DSM-III and DSM-III-R diagnosis of autism and pervasive developmental disorder in nursery school children

DSM-III and DSM-III-R diagnoses of 112 developmentally disordered preschool children were compared. There was no significant difference between the DSM-III and DSM-III-R diagnosis of the inclusive category of pervasive developmental disorder, but nearly twice as many cases (58) were diagnosed as autistic disorder by DSM-III-R criteria as were diagnosed as infantile autism (31) by DSM-III. Thirty children met both DSM-III and DSM-III-R criteria for autism (IA/AD) and 23 received a DSM-III diagnosis of atypical PDD (A-PDD) and a DSM-III-R diagnosis of AD (A-PDD/AD). All of the IA/AD children and none of the A-PDD/AD group displayed a marked lack of awareness of others. DSM-III-R criteria have specifically broadened the concept of autism to include children who, although socially impaired, are not pervasively unresponsive to others.     

DSM-III and DSM-III-R diagnoses of autism

The authors examined the reliability, sensitivity, and specificity of DSM-III and DSM-III-R criteria for autism in relation to each other and to clinical diagnoses in 114 children and adults (52 diagnosed by clinicians' best judgment as autistic and 62 as nonautistic but developmentally disordered). They used a standard, structured coding scheme to evaluate each patient. The reliability of specific criteria was generally high. Although DSM-III criteria were highly specific, they were less sensitive; the reverse was true for DSM-III-R. The authors conclude that the diagnostic concept of autism in DSM-III-R appears to have been substantially broadened.     

Social and Nonsocial Factors in the Childhood Autism Rating Scale

The Childhood Autism Rating Scale (CARS) was factor analyzed to determine if distinct and independent subgroups of symptoms could be derived, which would be consistent with the current multidimensional theory and nosology for autism. To address this issue, the CARS was factor analyzed for a sample of 90 children with diagnoses of either autism or PDDNOS, based on DSM-III-R diagnostic criteria. Five factors emerged: Social Communication, Emotional Reactivity, Social Orienting, Cognitive and Behavioral Consistency, and Odd Sensory Exploration. Factor-based scales were created. These factor-based scales were demonstrated to distinguish subjects with autism from subjects with PDDNOS and nonautistic subjects. Factor-based scores were examined to determine the degree to which they were associated with individual differences (such as age, IQ, gender, history of regression, and history of abnormal EEGs) among children with pervasive developmental disorders (PDDs). The application of these distinct and independent factors may have important clinical and research implications. The generation of factor-based scales may provide information on the nature of the individual differences that are thought to be present among children with autism. Additionally, the use of factor-based scale scores may increase the sensitivity of the CARS for identifying younger and/or higher functioning individuals within the PDD spectrum.     

Three diagnostic systems for autism: DSM-III,DSM-III-R,and ICD-10

ICD-10 draft research criteria for childhood autism were applied to a previously published data set comparing DSM-III and DSM-III-R to clinicians' diagnoses of autism. The ICD-10 approach paralleled clinicians' patterns of diagnosis and, to a lesser extent, the DSM-III system. Relative to either clinicians, DSM-III, or ICD-10 the DSM-III-R system overdiagnosed the presence of autism. Implications for research and for future revision of diagnostic criteria are discussed.This study was supported by a grant from the McArthur Foundation to the American Psychiatric Association and by grant HD-03008 from the National Institute of Child Health and Human Development, by National Institute of Mental Health grant MH-30929 to the Mental Health Clinical Research Center, and grant MRIS-1416 to the Veleran's Administration. The views expressed in this article are those of the authors and do not represent the official positions of the DSM-IV Task Force, Work Groups, or the American Psychiatric Association. The authors thank Helena Kraemer for her helpful comments.     

Use of the childhood autism rating scale with autistic adolescents and adults

The suitability of the Childhood Autism Rating Scale (CARS) for diagnosing adolescents and adults with autism was tested and CARS ratings over time were examined to assess how the characteristics of autism change during adolescence. Subjects (N = 89) had been rated with the CARS before age 10 and again after age 13. A cutoff score of 27 was used; of 59 diagnosed as autistic prior to age 10, 92% were diagnosed as autistic after age 13. Moreover, 9 CARS categories showed significant improvements over time; only 1 showed a significant decrement. The CARS is thought to be a useful screening device for adolescents and adults with autism. Implications of this research for the understanding of autism and how it changes with development are discussed.     

Fragile X syndrome, DSM-III-R, and autism

Although reports of autistic behavior in fragile X males have been published for 8 years, there is little information about specific behaviors shown by fragile X males that are suggestive of the diagnosis of autism. The new diagnostic criteria for autistic disorder contained in the DSM-III-R provided the opportunity for more closely investigating the topography of autistic behavior in 17 fragile X males and the effects of age and IQ on its occurrence. The criteria most frequently met by these subjects were related to deficits in social interaction with peers, abnormalities in verbal and nonverbal communication, stereotypic motor behavior, and unusual responses to sensory stimuli. Fragile X subjects did not frequently show abnormalities in attachment behaviors and reciprocal interaction with caregivers. There were no discernable age or IQ effects. The importance and implications of these findings are discussed and the need for greater exploration of autism "subtypes" is emphasized.     

Autistic Disorder and Schizophrenia: Diagnostic Overlaps

Data on 14 males with autism and 14 with schizophrenia were collected to examine symptom overlap. The Structured Clinical Interview (SCID), the schedule for positive symptoms (SAPS) and the schedule for negative symptoms (SANS) of schizophrenia, the Childhood Autism Rating Scale (CARS), and the DSM-III-R were administered. On the SCID, none of the men with paranoid schizophrenia met criteria for autism while 7 of those with autism met criteria for schizophrenia, disorganized type, showing negative symptoms. In addition, 5 showed positive symptoms on the SAPS and 6 negative symptoms on the SANS. As the difference in measured nonverbal intelligence was not significant, the effects could not be attributed to it. Although the findings continue to support the differentiation of autism and schizophrenia, they are also consistent with a comorbidity of the two disorders, mainly in those diagnosed with autism.     

Brainstem auditory evoked response and subcortical abnormalities in autism

Previous studies of the neurobiology of autism that have used the brainstem auditory evoked response have given contradictory results. The authors of this study considered two supplementary aspects; they added an ipsilateral masking procedure, and they compared the results for every subject to the values (corrected for age and sex) of a large number of normal children. Twenty autistic (according to DSM-III-R criteria) and 13 mentally retarded (nonverbal IQ less than 75) subjects were assessed. Eighty percent of the autistic subjects had abnormal interpeak latencies, compared to 15% of the mentally retarded subjects. The I-V and III-V prolonged interpeak latency values were seen only in the autistic subjects. The ipsilateral masking procedure doubled the rate of detection of higher-brainstem abnormalities in the autistic children.     

Chromosomes in autism and related pervasive developmental disorders: a cytogenetic study

Few studies have examined the occurrence of chromosome abnormalities in a large sample of patients with autism and related pervasive developmental disorders (PDDs). In the present report, the authors examined a consecutive series of 92 children with PDDs (DSM-III-R; 75 males and 17 females). A cycogenetic examination, including growth in folate deficient medium, was performed in all cases. Three patients (3.2%) (two females and one male) showed chromosome abnormalities: deletion of the long arm of chromosome 8; tetrasomy of chromosome 15; and XYY syndrome. Only the subject who had tetrasomy I S met the criteria for autistic disorder, while the others were diagnosed as suffering from a PDD not otherwise specified (PDDNOS). Another patient showed an abnormal fragile site at Xq27 in three out of 100 cells. However, subsequent molecular studies did not confirm the presence of fragile-X syndrome. These results suggest that chromosome abnormalities are uncommon in traditional autism and may be relatively more common in people with PDDNOS.     

Autism Diagnostic Interview-Revised and the Childhood Autism Rating Scale: convergence and discrepancy in diagnosing autism

The agreement between the Autism Diagnostic Interview–Revised (ADI-R) and the Childhood Autism Rating Scale (CARS) was investigated in the diagnostic assessment of 54 children aged 22–114 months referred for possible autism. The observed agreement between the two systems was 66.7% (Cohen's kappa = .40) when the ADI-R definition for autism was applied (i.e., scores reaching cutoff in three domains on the ADI-R), but increased considerably with less stringent criteria; that is, scores reaching cutoffs in two domains and in one domain on the ADI-R. As predicted, the CARS identified more cases of autism than the ADI-R. Children classified as autistic according to both instruments had significantly lower IQ/DQ and more severe autistic symptomatology than those classified with the CARS only.     

A comparison and evaluation of three commonly used autism scales

Reliability and validity of three commonly used autism scales, the Autism Behavior Checklist (Krug, Arick, & Almond, 1980), the Real Life Rating Scale (Freeman, Ritvo, Yokota, & Ritvo, 1986), and the Childhood Autism Rating Scale (Schopler, Reichler, & Renner, 1988), were investigated. Data analyses were based on completed protocols for 24 children or adolescents who met DSM-III-R criteria for pervasive developmental disorders. First, to replicate previous findings, interrater reliability of each of the two direct observational scales was assessed. Second, correlations between pairs of the three scales were calculated. Third, diagnostic classifications based on autism scale cutoff scores were compared to classifications based on DSM-III-R criteria. Fourth, relationships between autism scale scores and adaptive behavior scores were investigated. Results and implications for the use of these scales in the assessment of autistic behaviors are discussed.Special thanks to John Junginger for his help and support throughout this project.     

Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder

Autism is widely believed to be a heterogeneous disorder; diagnosis is currently based solely on clinical criteria, although genetic, as well as environmental, influences are thought to be prominent factors in the etiology of most forms of autism. Our goal is to determine whether a predictive model based on single-nucleotide polymorphisms (SNPs) can predict symptom severity of autism spectrum disorder (ASD). We divided 118 ASD children into a mild/moderate autism group (n = 65) and a severe autism group (n = 53), based on the Childhood Autism Rating Scale (CARS). For each child, we obtained 29 SNPs of 9 ASD-related genes. To generate predictive models, we employed three machine-learning techniques: decision stumps (DSs), alternating decision trees (ADTrees), and FlexTrees. DS and FlexTree generated modestly better classifiers, with accuracy = 67%, sensitivity = 0.88 and specificity = 0.42. The SNP rs878960 in GABRB3 was selected by all models, and was related associated with CARS assessment. Our results suggest that SNPs have the potential to offer accurate classification of ASD symptom severity.     

A prevalence study of autism in tuberous sclerosis

An estimate of the prevalence of autism in tuberous sclerosis (TSC) was made by interviewing the parents of 21 children between ages 3 and 11 ascertained during a previous population study of the condition in the West of Scotland. Five of the children (24%) were rated autistic and a further four (19%), all of whom were girls, has socially impaired behavior categorized as pervasive developmental disorder, without fulfilling all the DSM-III-R criteria for autism. One further boy had disruptive attention-seeking behavior that had excluded him from his, normal school. The estimated prevalence from this study of autism in TSC is 1 in 4 children in general, and 1 in 2 of those with mental retardation. Tuberous sclerosis could be a significant cause of autism and pervasive developmental disorders, particularly in girls.This work was supported by a grant from the Tuberous Sclerosis Association of Great Britain. The authors thank Jennifer Dennis for discussions during the preparation of the questionnaire and for collaboration on validating the recorded interviews.     

Comorbid association of autism and schizophrenia

OBJECTIVE: In the last several decades, considerable evidence has suggested that autism and schizophrenia are unrelated. However, recent reports have suggested that individuals with autism may be at greater risk for schizophrenia and that the conditions may be more closely related than generally believed. METHOD: The authors examined detailed case records of 163 adolescents and adults with well-documented histories of autism. These cases included 139 males and 24 females. RESULTS: Only one individual had an unequivocal history of schizophrenia. CONCLUSIONS: If the present study group is taken to be representative, it appears that the frequency of schizophrenia among autistic patients (0.6%) is roughly comparable to the frequency of schizophrenia in the general population. It does not appear that the two conditions are more commonly observed together than would be expected on a chance basis; therefore, the current (DSM-III-R) approach to dual diagnosis of these conditions appears reasonable.     

Diagnosis and classification in autism

This study compared four systems for the diagnosis of autism (DSM-III, DSM-III-R, DSM-IV, and ICD-10) with two empirically derived taxa of autism, and with three social subgroups of autism (Aloof, Passive, and Active-but-Odd) in 194 preschool children with salient social impairment. There were significant behavior and IQ differences between autistic and other-PDD groups for all four diagnostic systems, and a significant association was found (a) for Taxon B, diagnoses of autism, and the Aloof subgroup, and (b) for Taxon A, other-PDD, and the Active-but-Odd subgroup. Findings offer support for two major overlapping continua within idiopathic Pervasive Developmental Disorder.This study was supported in part by National Institutes of Health grant NS 20489 to the Autism and Language Disorders Collaborative Project: Preschool Study Group. The authors gratefully acknowledge the contributions of Robert Golden, Dorothy Aram, and Barbara Wilson to this study. The authors thank Dolores Drake and Maura McGovern Graber for assistance in the preparation of this paper, and thank Cathy Lord and several anonymous reviewers for their help in developing the structure of this paper.     

Normal P50 gating in children with autism

BACKGROUND: An important characteristic of children with autism is their unusual reaction to stimuli, which may be related to problems in the filtering of sensory input. For this reason, sensory filtering was measured in children with autism using the P50 gating paradigm. METHOD: Twelve non-mentally retarded children with autism (i.e., having a DSM-IV diagnosis of either autistic disorder or pervasive developmental disorder not otherwise specified) and 11 healthy control children were tested for their ability to suppress P50, measured at the Cz electrode. RESULTS: No differences were found between the children with autism and the control children with regard to absolute P50 amplitudes and P50 suppression. CONCLUSION: The excitability of the neuronal substrate that causes P50 is normal in children with autism, as are the early, inhibitory processes related to P50 gating. These results distinguish between subjects with autism and subjects with schizophrenia, in whom sensory gating is abnormal.     

Features of autism, autistic traits, autism: retrospective analysis of clinical symptoms in children treated in the Pediatric Psychiatric Clinic

All currently accepted definitions of autism include three main criteria which have to be met for a diagnosis to be made. These are: disturbance of reciprocal social interaction, disturbance of communication and restriction of normal variation in behaviour and interests. The criteria used in the ICD-10 include all these domains. On the other hand, the number of mentally handicapping conditions and brain damage syndromes show the same triad of symptoms. Many of patients actually fulfill all currently accepted criteria for autism, but, for some reason do not receive the diagnosis. We analysed retrospectively the clinical picture of all children hospitalised in Department of Child Psychiatry in the last 10 years before the introduction of ICD-10 in Poland (1986-1996) and diagnosed as having autism, autistic traits or features. The group consist of 19 children (0.8% of all children hospitalised in that time). 4 of them had been diagnosed as having autism, 4 as having features of autism and 11 as having autistic traits. Additional diagnoses were: organic brain damage in 2 children, carnitine deficiency and cerebral palsy in 1 child and minimal brain dysfunction in 3 cases. Despite of the diagnosis all children met current ICD-10 criteria for autism or atypical autism, according to age of onset. There were no significant differences in constellation of symptoms included in ICD-10 between groups divided according to the past diagnosis.     

The roles of cortisol and pro-inflammatory cytokines in assisting the diagnosis of autism spectrum disorder

Autism spectrum disorders (ASD) is a severe neurodevelopmental disorder characterized by impairments in social interaction and repetitive behaviors. Diagnosis of ASD is currently phenotype based with no reliable laboratory test available to assist clinicians. The desire for clinically useful and reliable biomarkers is strong. Researches have shown that individuals with autism often exhibit dysfunction of hypothalamic–pituitary–adrenal (HPA) axis and cytokines. The purpose of this study was to evaluate diurnal variation of cortisol (cortisol VAR), interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) as potential biomarkers for ASD. The present results demonstrated that in comparison to the healthy controls, the individuals with autism showed a lower level of cortisol VAR, higher level of IL-6 and TNF-α. The levels of cortisol VAR, IL-6 and TNF-α have significantly correlations with the severity of ASD measured by CARS scores. The results of ROC analysis indicated the cortisol VAR, IL-6 and TNF-α were potential biomarkers in diagnosis of ASD. The combination of three factors performed the best sensitivity and specificity for diagnosis of ASD. Therefore, the present study may reveal a simple clinical approach with great potential for assisting the diagnosis of ASD.     

Subtypes of autism by cluster analysis based on structural MRI data

Abstract The aim of our study was to subcategorize Autistic Spectrum Disorders (ASD) using a multidisciplinary approach. Sixty four autistic patients (mean age 9.4±5.6 years) were entered into a cluster analysis. The clustering analysis was based on MRI data. The clusters obtained did not differ significantly in the overall severity of autistic symptomatology as measured by the total score on the Childhood Autism Rating Scale (CARS). The clusters could be characterized as showing significant differences: Cluster 1: showed the largest sizes of the genu and splenium of the corpus callosum (CC), the lowest pregnancy order and the lowest frequency of facial dysmorphic features. Cluster 2: showed the largest sizes of the amygdala and hippocampus (HPC), the least abnormal visual response on the CARS, the lowest frequency of epilepsy and the least frequent abnormal psychomotor development during the first year of life. Cluster 3: showed the largest sizes of the caput of the nucleus caudatus (NC), the smallest sizes of the HPC and facial dysmorphic features were always present. Cluster 4: showed the smallest sizes of the genu and splenium of the CC, as well as the amygdala, and caput of the NC, the most abnormal visual response on the CARS, the highest frequency of epilepsy, the highest pregnancy order, abnormal psychomotor development during the first year of life was always present and facial dysmorphic features were always present. This multidisciplinary approach seems to be a promising method for subtyping autism.     

Childhood Autism Rating Scale (CARS) and Autism Behavior Checklist (ABC) Correspondence and Conflicts with DSM-IV Criteria in Diagnosis of Autism

Childhood Autism Rating Scale (CARS) and Autism Behavior Checklist (ABC) are tests widely used for screening and diagnosis of autism. This study verified their correspondence and conflict with a diagnosis made with DSM-IV criteria. The sample consisted of 65 children, aged 18 months to 11 years. We found complete agreement between DSM-IV and CARS. We show that ABC does not distinguish individuals with autistic disorders from other cases of developmental disorders as well as CARS: the number of false negatives is high (46%) with ABC as opposed to 0% with CARS.