Autism Diagnostic Observation Schedule (ADOS-2) elevations in a clinical sample of children and adolescents who do not have autism: Phenotypic profiles of false positives

Greene, Rachel K.; Vasile, Iulia; Bradbury, Kathryn; Olsen, Aarika; Duvall, Susanne W.

doi:10.1080/13854046.2021.1942220

Cited by 19 publications

(13 citation statements)

References 47 publications

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“…The considerable symptom overlap of ASD with depressive and anxiety disorders entails the risk of false-positive evaluations in females. Although the ADOS-2 shows high sensitivity (0.91; [ 23 ], p. 243) for detecting autism versus non-spectrum cases, emerging research shows that it may be less accurate in detecting ASD in individuals with complex psychiatric presentations [ 36 ]. Moreover, the observation in the current sample that the prevalence of ASD diagnoses increases with age (45.9% of all adolescent/ adult females, but only 33.8% of the younger sample, received an ASD diagnosis) underlines the need to carefully consider differential, potentially overlapping diagnoses during the diagnostic process.…”

Section: Discussionmentioning

confidence: 99%

Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder

et al. 2022

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Background Although autism spectrum disorder (ASD) is a common developmental disorder, our knowledge about a behavioral and neurobiological female phenotype is still scarce. As the conceptualization and understanding of ASD are mainly based on the investigation of male individuals, females with ASD may not be adequately identified by routine clinical diagnostics. The present machine learning approach aimed to identify diagnostic information from the Autism Diagnostic Observation Schedule (ADOS) that discriminates best between ASD and non-ASD in females and males. Methods Random forests (RF) were used to discover patterns of symptoms in diagnostic data from the ADOS (modules 3 and 4) in 1057 participants with ASD (18.1% female) and 1230 participants with non-ASD (17.9% % female). Predictive performances of reduced feature models were explored and compared between females and males without intellectual disabilities. Results Reduced feature models relied on considerably fewer features from the ADOS in females compared to males, while still yielding similar classification performance (e.g., sensitivity, specificity). Limitations As in previous studies, the current sample of females with ASD is smaller than the male sample and thus, females may still be underrepresented, limiting the statistical power to detect small to moderate effects. Conclusion Our results do not suggest the need for new or altered diagnostic algorithms for females with ASD. Although we identified some phenotypic differences between females and males, the existing diagnostic tools seem to sufficiently capture the core autistic features in both groups.

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Section: Discussionmentioning

confidence: 99%

Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder

et al. 2022

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“…Based on all this information, individuals are categorized according to the psychiatric multiaxial schema (12,13). Within this schema, individuals will be categorized in six axes: (1) psychiatric syndromes; (2) Although the ADOS/2 offers good sensitivity and specificity values (14,15), there are frequent cases in which the diagnosis is either missed (false negative) or given incorrectly (false positive)-false particularly in the retrospective consideration of changes in phenomenology and thus diagnoses during development over several years (16). There are several potential reasons facilitating false-positive or false-negative cases, like under-resourced familial or educational environment, presence of only subtle ASD symptoms, and presence or absence of coexisting or differential diagnoses [e.g., anxiety disorder (17), ADHD (18), or psychosis (19)].…”

Section: Introductionmentioning

confidence: 99%

“…As the accuracy of psychiatric diagnoses affects both the preferred therapy choice and the respective outcome ( 24 ), and implications from obtaining a lifelong diagnosis such as ASD can be severe, the diagnostic accuracy should be as high as possible. Therefore, clinicians should be aware of specific individual characteristics, which are concomitant with different levels of IQ and which may lead to increased or decreased ADOS/2 scores ( 16 ).…”

Section: Introductionmentioning

confidence: 99%

Abilities and Disabilities—Applying Machine Learning to Disentangle the Role of Intelligence in Diagnosing Autism Spectrum Disorders

et al. 2022

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ObjectiveAlthough autism spectrum disorder (ASD) is a relatively common, well-known but heterogeneous neuropsychiatric disorder, specific knowledge about characteristics of this heterogeneity is scarce. There is consensus that IQ contributes to this heterogeneity as well as complicates diagnostics and treatment planning. In this study, we assessed the accuracy of the Autism Diagnostic Observation Schedule (ADOS/2) in the whole and IQ-defined subsamples, and analyzed if the ADOS/2 accuracy may be increased by the application of machine learning (ML) algorithms that processed additional information including the IQ level.MethodsThe study included 1,084 individuals: 440 individuals with ASD (with a mean IQ level of 3.3 ± 1.5) and 644 individuals without ASD (with a mean IQ level of 3.2 ± 1.2). We applied and analyzed Random Forest (RF) and Decision Tree (DT) to the ADOS/2 data, compared their accuracy to ADOS/2 cutoff algorithms, and examined most relevant items to distinguish between ASD and Non-ASD. In sum, we included 49 individual features, independently of the applied ADOS module.ResultsIn DT analyses, we observed that for the decision ASD/Non-ASD, solely one to four items are sufficient to differentiate between groups with high accuracy. In addition, in sub-cohorts of individuals with (a) below (IQ level ≥4)/ID and (b) above average intelligence (IQ level ≤ 2), the ADOS/2 cutoff showed reduced accuracy. This reduced accuracy results in (a) a three times higher risk of false-positive diagnoses or (b) a 1.7 higher risk for false-negative diagnoses; both errors could be significantly decreased by the application of the alternative ML algorithms.ConclusionsUsing ML algorithms showed that a small set of ADOS/2 items could help clinicians to more accurately detect ASD in clinical practice across all IQ levels and to increase diagnostic accuracy especially in individuals with below and above average IQ level.

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“…The current data suggest anxiety and mood disorders are particularly important to understanding who may require more comprehensive in-person testing. Research suggests that ASD shares many symptoms with other psychiatric conditions, including anxiety and depression, making the differential diagnosis challenging (e.g., Greene et al, 2021 ; Wittkopf et al, 2021 ). Specifically, it can be difficult to accurately assess social communication deficits that may differentiate ASD and anxiety and mood disorders (Wittkopf et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Identifying Patient Characteristics to Understand Which Children May Receive Diagnostic Clarity in a Virtual Autism Spectrum Disorder Evaluation

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Greene

et al. 2022

J Autism Dev Disord

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Given long waitlists for autism spectrum disorder (ASD) evaluation coupled with the COVID-19 pandemic, it is crucial to triage patients to services they are likely to receive diagnostic clarity (i.e., virtual, in-person evaluation). Participants attended a virtual ASD assessment. A subset also attended in-person evaluation. Results suggest younger children with educational services for ASD may benefit from virtual assessment while older patients with a history of psychiatric conditions may benefit from in-person evaluation. An ASD symptom severity tool related to virtual and in-person diagnostic clarity. Family history of ASD related to in-person diagnosis while other variables (e.g., age, educational services) did not. The study suggests patient characteristics may be used to determine for whom virtual ASD assessment may be appropriate.

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Autism Diagnostic Observation Schedule (ADOS-2) elevations in a clinical sample of children and adolescents who do not have autism: Phenotypic profiles of false positives

Cited by 19 publications

References 47 publications

Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder

Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder

Abilities and Disabilities—Applying Machine Learning to Disentangle the Role of Intelligence in Diagnosing Autism Spectrum Disorders

Identifying Patient Characteristics to Understand Which Children May Receive Diagnostic Clarity in a Virtual Autism Spectrum Disorder Evaluation

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