INTRODUCTION: This study aimed to examine patterns of diagnosis, cognitive and adaptive functioning, and school placement outcomes in autistic children in Singapore, and to assess earlier predictive factors of cognitive outcomes. METHOD: Retrospective data were extracted from medical records of a specialist developmental paediatrics service for children born in 2008-2011 and referred to the autism clinic or were given a diagnosis of autism. Data items included demographic data, diagnostic methods, psychological assessment results, early intervention attendance and school placement outcomes. RESULTS: A total of 2124 children (82.6% male; 66.4% Chinese, 13.4% Malay, 9.8% Indian and 10.5% Others) were diagnosed with autism from the 4 birth-year cohorts. The mean (SD) age of the first clinical diagnosis of autism was 3.56 (1.14) years, with 81.0% of children receiving a concordant initial clinical diagnosis. A total of 1811 (85.2%) had a formal diagnostic assessment using the Autism Diagnostic Observation Schedule (ADOS) at a mean (SD) age of 4.16 (1.11) years. Of 1326 with cognitive and adaptive assessment results, 16.6% had mild and 19.8% had moderate-severe cognitive impairment. Of 1483 with school placement outcomes, 45.9% went to mainstream schools, 21.8% entered SPED schools offering the national curriculum and 32.3% required customised curriculum SPED schools. Logistic regression showed that factors predicting intellectual impairment included higher ADOS scores (aOR 95% CI 1.13 [1.08-1.19] for Comm+SI total and 1.53 [1.33-1.75] for SBRI total), higher social communication level of support (based on the DSM-5 criteria) (aOR [95% CI] 2.14 [1.10-4.16] for level 2 and 14.94 [5.77-38.64] for level 3), and minority race (aOR [95% CI] 2.82 [1.52-5.20] for Malay, 5.19 [2.36-11.44] for Indian, and 4.54 [1.91-10.79] for Others). CONCLUSION These findings could guide policymakers and practitioners worldwide to strategically allocate diagnostic, intervention and educational resources, maximising developmental outcomes for autistic children across diverse settings.
Humans ; Singapore/epidemiology* ; Male ; Female ; Retrospective Studies ; Child, Preschool ; Autistic Disorder/complications* ; Child ; Early Intervention, Educational ; Cognitive Dysfunction/diagnosis* ; Cognition

Extraction and analysis of electroencephalogram (EEG) signal characteristics of patients with autism spectrum disorder (ASD) is of great significance for the diagnosis and treatment of the disease. Based on recurrence quantitative analysis (RQA)method, this study explored the differences in the nonlinear characteristics of EEG signals between ASD children and children with typical development (TD). In the experiment, RQA method was used to extract nonlinear features such as recurrence rate (RR), determinism (DET) and length of average diagonal line (LADL) of EEG signals in different brain regions of subjects, and support vector machine was combined to classify children with ASD and TD. The research results show that for the whole brain area (including parietal lobe, frontal lobe, occipital lobe and temporal lobe), when the three feature combinations of RR, DET and LADL are selected, the maximum classification accuracy rate is 84%, the sensitivity is 76%, the specificity is 92%, and the corresponding area under the curve (AUC) value is 0.875. For parietal lobe and frontal lobe (including parietal lobe, frontal lobe), when the three features of RR, DET and LADL are combined, the maximum classification accuracy rate is 82%, the sensitivity is 72%, and the specificity is 92%, which corresponds to an AUC value of 0.781. The research in this paper shows that the nonlinear characteristics of EEG signals extracted based on RQA method can become an objective indicator to distinguish children with ASD and TD, and combined with machine learning methods, the method can provide auxiliary evaluation indicators for clinical diagnosis. At the same time, the difference in the nonlinear characteristics of EEG signals between ASD children and TD children is statistically significant in the parietal-frontal lobe. This study analyzes the clinical characteristics of children with ASD based on the functions of the brain regions, and provides help for future diagnosis and treatment.
Autism Spectrum Disorder/diagnosis* ; Autistic Disorder ; Brain ; Child ; Electroencephalography ; Humans ; Recurrence

OBJECTIVE: To evaluate the value of autism screening checklists in the early identification of autism spectrum disorder (ASD). METHODS: A total of 2 571 children who attended the Children's Hospital of Chongqing Medical University and completed autism screening and diagnostic test were enrolled as subjects, among whom 2 074 were diagnosed with ASD, 261 were diagnosed with global developmental delay (GDD), 206 were diagnosed with developmental language disorder (DLD), and 30 had normal development. The sensitivity, specificity, and optimal threshold value of the Modified Checklist for Autism in Toddlers (M-CHAT) and the Autism Behavior Checklist (ABC) for the early identification of ASD were evaluated by the receiver operating characteristic (ROC) curve. RESULTS: The M-CHAT had a high sensitivity of 88.3% but a low specificity of 36.0% for the identification of ASD. Its sensitivity decreased with age, and was maintained above 80% for children aged 16 to < 48 months. The ABC had a high specificity of 87.3% but a low sensitivity of 27.2%, with an optimal cut-off value of 47.5 based on the ROC curve analysis. The multivariate linear regression model based on a combination of the M-CHAT and ABC for screening of ASD showed a specificity of 85.8% and a sensitivity of 56.6%. CONCLUSIONS The M-CHAT has a high sensitivity and a low specificity in the identification of ASD, with a better effect in children aged 16 to < 48 months. The ABC has a high specificity and a low sensitivity. The multiple linear regression model method based on the combined M-CHAT and ABC to screen ASD appears to be effective.
Adolescent ; Autism Spectrum Disorder/diagnosis* ; Autistic Disorder ; Checklist ; Humans ; Infant ; Mass Screening ; ROC Curve

OBJECTIVES: The purpose of this study was to analyze the discriminant validity and the clinical cut off scores of the Child Behavior Checklist 1.5-5 (CBCL 1.5-5) in the diagnosis of autism spectrum disorder (ASD) and non-ASD. METHODS: In total, 104 ASD and 441 non-ASD infants were included in the study. T-test, discriminant analysis, receiver operating characteristic (ROC) curve analysis, and odds ratio analysis were performed on the data. RESULTS: The discriminant validity was confirmed by mean differences and discriminant analysis on the subscales of Emotionally reactive, Somatic complaints, Withdrawn, Sleep problems, Attention problems, Aggressive behavior, Internalizing problems, Externalizing problems, and Total problems, along with the Diagnostic and Statistical Manual of Mental Disorders (DSM)-oriented scales between the two groups. ROC analysis showed that the following subscales significantly separated ASD from normal infants: Emotionally reactive, Somatic complaints, Withdrawn, Sleep problems, Attention problems, Aggressive behavior, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems. Moreover, the clinical cut off score criteria adopted in the Korean-CBCL 1.5-5 were shown to be valid for the subscales Withdrawn, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems. CONCLUSION: The subscales of Withdrawn, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems significantly discriminated infants with ASD.
Autism Spectrum Disorder ; Autistic Disorder ; Checklist ; Child ; Child Behavior ; Child ; Diagnosis ; Diagnostic and Statistical Manual of Mental Disorders ; Humans ; Infant ; Odds Ratio ; ROC Curve ; Weights and Measures

OBJECTIVES: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective data-driven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. METHODS: Based on our search and exclusion criteria, we reviewed 13 studies. RESULTS: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. CONCLUSION: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.
Artificial Intelligence ; Autism Spectrum Disorder ; Autistic Disorder ; Behavior Observation Techniques ; Decision Support Systems, Clinical ; Delivery of Health Care ; Diagnosis ; Mass Screening ; Methods ; Sensitivity and Specificity

BACKGROUND: Chromosomal microarray (CMA) testing is a first-tier test for patients with developmental delay, autism, or congenital anomalies. It increases diagnostic yield for patients with developmental delay or intellectual disability. In some countries, including Korea, CMA testing is not yet implemented in clinical practice. We assessed the diagnostic utility of CMA testing in a large cohort of patients with developmental delay or intellectual disability in Korea. METHODS: We conducted a genome-wide microarray analysis of 649 consecutive patients with developmental delay or intellectual disability at the Seoul National University Children's Hospital. Medical records were reviewed retrospectively. Pathogenicity of detected copy number variations (CNVs) was evaluated by referencing previous reports or parental testing using FISH or quantitative PCR. RESULTS: We found 110 patients to have pathogenic CNVs, which included 100 deletions and 31 duplications of 270 kb to 30 Mb. The diagnostic yield was 16.9%, demonstrating the diagnostic utility of CMA testing in clinic. Parental testing was performed in 66 patients, 86.4% of which carried de novo CNVs. In eight patients, pathogenic CNVs were inherited from healthy parents with a balanced translocation, and genetic counseling was provided to these families. We verified five rarely reported deletions on 2p21p16.3, 3p21.31, 10p11.22, 14q24.2, and 21q22.13. CONCLUSIONS: This study demonstrated the clinical utility of CMA testing in the genetic diagnosis of patients with developmental delay or intellectual disability. CMA testing should be included as a clinical diagnostic test for all children with developmental delay or intellectual disability.
Autistic Disorder ; Child* ; Cohort Studies ; Diagnosis ; Diagnostic Tests, Routine ; Genetic Counseling ; Humans ; Intellectual Disability* ; Korea ; Medical Records ; Microarray Analysis ; Parents ; Polymerase Chain Reaction ; Retrospective Studies ; Seoul ; Virulence

OBJECTIVES: The objective of this study was to investigate the concordance of Diagnostic and Statistical Manual of Mental Disorders (DSM-IV and DSM-5) diagnostic criteria for autism spectrum disorder (ASD). METHODS: We retrospectively reviewed the medical records of 170 subjects (age range: 3–23, 140 boys) with developmental delay or social deficit from January 2011 to July 2016 at the Department of Psychiatry of Asan Medical Center. The Autism Diagnostic Interview-Revised (ADI-R), the Autism Diagnostic Observation Schedule (ADOS), and intelligence tests were performed for each subject. Diagnosis was reviewed and confirmed for each subject with DSM-IV Pervasive Developmental Disorder (PDD) and DSM-5 ASD criteria, respectively. RESULTS: Fifty-eight of 145 subjects (34.1%) who were previously diagnosed as having PDD in DSM-IV did not meet DSM-5 ASD criteria. Among them, 28 (48.3%) had Asperger's disorder based on DSM-IV. Most algorithm scores on ADOS and all algorithm scores on ADI-R were highest in subjects who met both DSM-IV PDD criteria and DSM-5 ASD criteria (the Convergent group), followed by subjects with a DSM-IV PDD diagnosis who did not have a DSM-5 ASD diagnosis (the Divergent group), and subjects who did not meet either DSM-IV PDD or DSM-5 ASD criteria (the non-PDD group). Intelligence quotient was lower in the Convergent group than in the Divergent group. CONCLUSION: The results of our study suggest that ASD prevalence estimates could be lower under DSM-5 than DSM-IV diagnostic criteria. Further prospective study on the impact of new DSM-5 ASD diagnoses in Koreans with ASD is needed.
Appointments and Schedules ; Asperger Syndrome ; Autism Spectrum Disorder* ; Autistic Disorder* ; Chungcheongnam-do ; Diagnosis* ; Diagnostic and Statistical Manual of Mental Disorders ; Intelligence ; Intelligence Tests ; Medical Records ; Prevalence ; Prospective Studies ; Retrospective Studies

OBJECTIVES: We examined the agreement between the Autism Diagnostic Observation Schedule (ADOS) and the Childhood Autism Rating Scale (CARS) in the diagnosis of autism spectrum disorder. METHODS: The ADOS and CARS scores of 78 children were retrospectively collected from a chart review. A correlation analysis was performed to examine the concurrent validity between the two measures. Using the receiver operating characteristic (ROC) curve, we determined the optimal cut-off score of the CARS for identifying autism spectrum disorder. RESULTS: The CARS score was significantly correlated with the ADOS score (r=0.808, p < 0.001). Taking ADOS as the ideal standard, the optimal cut-off scores of CARS for identifying autism and autism spectrum were 30 and 24.5, respectively. CONCLUSION: We determined the optimal cut-off scores of CARS for screening and diagnosing autism spectrum disorder.
Appointments and Schedules* ; Autism Spectrum Disorder* ; Autistic Disorder* ; Child ; Diagnosis* ; Humans ; Mass Screening ; Retrospective Studies ; ROC Curve

We present a case of 32-year-old male with profound mental retardation and autism spectrum disorder who had presented with seizures, rigidity and elevated creatine kinase and was initially diagnosed as neuroleptic malignant syndrome (NMS). The patient subsequently had a complicated clinical course, developing refractory status epilepticus, which lead to the eventual diagnosis of progressive encephalomyelitis with rigidity and myoclonus (PERM). We discuss the clinical similarities and differences between NMS and PERM, and highlight the need to consider alternative diagnoses when the clinical picture of NMS is atypical, particularly in this patient group where the history and clinical examination may be challenging.
Adult ; Autism Spectrum Disorder ; Autistic Disorder ; Creatine Kinase ; Diagnosis ; Encephalomyelitis* ; Humans ; Intellectual Disability ; Male ; Myoclonus* ; Neuroleptic Malignant Syndrome* ; Seizures ; Status Epilepticus

OBJECTIVES: Early intensive interventions are very important for children with autism spectrum disorder. We examined the actual conditions of hospital-based early intensive interventions for autism spectrum disorder in Seoul, in order to help develop and implement an evidence-based early intensive intervention model for use in Korea. METHODS: Nine hospital-based institutes running an early intensive intervention program for children with autism spectrum disorder responded to a questionnaire in September 2014. They provided a brief introduction to their program, explained its theoretical bases, and reported the number of children, their age, intervention time, duration and so on. RESULTS: In the majority of the institutions, the intervention was provided for over 20 hours every week, and the theoretical bases included various applied behavioral analysis (ABA) methods and other therapies (language and occupational therapy). The therapist-child ratio ranged from 1:1 to 5:3. Various types of therapists were involved, including behavioral analysts, special education teachers and (or) language pathologists. There was only one clinic where the behavioral analyst was the main therapist. Usually, the intervention was terminated just before the child entered elementary school. The main merit of the hospital-based intervention in our survey was the effectiveness of the multi-disciplinary intervention plan and its other merits were the accuracy of the diagnosis, its ability to be combined with medicine, and so on. CONCLUSION: The current hospital-based early intensive intervention programs provide interventions for over 20 hours per week and employ multidisciplinary approaches. However, there are very few institutes for children with autism and very few intervention specialists and specialist education courses in the country. We need more educational programs for intervention therapists and have to try to develop policies which encourage the implementation of an evidence-based early intensive intervention program nationwide.
Academies and Institutes ; Autism Spectrum Disorder* ; Autistic Disorder* ; Child ; Diagnosis ; Education ; Education, Special ; Humans ; Korea* ; Running ; Seoul ; Specialization