exchange (AGRE) for 891 individuals diagnosed with autism and 75 individuals who did not meet the criteria for an autism diagnosis. Our analysis showed that 7 of the 93 items contained in the ADI-R were sufficient to classify autism with 99.9% statistical accuracy. We further tested the accuracy of this 7-question classifier against complete sets of answers from two independent sources, a collection of 1654 individuals with autism from the Simons Foundation and a collection of 322 individuals with autism from the Boston Autism Consortium. In both cases, our classifier performed with nearly 100% statistical accuracy, properly categorizing all but one of the individuals from these two resources who previously had been diagnosed with autism through the standard ADI-R. Our ability to measure specificity was limited by the small numbers of non-spectrum cases in the research data used, however, both real and simulated data demonstrated a range in specificity from 99% to 93.8%. With incidence rates rising, the capacity to diagnose autism quickly and effectively requires careful design of behavioral assessment methods. Ours is an initial attempt to retrospectively analyze large data repositories to derive an accurate, but significantly abbreviated approach that may be used for rapid detection and clinical prioritization of individuals likely to have an autism spectrum disorder. Such a tool could assist in streamlining the clinical diagnostic process overall, leading to faster screening and earlier treatment of individuals with autism.