A road map for identifying autism spectrum disorder: recognizing and evaluating characteristics that should raise red or "pink" flags to guide accurate differential diagnosis

Factors Associated with Confirmed and Unconfirmed Autism Spectrum Disorder Diagnosis in Children Volunteering for Research

PURPOSE

Diagnostic accuracy of autism spectrum disorder (ASD) is crucial to track and characterize ASD, as well as to guide appropriate interventions at the individual level. However, under-diagnosis, over-diagnosis, and misdiagnosis of ASD are still prevalent.

METHODS

We describe 232 children (MAge = 10.71 years; 19% female) with community-based diagnoses of ASD referred for research participation. Extensive assessment procedures were employed to confirm ASD diagnosis before study inclusion. The sample was subsequently divided into two groups with either confirmed ASD diagnoses (ASD+) or unconfirmed/inaccurate diagnoses (ASD-). Clinical characteristics differentiating the groups were further analyzed.

RESULTS

47% of children with community-based ASD diagnoses did not meet ASD criteria by expert consensus. ASD + and ASD- groups did not differ in age, gender, ethnicity, or racial make-up. The ASD + group was more likely to have a history of early language delays compared to the ASD- group; however, no group differences in current functional language use were reported by caregivers. The ASD + group scored significantly higher on ADI-R scores and on the ADOS-2 algorithm composite scores and calibrated severity scores (CSSs). The ASD- group attained higher estimated IQ scores and higher rates of psychiatric disorders, including anxiety disorder, disruptive behavior, and mood disorder diagnoses. Broadly, caregiver questionnaires (SRS-2, CCC-2) did not differentiate groups.

CONCLUSION

Increased reported psychiatric disorders in the ASD- group suggests psychiatric complexity may contribute to community misdiagnosis and possible overdiagnosis of ASD. Clinician-mediated tools (ADI-R, ADOS-2) differentiated ASD + versus ASD- groups, whereas caregiver-reported questionnaires did not.

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths

PURPOSE

Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions and support may be tailored to an individual's specific requirements. The current diagnostic procedures for ASD and ADHD require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG), an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD.

METHODS

Previous studies identified differences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS techniques.

RESULTS

Key features were selected, and machine learning models were trained to classify individuals based on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups.

CONCLUSION

The ERG to the stronger flash strength provided better separation and the high frequency dynamics (80-300 Hz) were more informative features than lower frequency components. To further improve classification a greater number of different flash strengths may be required along with a discrimination comparison to participants who meet both ASD and ADHD classifications and carry both diagnoses.

DSM-5 based algorithms for the Autism Diagnostic Interview-Revised for children ages 4-17 years

BACKGROUND

The Autism Diagnostic Interview, Revised (ADI-R) is a caregiver interview that is widely used as part of the diagnostic assessment for Autism Spectrum Disorder (ASD). Few large-scale studies have reported the sensitivity and specificity of the ADI-R algorithms, which are based on DSM-IV Autistic Disorder criteria. Kim and Lord (Journal of Autism and Developmental Disorders, 2012, 42, 82) developed revised DSM-5-based toddler algorithms, which are only applicable to children under 4 years. The current study developed DSM-5-based algorithms for children ages 4-17 years and examined their performance compared to clinical diagnosis and to the original DSM-IV-based algorithms.

METHODS

Participants included 2,905 cases (2,144 ASD, 761 non-ASD) from clinical-research databanks. Children were clinically referred for ASD-related concerns or recruited for ASD-focused research projects, and their caregivers completed the ADI-R as part of a comprehensive diagnostic assessment. Items relevant to DSM-5 ASD criteria were selected for the new algorithms primarily based on their ability to discriminate ASD from non-ASD cases. Algorithms were created for individuals with and without reported use of phrase speech. Confirmatory factor analysis tested the fit of a DSM-5-based two-factor structure. ROC curve analyses examined the diagnostic accuracy of the revised algorithms compared to clinical diagnosis.

RESULTS

The two-factor structure of the revised ADI-R algorithms showed adequate fit. Sensitivity of the original ADI-R algorithm ranged from 74% to 96%, and specificity ranged from 38% to 83%. The revised DSM-5-based algorithms performed similarly or better, with sensitivity ranging from 77% to 99% and specificity ranging from 71% to 92%.

CONCLUSIONS

In this large sample aggregated from US clinical-research sites, the original ADI-R algorithm showed adequate diagnostic validity, with poorer specificity among individuals without phrase speech. The revised DSM-5-based algorithms introduced here performed comparably to the original algorithms, with improved specificity in individuals without phrase speech. These revised algorithms offer an alternative method for summarizing ASD symptoms in a DSM-5-compatible manner.

Differential diagnosis of autism, attachment disorders, complex post-traumatic stress disorder and emotionally unstable personality disorder: A Delphi study

Individuals diagnosed with autism, attachment disorders, emotionally unstable personality disorder (EUPD) or complex post-traumatic stress disorder (CPTSD) can present with similar features. This renders differential and accurate diagnosis of these conditions difficult, leading to diagnostic overshadowing and misdiagnosis. The purpose of this study was to explore professionals' perspectives on the differential diagnosis of autism, attachment disorders and CPTSD in young people; and of autism, CPTSD and EUPD in adults. A co-produced three-round Delphi study gathered information through a series of questionnaires from 106 international professionals with expertise in assessing and/or diagnosing at least one of these conditions. To provide specialist guidance and data triangulation, working groups of experts by experience, clinicians and researchers were consulted. Delphi statements were considered to have reached consensus if at least 80% of participants were in agreement. Two hundred and seventy-five Delphi statements reached consensus. Overlapping and differentiating features, methods of assessment, difficulties encountered during differential diagnosis and suggestions for improvements were identified. The findings highlight current practices for differential diagnosis of autism, attachment disorders, CPTSD and EUPD in young people and adults. Areas for future research, clinical and service provision implications, were also identified.

Validation of an Enhanced Telehealth Platform for Toddlers at Increased Likelihood for a Diagnosis of Autism Spectrum Disorder (ASD)

Use of telehealth assessments for toddlers at increased likelihood of autism spectrum disorder (ASD) began prior to the global COVID-19 pandemic; however, the value of telehealth assessments as an alternative to in-person assessment (IPA) became clearer during the pandemic. The Naturalistic Observation Diagnosis Assessment (NODA™), previously demonstrated as a valid and reliable tool to evaluate asynchronous behaviors for early diagnosis, was enhanced to add synchronous collection of behaviors to assist clinicians in making a differential diagnosis of ASD. This study was conducted to validate the information gathered through NODA-Enhanced (NODA-E™) as compared to a gold standard IPA. Forty-nine toddlers aged 16.0-32.1 months of age, recruited through community pediatric offices and a tertiary ASD clinic, participated in both NODA-E and IPA assessments. There was high agreement between the two assessment protocols for overall diagnosis (46 of 49 cases; 93.6%; κ = .878), specific diagnostic criteria for social communication and social interaction (SCI; range 95.9-98%; κ = .918-.959), and for two of four criteria specified for restricted and repetitive behaviors (RRB; range 87.8-98%; κ = .755 and .959). There was lower agreement for two subcategories of RRBs (range 65.3-67.3%; κ = .306 and .347). NODA-E is a tool that can assist clinicians in making reliable and valid early ASD diagnoses using both asynchronous and synchronous information gathered via telehealth and offers an additional tool within a clinician's assessment toolbox.

Autism or not? A case series of evaluation decision points in child and adolescent psychological assessment

Increase in the incidence of autism spectrum disorders (ASD) and increased attention to symptoms of ASD in social media have contributed to a significant rise in referrals for neuropsychological assessment of possible ASD. Many practitioners lack specific training in the assessment of ASD and may avoid addressing these concerns, despite the frequency of those referrals. This paper reviews potential contributors to the rise in referrals and several related conditions which share some overlap with features of ASD. That is followed by descriptions of four school-aged children and adolescents referred for comprehensive evaluation of suspected ASD. The authors describe decision points in the diagnostic process for those with or without proficiency in ASD-specific testing and close with a series of recommendations for the assessment of clients with complex presentations referred for suspected ASD.

Differential Diagnosis of Autism and Other Neurodevelopmental Disorders

This article discusses the diagnostic criteria for autism spectrum disorder (ASD), as well as other neurodevelopmental disorders that may be confused with or co-occur with ASD. Practitioners involved in diagnostic assessment of ASD must be well versed in the features that differentiate ASD from other conditions and be familiar with how co-occurring conditions may manifest in the context of ASD. ASD symptoms present differently across development, underscoring the need for training about typical developmental expectations for youth. Periodic reevaluations throughout development are also important because support needs for individuals with autism change over time.

Learning to Fuse Multiple Brain Functional Networks for Automated Autism Identification

Functional connectivity network (FCN) has become a popular tool to identify potential biomarkers for brain dysfunction, such as autism spectrum disorder (ASD). Due to its importance, researchers have proposed many methods to estimate FCNs from resting-state functional MRI (rs-fMRI) data. However, the existing FCN estimation methods usually only capture a single relationship between brain regions of interest (ROIs), e.g., linear correlation, nonlinear correlation, or higher-order correlation, thus failing to model the complex interaction among ROIs in the brain. Additionally, such traditional methods estimate FCNs in an unsupervised way, and the estimation process is independent of the downstream tasks, which makes it difficult to guarantee the optimal performance for ASD identification. To address these issues, in this paper, we propose a multi-FCN fusion framework for rs-fMRI-based ASD classification. Specifically, for each subject, we first estimate multiple FCNs using different methods to encode rich interactions among ROIs from different perspectives. Then, we use the label information (ASD vs. healthy control (HC)) to learn a set of fusion weights for measuring the importance/discrimination of those estimated FCNs. Finally, we apply the adaptively weighted fused FCN on the ABIDE dataset to identify subjects with ASD from HCs. The proposed FCN fusion framework is straightforward to implement and can significantly improve diagnostic accuracy compared to traditional and state-of-the-art methods.

Effectiveness of Online-Delivered Project ImPACT for Children With ASD and Their Parents: A Pilot Study During the COVID-19 Pandemic

OBJECTIVE

During the COVID-19 pandemic, face-to-face intervention services for families of children with autism spectrum disorder (ASD) were limited. This study aimed to evaluate the effectiveness of an 8-week, online-delivered Project ImPACT program for children with ASD and their parents in China during the COVID-19 pandemic.

METHODS

A pilot non-randomized study with a waitlist control group was conducted in 68 children with ASD and their parents in the Department of Developmental and Behavioral Pediatrics between April 15, 2020 and March 19, 2021. Participants were allocated to either the intervention (IG) or the waitlist group (WLG) according to their order of recruitment. Parents in the IG immediately received 8 weeks of the online-delivered Project ImPACT program, and the WLG received the same program with a delay when the IG had completed all sessions. Participants in both groups received treatment as usual during the research period.

RESULTS

The online-delivered Project ImPACT program significantly improved the parent-reported social communication skills of children with ASD. Furthermore, parent's involvement in the training program produced a collateral reduction in parenting stress and an increase in perceived competence in the parental role. Parents rated the program acceptable in terms of curriculum schedule, session content, homework assignments, and therapist feedback.

CONCLUSIONS

The 8-week, online-delivered Project ImPACT program is a feasible and effective social skill training program for families of children with ASD in China during the COVID-19 pandemic. Due to the methodological limitations, randomized controlled studies with larger sample sizes are suggested to provide more solid evidence.

Survey of training experiences and clinical practice in assessment for autism spectrum disorder by neuropsychologists

OBJECTIVE

The primary purpose of this study is to better understand current practices in the assessment of autism spectrum disorder (ASD) by neuropsychologists.

METHODS

A 21-item survey regarding ASD assessment beliefs and practices was sent via email through neuropsychology listservs. The survey was accessed by 445 licensed psychologists who identified as neuropsychologists. A total of 367 surveys were deemed usable for data analysis. Descriptive statistics were used to characterize the full sample. Exploratory analyses were conducted between groups of interest, including primary population served (pediatric, adult, or lifespan), primary practice setting (medical center vs. private practice) and years in practice (< 5 years, 5 to 14 years, or 15+ years).

RESULTS

Respondents were well-distributed across age range, years in practice, primary practice setting, and primary practice location. Almost all respondents (most of whom self-identified as pediatric-focused clinicians) believe that neuropsychologists should be able to competently rule in or out ASD and most received training in ASD assessment. Approximately 40% of respondents endorsed wanting more training in ASD assessment to increase their competence and confidence in making this differential diagnosis. Minimal differences in ASD beliefs and assessment practices were seen across years of practice or primary practice setting. Pediatric and lifespan clinicians had similar experience with ASD assessment practices, and both generally differed from adult clinicians.

CONCLUSIONS

Our findings suggest many respondents desire further specialty ASD training for neuropsychologists. Additionally, the large majority of respondents indicated that future neuropsychologists should receive training in ASD assessment during graduate school, internship and/or post-doctoral fellowship.