Replication study for ADOS-2 cut-offs to assist evaluation of autism spectrum disorder

Community testing practices for autism within the autism and developmental disabilities monitoring network

BACKGROUND

No data exist at the population level on what tests are used to aid in the diagnosis of autism spectrum disorder in community practice.

OBJECTIVES

To describe autism spectrum disorder testing practices to inform autism spectrum disorder identification efforts.

METHODS

Data are from the Autism and Developmental Disabilities Monitoring Network, a multi-site surveillance system reporting prevalence estimates and characteristics of 8-year-old children with autism spectrum disorder. Percentages of children with autism spectrum disorder who received any autism spectrum disorder test or a 'gold standard' test were calculated by site, sex, race, median household income, and intellectual ability status. Risk ratios were calculated to compare group differences.

RESULTS

Of 5058 8-year-old children with autism spectrum disorder across 11 sites, 3236 (64.0%) had a record of any autism spectrum disorder test and 2136 (42.2%) had a 'gold standard' ADOS or ADI-R test. Overall, 115 children (2.3%) had both the ADOS and ADI-R in their records. Differences persisted across race, median household income, and intellectual ability status. Asian/Pacific Islander children had the highest percent receiving any ASD test (71.8%; other groups range: 57.4-66.0%) and White children had the highest percent receiving 'gold standard' tests (46.4%; other groups range: 35.6-43.2%). Children in low-income neighbourhoods had a lower percent of any test (62.5%) and 'gold standard' tests (39.4%) compared to medium (70.2% and 47.5%, respectively) and high (69.6% and 46.8%, respectively) income neighbourhoods. Children with intellectual disability had a lower percent of any ASD test (81.7%) and 'gold standard' tests (52.6%) compared to children without intellectual disability (84.0% and 57.6%, respectively).

CONCLUSIONS

Autism spectrum disorder testing practices vary widely by site and differ by race and presence of co-occurring intellectual disability, suggesting opportunities to standardise and/or improve autism spectrum disorder identification practices.

Psychometric Evaluation of the Autism Spectrum Rating Scales (6-18 Years Parent Report) in a Clinical Sample

ASD is a neurodevelopmental disorder impacting 1 in 44 children and early identification of children with ASD is critical for the intervention. Several screening measures have been developed for early identification, including the Autism Spectrum Rating Scales, 6-18 years Parent Report (ASRS). The ASRS has been understudied, and the current study assessed the validity of the ASRS in a clinical sample of 490 children at a tertiary ASD-specialty clinic. Results indicated that the ASRS demonstrated favorable sensitivity, but poor specificity. True positive screening results were more likely to occur for children with a multiracial background, while they were less likely to occur for children with a high social capital. Overall, though the ASRS has clinical utility as a screening measure, it did not perform effectively to differentiate ASD from Non-ASD clinical disorders.

Risk factors and clinical characteristics of autism spectrum disorder with regression in China

Autism spectrum disorder with regression (ASD-R) is characterized by the loss of previously acquired skills during the initial year of life. This study aimed to investigate the clinical characteristics, patterns of regression, and potential risk factors associated with ASD-R in the Chinese Han population. A case-control study was conducted between September 2020 and March 2022. A total of 186 children were enrolled, including 58 children with ASD-R, 70 with ASD without regression (ASD-NR), and 58 typically developing children. Demographic information, clinical characteristics, and potential risk factors related to ASD-R were assessed using a combination of questionnaires, interviews, and physician assessments. The results revealed that children with ASD-R exhibited more severe impairments in social communication and stereotyped behaviors compared with those with ASD-NR. Language regression, constituting 40% of cases within the ASD-R group, was found to be the most common type of regression. Furthermore, our analysis revealed that fever (OR = 4.01, 95% CI: 1.26-12.76) and diarrhea (OR = 6.32, 95% CI: 1.38-29.03) were identified as significant risk factors for ASD-R. These findings contribute to our understanding of the heterogeneity of ASD and highlight the importance of considering immune responses and gastrointestinal factors in the etiology of ASD-R.

A Machine Learning Approach to the Diagnosis of Autism Spectrum Disorder and Multi-Systemic Developmental Disorder Based on Retrospective Data and ADOS-2 Score

Early and accurate diagnosis of autism spectrum disorders (ASD) and tailored therapeutic interventions can improve prognosis. ADOS-2 is a standardized test for ASD diagnosis. However, owing to ASD heterogeneity, the presence of false positives remains a challenge for clinicians. In this study, retrospective data from patients with ASD and multi-systemic developmental disorder (MSDD), a term used to describe children under the age of 3 with impaired communication but with strong emotional attachments, were tested by machine learning (ML) models to assess the best predictors of disease development as well as the items that best describe these two autism spectrum disorder presentations. Maternal and infant data as well as ADOS-2 score were included in different ML testing models. Depending on the outcome to be estimated, a best-performing model was selected. RIDGE regression model showed that the best predictors for ADOS social affect score were gut disturbances, EEG retrievals, and sleep problems. Linear Regression Model showed that term pregnancy, psychomotor development status, and gut disturbances were predicting at best for the ADOS Repetitive and Restricted Behavior score. The LASSO regression model showed that EEG retrievals, sleep disturbances, age at diagnosis, term pregnancy, weight at birth, gut disturbances, and neurological findings were the best predictors for the overall ADOS score. The CART classification and regression model showed that age at diagnosis and weight at birth best discriminate between ASD and MSDD.