Universal screening is recommended to reduce the age of diagnosis for autism spectrum disorder (ASD). However, there are insufficient data on children who screen negative and no study of outcomes ...from truly universal screening. With this study, we filled these gaps by examining the accuracy of universal screening with systematic follow-up through 4 to 8 years.
Universal, primary care-based screening was conducted using the Modified Checklist for Autism in Toddlers with Follow-Up (M-CHAT/F) and supported by electronic administration and integration into electronic health records. All children with a well-child visit (1) between 16 and 26 months, (2) at a Children's Hospital of Philadelphia site after universal electronic screening was initiated, and (3) between January 2011 and July 2015 were included (
= 25 999).
Nearly universal screening was achieved (91%), and ASD prevalence was 2.2%. Overall, the M-CHAT/F's sensitivity was 38.8%, and its positive predictive value (PPV) was 14.6%. Sensitivity was higher in older toddlers and with repeated screenings, whereas PPV was lower in girls. Finally, the M-CHAT/F's specificity and PPV were lower in children of color and those from lower-income households.
Universal screening in primary care is possible when supported by electronic administration. In this "real-world" cohort that was systematically followed, the M-CHAT/F was less accurate in detecting ASD than in previous studies. Disparities in screening rates and accuracy were evident in traditionally underrepresented groups. Future research should focus on the development of new methods that detect a greater proportion of children with ASD and reduce disparities in the screening process.
Background: Although a diagnosis of autism spectrum disorder (ASD) appears to be stable in children as young as age three, few studies have explored stability of a diagnosis in younger children. ...Predictive value of diagnostic tools for toddlers and patterns of symptom change are important considerations for clinicians making early diagnoses. Most findings come from high‐risk samples, but reports on children screened in community settings are also needed.
Methods: Stability of diagnosis and Autism Diagnostic Observation Schedule – Toddler Module (ADOS‐T) classifications and scores was examined across two time points in a sample of 82 children identified through the FIRST WORDS® Project. Children received two comprehensive diagnostic evaluations at average ages of 19.39 (SD = 2.12) and 36.89 (SD = 3.85) months.
Results: Stability was 100% when confirming and ruling out a diagnosis of ASD based on a comprehensive diagnostic evaluation that included clinic and home observations, although diagnosis was initially deferred for 17% of the sample. Receiver Operating Characteristic curves revealed excellent sensitivity and acceptable specificity for the ADOS‐T compared to concurrent diagnosis. Logistic regressions indicated good predictive value of initial ADOS‐T scores for follow‐up diagnosis. Finally, both ASD and Non‐ASD children demonstrated a decrease in Social Affect scores (i.e., improvement), whereas children with ASD demonstrated an increase in Restricted and Repetitive Behavior scores (i.e., worsening), changes that were accounted for by nonverbal developmental level in mixed model analyses.
Conclusions: Short‐term stability was documented for children diagnosed at 19 months on average, although a minority of children initially showed unclear diagnostic presentations. Findings highlight utility of the ADOS‐T in making early diagnoses and predicting follow‐up diagnoses. Children with ASD demonstrated improvement in social communication behaviors and unfolding of repetitive behaviors, suggesting that certain early patterns of change in symptoms may be characteristic of ASD.
Although the American Academy of Pediatrics recommends screening for autism spectrum disorder (ASD) for all young children, disparities in ASD diagnosis and intervention in minority children persist. ...One potential contributor to disparities could be whether physicians take different actions after an initial positive screen based on patient demographics. This study estimated factors associated with physicians completing the follow-up interview for the Modified Checklist for Autism in Toddlers with Follow-up (M-CHAT-F), and referring children to diagnostic services, audiology, and Early Intervention (EI) immediately after a positive screen.
Children seen in a large primary care network that has implemented universal ASD screening were included if they screened positive on the M-CHAT parent questionnaire during a 16-30 month well child visit (N = 2882). Demographics, screening results, and referrals were extracted from the electronic health record.
Children from lower-income families or on public insurance were more likely to have been administered the follow-up interview. Among children who screened positive, 26% were already in EI, 31% were newly referred to EI, 11% were referred each to audiology and for comprehensive ASD evaluation. 40.2% received at least one recommended referral; 3.7% received all recommended referrals. In adjusted multivariable models, male sex, white versus black race, living in an English-speaking household, and having public insurance were associated with new EI referral. Male sex, black versus white race, and lower household income were associated with referral to audiology. Being from an English-speaking family, white versus Asian race, and lower household income were associated with referral for ASD evaluation. A concurrent positive screen for general developmental concerns was associated with each referral.
We found low rates of follow-up interview completion and referral after positive ASD screen, with variations in referral by sex, language, socio-economic status, and race. Understanding pediatrician decision-making about ASD screening is critical to improving care and reducing disparities.
Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, ...cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations.
We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score.
We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP.
The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant ...challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients' clinical narratives.
To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method.
Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders.
Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making.
The ability to selectively respond to one's own name is important for social and language development, and is disrupted in atypically developing populations (e.g., autism spectrum disorder). Research ...with typically developing samples using event-related potentials (ERPs) has demonstrated that the subject's own name (SON) is differentiated from other stimuli at both early sensory and later cognitive stages of auditory processing. While neural indices of response to name have been researched extensively in adults, no such studies have been conducted with typically developing preschool children or children with autism spectrum disorder (ASD). The present study investigated ERP response to name in a sample of typically developing (TD) preschoolers (n = 19; mean age = 4.3 years) as well as a small, exploratory comparison group of preschoolers with ASD (n = 13; mean age = 4.4 years). TD preschoolers exhibited significantly greater negativity to SON over frontal regions than to an unfamiliar nonsense name, consistent with the adult SON negativity component. This component was present whether the name was spoken by a parent or an unfamiliar adult, suggesting that it reflects SON-specific processing rather than broad self-relevant information processing. Comparing preschoolers with ASD to the TD children revealed a significant SON negativity component across both groups. The amplitude of the SON negativity response was significantly correlated with social variables in the ASD group, though these correlations did not survive correction for multiple comparisons. This study is the first to demonstrate the presence of the SON component in preschool children with and without ASD.
The present study examined the factor structure of autism symptoms in toddlers, to aid understanding of the phenotype during the developmental period that represents the earliest manifestations of ...autism symptoms. This endeavor is particularly timely, given changes in symptom structure from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) to the recently released Fifth Edition (DSM-5).
Factor structure was examined in a sample of toddlers between 12 and 30 months of age (mean = 20.37 months, SD = 3.32 months) diagnosed with autism spectrum disorder (ASD) and recruited from community settings or referred for evaluation (N = 237). Confirmatory factor analyses were conducted comparing the relative fit of 4 distinct, previously proposed and validated models: DSM-5, DSM-IV, 1-factor, and an alternative 3-factor model proposed by van Lang et al.
Findings revealed that the 1-factor model provided the poorest fit, followed by the DSM-IV model and the van Lang et al. model. The DSM-5 model provided the best fit to the data relative to other models and good absolute fit. Indicators for the confirmatory factor analyses, drawn from the Autism Diagnostic Observation Schedule-Toddler Module (ADOS-T), loaded strongly onto the DSM-5 Social Communication and Social Interaction factor and more variably onto the DSM-5 Restricted/Repetitive Language and Behavior factor.
Results indicate that autism symptoms in toddlers, as measured by the ADOS-T, are separable and best deconstructed into the 2-factor DSM-5 structure, supporting the reorganization of symptoms in the DSM-5. Consistency of the present results in toddlers with previous studies in older children and adults suggests that the structure of autism symptoms may be similar throughout development.
Background and Aims
Children with autism spectrum disorder (ASD) exhibit a heterogeneous clinical phenotype with wide variability in their language and intellectual profiles that complicates efforts ...at early detection. There is limited research examining observational measures to characterize differences between young children with and without ASD and co-occurring language delay (LD) and global developmental delay (GDD). The first aim of this study was to compare early social communication measured in the second year of life in children diagnosed at age 3 with ASD, developmental delays (DD), and typical development (TD). The second aim was to compare early social communication in six subgroups of children: ASD, ASD+LD, ASD+GDD, LD, GDD, and TD. Our third aim was to determine the collective and unique contributions of early social communication to predict verbal and nonverbal developmental outcomes at three years of age for children with and without ASD.
Methods
Analyses of covariance controlling for maternal education were employed to examine group differences in social communication in 431 toddlers recruited through screening in primary care. Multiple linear regression analyses were conducted to evaluate associations between the Communication and Symbolic Behavior Scales (CSBS) Behavior Sample composite standard scores and Mullen Scales of Early Learning T scores for children with and without ASD.
Results
Distinct patterns of early social communication were evident by 20 months. Children with TD differed significantly from children with ASD and DD on all three CSBS Behavior Sample composites. Children with ASD had significantly lower scores than those with DD and TD on the social and symbolic composites. Among the six subgroups, all three composites of the CSBS Behavior Sample differentiated children with TD from all other subgroups. Children with ASD+GDD scored significantly lower than all other subgroups on social and symbolic composites. Patterns of social communication emerged for children with and without ASD, which held among subgroups divided by developmental level. The CSBS Behavior Sample social and symbolic composites contributed unique variance in predicting developmental outcomes in both groups. The speech composite contributed unique variance to expressive language, receptive language, and visual reception in children without ASD, and contributed uniquely to expressive language only for children with ASD.
Conclusions
The CSBS Behavior Sample, an observational measure for children aged 12–24 months, detected social communication delays and explained a significant amount of variance in verbal and nonverbal outcomes a year later in this large sample of young children grouped by ASD diagnosis and developmental level.
Implications
In light of the continued search for early predictors of ASD and developmental delay, our findings underscore the importance of monitoring early social communication skills, including the expression of emotions, eye gaze, gestures, rate of communication, joint attention, understanding words, and object use in play. There is a need for clinical utility of screening and evaluation tools that can detect social communication delays in very young children. This would enable intervention for infants and toddlers who show social communication delays which may be early signs for ASD or other DD, rather than waiting to confirm a formal diagnosis.
BackgroundCreatine transporter deficiency (CTD) is a rare X-linked disorder of creatine transport caused by pathogenic variants in SLC6A8 (Xq28). The disorder is marked by developmental delay, ...especially speech delay. The biomarkers Aβ40, Aβ42 and total tau are abnormal in Alzheimer disease (AD), a common neurodegenerative disorder pathologically characterized by Aβ peptide containing amyloid plaques and tau neurofibrillary tangles. Although CTD results in neuronal energy deficiency, the pathological processes underlying the CTD phenotype are not fully characterized.MethodsCerebral spinal fluid (CSF) was collected as an optional part of a natural history study of CTD. Aβ40, Aβ42 and total tau levels were quantified in CSF from individuals with CTD and from age-appropriate comparison samples. Neuro3-Plex enzyme-linked immunoassay was performed on a Quanterix SR-X instrument. The Vineland Adaptive Behavior Scale, 3rd Edition was used to determine an overall Adaptive Behavior Composite (ABC) standard score.ResultsCSF from 12 individuals with CTD and 23 age appropriate non-CTD comparison samples were analyzed. We found that levels of total tau t(32) = 4.05, p = 0.0003, Aβ40 t(31) = 6.11, p < 0.0001, and Aβ42 t(32) = 3.20, p = 0.003 were elevated in the participants with CTD relative to the comparison group. Additionally, except for one individual that we considered an outlier, all three biomarkers correlated inversely with the adaptive behavior score (total tau: ρ = -0.60 -0.88, 0.005; Aβ40: ρ = -0.67 -0.91, -0.12; Aβ42: ρ = -0.62 -0.89, -0.02).ConclusionWe describe here the novel finding of elevated protein biomarkers in the CSF of individuals with CTD. Aβ40, Aβ42 and total tau are markedly elevated in individuals with CTD compared to comparison samples, and increased levels of these biomarkers inversely correlated with ABC scores. We hypothesize that elevated CSF levels of Aβ40 and Aβ42 are due to cellular energy deficiency. Elevated CSF total tau levels may indicate ongoing neuronal damage. The observed inverse correlation of Vineland ABC scores with increased biomarker levels needs to be confirmed in a larger CTD cohort; however, our observation of increased Aβ40, Aβ42 and total tau levels in CSF from individuals with CTD may provide insight into pathological mechanisms contributing to the CTD phenotype and may prove useful as supportive data in future therapeutic trials.
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