Accuracy of phenotyping children with autism based on parent report: what specifically do we gain phenotyping “rapidly”?

Warren, Zachary; Vehorn, Alison; Dohrmann, Elizabeth Howell; Nicholson, Amy; Sutcliffe, James S.; Veenstra‐VanderWeele, Jeremy

doi:10.1002/aur.230

Cited by 19 publications

(18 citation statements)

References 21 publications

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“…At the level of clinical screening, efforts at rapid screening for ASD by parent‐report questionnaires tend to have higher sensitivity than specificity (Charman et al, ; Warren et al, ); however, when clinicians are asked to make rapid ASD referral decisions for preschool age children (i.e., based on 10‐min observations), specificity for ultimate ASD diagnosis is higher than sensitivity (i.e., 0.82 vs. 0.61; Gabrielsen et al, ). Perhaps if frank features can be formalized and quantified, they can be better incorporated into screening measures that yield stronger overall diagnostic classification power.…”

Section: Discussionmentioning

confidence: 99%

“Frank” presentations as a novel research construct and element of diagnostic decision‐making in autism spectrum disorder

Marchena

Miller

2016

Autism Research

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Scientific Abstract Many individuals with ASD have a distinctive behavioral presentation that is recognizable within moments, a phenomenon we call “frank” ASD. This phenomenon has been discussed informally for decades, perhaps as “classic” ASD; however, there is no unitary “classic” presentation, and classic autism does not seem to correspond to level of functioning. Thus, neither “frank” nor “classic” autism has been delineated or studied as a research construct. To initiate the empirical study of frank ASD, we surveyed 151 clinicians, from a range of disciplines that diagnose ASD, about this phenomenon. Respondents completed a 13-item questionnaire about frank ASD, which was analyzed using a mixed-methods approach. 97% of respondents were familiar with the phenomenon. Respondents estimated that 40% of the ASD population has a frank presentation. Respondents reported the most highly specific behaviors associated with frank presentations were a general sense of impaired reciprocity, quality of eye contact, atypical vocal prosody, presence of motor mannerisms, and atypical gait or posture. In general, respondents reported detecting frank features rapidly, with the majority forming their impressions within the first ten minutes of interaction or observation. Although unstudied empirically, “frank” presentations of ASD are familiar to diagnosing clinicians, and appear to be based on behaviors both central to ASD diagnostic criteria (e.g., impaired reciprocity), and absent from diagnostic criteria (e.g., atypical gait or posture). We discuss these findings within the context of diagnostic decision-making and behavioral phenotyping of ASD.

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Section: Discussionmentioning

confidence: 99%

“Frank” presentations as a novel research construct and element of diagnostic decision‐making in autism spectrum disorder

Marchena

Miller

2016

Autism Research

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“…Taken together, these results indicate that SRS scores are highly influenced by behavior problems. It doi:10.1111/j.1469-7610.2012.02589.x Factors influencing the SRS remains unclear whether this is because items intended to capture social impairments (e.g., poor eye contact, difficulty with peers) lack diagnostic specificity, or whether parents interpret questions as describing qualitatively different behaviors than the ASD-symptoms that items were intended to assess (Veenstra-VanderWeele & Warren, 2012). Thus, it may be appropriate to interpret SRS scores as reflecting parents' perception of their child's overall level of impairment (which may be influenced by developmental difficulties and behavior problems, as well as ASD-symptoms), rather than as a measure of severity of core-ASD-features.…”

Section: Discussionmentioning

confidence: 99%

“…Recent discussions regarding developmental screening and assessments have called for better understanding of factors influencing parent report (Aylward, 2009;Warren et al, 2012). Often, parentfactors, such as education level and frame of reference, are acknowledged as limitations and weighed against the relative benefit of efficiency and cost-effectiveness of questionnaires compared to interview or observational measures requiring more time and highly trained clinicians.…”

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confidence: 99%

Factors influencing scores on the social responsiveness scale

Hus

Bishop

Gotham

et al. 2012

Child Psychology Psychiatry

245

202

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Background The Social Responsiveness Scale (SRS) is a parent-completed screening questionnaire often used to measure 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. Further 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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“…An approach such as the one here could require less specialty training for administration and delivery and enable a wider reach to the population at risk, particularly if delivered through mobilized channels (internet and handheld devices) and used for prioritization of cases for subsequent clinical evaluation and diagnosis. However, work has shown performance problems of rapid assessment tools like the Social Responsiveness Scale and Social Communication Questionnaire [13] especially with differentiation of autism spectrum cases from other neurodevelopmental conditions and learning delays [19]–[21], as well as problems with agreement in the clinical diagnosis even when full ADI-R and other instruments are used in the diagnostic process [10]. Future work must therefore involve further exploration of the clinical potentials of this approach through prospective studies, being careful to consider the additional limitations covered in the section below.…”

Section: Discussionmentioning

confidence: 99%

Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism

et al. 2012

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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.

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Accuracy of phenotyping children with autism based on parent report: what specifically do we gain phenotyping “rapidly”?

Cited by 19 publications

References 21 publications

“Frank” presentations as a novel research construct and element of diagnostic decision‐making in autism spectrum disorder

“Frank” presentations as a novel research construct and element of diagnostic decision‐making in autism spectrum disorder

Factors influencing scores on the social responsiveness scale

Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism

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