Development of standardized diagnostic instruments has facilitated the systematic characterization of individuals with autism spectrum disorders (ASD) in clinical and research settings. However, ...overemphasis on scores from specific instruments has significantly detracted from the original purpose of these tools. Rather than provide a definitive “answer,” or even a confirmation of diagnosis, standardized diagnostic instruments were designed to aid clinicians in the process of gathering information about social communication, play, and repetitive and sensory behaviors relevant to diagnosis and treatment planning. Importantly, many autism diagnostic instruments are not validated for certain patient populations, including those with severe vision, hearing, motor, and/or cognitive impairments, and they cannot be administered via a translator. In addition, certain circumstances, such as the need to wear personal protective equipment (PPE), or behavioral factors (e.g., selective mutism) may interfere with standardized administration or scoring procedures, rendering scores invalid. Thus, understanding the uses and limitations of specific tools within specific clinical or research populations, as well as similarities or differences between these populations and the instrument validation samples, is paramount. Accordingly, payers and other systems must not mandate the use of specific tools in cases when their use would be inappropriate. To ensure equitable access to appropriate assessment and treatment services, it is imperative that diagnosticians be trained in best practice methods for the assessment of autism, including if, how, and when to appropriately employ standardized diagnostic instruments.
This article provides a selective review of advances in scientific knowledge about autism spectrum disorder (ASD), using DSM-5 (
Diagnostic and Statistical Manual of Mental Disorders
, fifth edition) ...diagnostic criteria as a framework for the discussion. We review literature that prompted changes to the organization of ASD symptoms and diagnostic subtypes in DSM-IV, and we examine the rationale for new DSM-5 specifiers, modifiers, and severity ratings as well as the introduction of the diagnosis of social (pragmatic) communication disorder. Our goal is to summarize and critically consider the contribution of clinical psychology research, along with that of other disciplines, to the current conceptualization of ASD.
To examine differences in behavioral symptoms and cognitive functioning between males and females with autism spectrum disorder (ASD).
We analyzed data from 2,418 probands with autism (304 females ...and 2,114 males) included in the Simons Simplex Collection. Sex differences were evaluated across measures of autism symptoms, cognitive and motor functioning, adaptive behavior, and associated behavior problems. Measurement bias was examined using latent variable models of symptoms. Unadjusted and propensity-adjusted analyses were computed to ensure that sex differences were not due to unbalanced sampling. Moderator and mediator analyses evaluated whether sex differences were modified by clinical characteristics or were driven by cognitive ability.
Females with ASD had greater social communication impairment, lower levels of restricted interests, lower cognitive ability, weaker adaptive skills, and greater externalizing problems relative to males. Symptom differences could not be accounted for by measurement differences, indicating that diagnostic instruments captured autism similarly in males and females. IQ reductions mediated greater social impairment and reduced adaptive behavior in females with ASD, but did not mediate reductions in restricted interests or increases in irritability.
A specific female ASD phenotype is emerging that cannot be accounted for by differential symptom measurement. The present data suggest that the relatively low proportion of high-functioning females may reflect the effect of protective biological factors or may be due to under-identification. Additional carefully accrued samples are needed to confirm the present pattern and to evaluate whether observed sex ratios in high-functioning cases are reduced if female-specific indicators of restricted interests are included.
Background
Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we ...fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools.
Methods
The data consisted of Autism Diagnostic Interview‐Revised (ADI‐R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non‐ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best‐estimate clinical diagnosis of ASD versus non‐ASD. Parameter settings were tuned in multiple levels of cross‐validation.
Results
The created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near‐peak performance with five or fewer codes). Results from ML‐based fusion of ADI‐R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes.
Conclusions
ML is useful for creating robust, customizable instrument algorithms. In a unique dataset comprised of controls with other difficulties, our findings highlight the limitations of current caregiver‐report instruments and indicate possible avenues for improving ASD screening and diagnostic tools.
Differential diagnosis of autism is very complex. Best practice guidelines in the US encourage the use of specialized tools by a highly trained provider. The need for this comprehensive evaluation, ...coupled with the increase in autism prevalence and awareness, has led to alarmingly long wait times for diagnostic evaluations. Several solutions are currently being researched to remedy this problem and relieve the pressure, including testing new devices or procedures that can speed up the diagnostic process. Creative solutions are welcomed; however, we urge caution in the use of new devices and methods without being fully vetted. Moreover, a quality assessment provides much more than just a designation of whether or not autism is present. Thus, even in cases when alternative means could be used to more quickly arrive at a diagnosis, a comprehensive assessment with a trained clinician is needed to guide recommendations and ongoing care.
Substantial revisions to the DSM-IV criteria for autism spectrum disorders (ASDs) have been proposed in efforts to increase diagnostic sensitivity and specificity. This study evaluated the proposed ...DSM-5 criteria for the single diagnostic category of autism spectrum disorder in children with DSM-IV diagnoses of pervasive developmental disorders (PDDs) and non-PDD diagnoses.
Three data sets included 4,453 children with DSM-IV clinical PDD diagnoses and 690 with non-PDD diagnoses (e.g., language disorder). Items from a parent report measure of ASD symptoms (Autism Diagnostic Interview-Revised) and clinical observation instrument (Autism Diagnostic Observation Schedule) were matched to DSM-5 criteria and used to evaluate the sensitivity and specificity of the proposed DSM-5 criteria and current DSM-IV criteria when compared with clinical diagnoses.
Based on just parent data, the proposed DSM-5 criteria identified 91% of children with clinical DSM-IV PDD diagnoses. Sensitivity remained high in specific subgroups, including girls and children under 4. The specificity of DSM-5 ASD was 0.53 overall, while the specificity of DSM-IV ranged from 0.24, for clinically diagnosed PDD not otherwise specified (PDD-NOS), to 0.53, for autistic disorder. When data were required from both parent and clinical observation, the specificity of the DSM-5 criteria increased to 0.63.
These results suggest that most children with DSM-IV PDD diagnoses would remain eligible for an ASD diagnosis under the proposed DSM-5 criteria. Compared with the DSM-IV criteria for Asperger's disorder and PDD-NOS, the DSM-5 ASD criteria have greater specificity, particularly when abnormalities are evident from both parents and clinical observation.
Background
Minimally verbal (MV) children with autism spectrum disorder (ASD) are often assumed to be profoundly cognitively impaired and excluded from analyses due to challenges completing ...standardized testing protocols. A literature aimed at increasing understanding of this subgroup is emerging; however, the many methods used to define MV status make it difficult to compare studies. Understanding how different instruments and definitions used to identify MV children affect sample composition is critical to advance research on this understudied clinical population.
Method
The MV status of 1,470 school‐aged children was defined using five instruments commonly used in ASD research. MV sample composition was compared across instruments. Analyses examined the proportion of overlap across MV subgroups and the extent to which child characteristics varied across MV subgroups defined using different definitions or combinations of measures.
Results
A total of 257 children were classified as MV on at least one instrument. Proportion of overlap between definitions ranged from 3% to 100%. The stringency of definition (i.e. few‐to‐no vs. some words) was associated with differences in cognitive and adaptive functioning; more stringent definitions yielded greater consistency of MV status across instruments. Cognitive abilities ranged from profoundly impaired to average intelligence; 16% had NVIQ ≥ 70. Approximately half exhibited verbal skills commensurate with nonverbal cognitive ability, whereas half had verbal abilities significantly lower than their estimated NVIQ.
Conclusions
Future studies of MV children must carefully consider the methods used to identify their sample, acknowledging that definitions including children with ‘some words’ may yield larger samples with a wider range of language and cognitive abilities. Broadly defined MV samples may be particularly important to delineate factors interfering with language development in the subgroup of children whose expressive impairments are considerably below their estimated nonverbal cognitive abilities.
The topic of this special issue on secondary versus idiopathic autism allows for discussion of how different groups may come to manifest autism spectrum disorder (ASD) or ASD-like symptoms despite ...important etiological differences. A related issue is that, because many of the social communication deficits that define ASD represent a failure to acquire developmentally expected skills, these same deficits would be expected to occur to some extent in all individuals with intellectual disability (ID). Thus, regardless of etiology, ASD symptoms may appear across groups of individuals with vastly different profiles of underlying deficits and strengths. In this focused review, we consider the impact of ID on the diagnosis of ASD. We discuss behavioral distinctions between ID and ASD, in light of the diagnostic criterion mandating that ASD should
be diagnosed if symptoms are accounted for by ID or general developmental delay. We review the evolution of the autism diagnosis and ASD diagnostic tools to understand how this distinction has been conceptualized previously. We then consider ways that operationalized criteria may be beneficial for making the clinical distinction between ID with and without ASD. Finally, we consider the impact of the blurred diagnostic boundaries between ID and ASD on the study of secondary versus idiopathic ASD. Especially pertinent to this discussion are findings that a diagnosis of ID in the context of an ASD diagnosis may be one of the strongest indicators that an associated condition or specific etiological factor is present (i.e., secondary autism).
Autism Spectrum Disorder (ASD or autism) is a heterogeneous neurodevelopmental disorder. We are now at a critical juncture in autism research where we have the knowledge base and expertise to begin ...to think about studies that view heterogeneity, not as ‘statistical noise’ that can be ‘accounted for’ using data‐reduction techniques (such as group trajectories), but rather as ‘informative variance’ that can help form a more precise and dynamic picture of autism. In this Editorial we coin a new term and introduce the concept of ‘chronogeneity’ for the study of autism heterogeneity in relation to the dimension of time (chrono). Using examples of ongoing research and analytical advances we build the case for the potential utility of the concept of ‘chronogeneity’ and argue that a refined approach to the longitudinal investigation of autism (and other neurodevelopmental disorders) may move us closer to more precise and adaptive models of care for the children and youth affected by these disorders.
Background: The Social Responsiveness Scale (SRS) is a parent‐completed screening questionnaire often used to measure autism spectrum disorders (ASD) severity. Although child characteristics are ...known to influence scores from other ASD‐symptom measures, as well as parent‐questionnaires more broadly, there has been limited consideration of how non‐ASD‐specific factors may affect interpretation of SRS scores. Previous studies have explored effects of behavior problems on SRS specificity, but have not addressed influences on the use of the SRS as a quantitative measure of ASD‐symptoms.
Method: Raw scores (SRS‐Raw) from parent‐completed SRS were analyzed for 2,368 probands with ASD and 1,913 unaffected siblings. Regression analyses were used to assess associations between SRS scores and demographic, language, cognitive, and behavior measures.
Results: For probands, higher SRS‐Raw were associated with greater non‐ASD behavior problems, higher age, and more impaired language and cognitive skills, as well as scores from other parent report measures of social development and ASD‐symptoms. For unaffected siblings, having more behavior problems predicted higher SRS‐Raw; male gender, younger age, and poorer adaptive social and expressive communication skills also showed small, but significant effects.
Conclusions: When using the SRS as a quantitative phenotype measure, the influence of behavior problems, age, and expressive language or cognitive level on scores must be considered. If effects of non‐ASD‐specific factors are not addressed, SRS scores are more appropriately interpreted as indicating general levels of impairment, than as severity of ASD‐specific symptoms or social impairment. Additional research is needed to consider how these factors influence the SRS’ sensitivity and specificity in large, clinical samples including individuals with disorders other than ASD.
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