Comparison of DSM-IV-TR and DSM-5 Criteria in Diagnosing Autism Spectrum Disorders in Singapore

The LEADING Guideline: Reporting Standards for Expert Panel, Best-Estimate Diagnosis, and Longitudinal Expert All Data (LEAD) Studies

Accurate assessments of symptoms and diagnoses are essential for health research and clinical practice but face many challenges. The absence of a single error-free measure is currently addressed by assessment methods involving experts reviewing several sources of information to achieve a more accurate or best-estimate assessment. Three bodies of work spanning medicine, psychiatry, and psychology propose similar assessment methods: The Expert Panel, the Best-Estimate Diagnosis, and the Longitudinal Expert All Data (LEAD). However, the quality of such best-estimate assessments is typically very difficult to evaluate due to poor reporting of the assessment methods and when it is reported, the reporting quality varies substantially. Here we tackle this gap by developing reporting guidelines for such studies, using a four-stage approach: 1) drafting reporting standards accompanied by rationales and empirical evidence, which were further developed with a patient organization for depression, 2) incorporating expert feedback through a two-round Delphi procedure, 3) refining the guideline based on an expert consensus meeting, and 4) testing the guideline by i) having two researchers test it and ii) using it to examine the extent previously published articles report the standards. The last step also demonstrates the need for the guideline: 18 to 58% (Mean = 33%) of the standards were not reported across fifteen randomly selected studies. The LEADING guideline comprises 20 reporting standards related to four groups: The Longitudinal design; the Appropriate data; the Evaluation - experts, materials, and procedures; and the Validity group. We hope that the LEADING guideline will be useful in assisting researchers in planning, reporting, and evaluating research aiming to achieve best-estimate assessments.

An initial examination of the psychometric properties of the Diagnostic Instrument for Social and Communication Disorders (DISCO-11) in a clinical sample of children with a diagnosis of Autism spectrum disorder

INTRODUCTION

The diagnostic interview for social and communication disorders (DISCO - 11; Wing 2006), is a semi-structured, interview-based instrument used in the diagnosis of children with autism spectrum disorder (ASD). This paper explores the psychometric properties of the DISCO-11 used in a specialist Paediatric clinical setting. Two key research questions were examined; (1) Does the factor structure of the DISCO-11 reflect the diagnostic and statistical manual 5th edition (DSM-5, American Psychiatric Association [APA], 2013) dyad of impairment in ASD? (2) Is there evidence of diagnostic stability over time using the DISCO?

METHODS

Review assessments of 65 children with ASD were carried out using standardised measures including the DISCO-11 and the autism diagnostic observation schedule.

RESULTS

The results revealed two factors resembling the DSM-5 algorithms, as used in DISCO-11, which were named as social-communication, and restricted and repetitive behaviours. The reliability, for the overall DISCO score was good (Cronbach's alpha = 0.78). The social communication and social interaction subscale showed good reliability (Cronbach's Alpha = 0.77) as did the restricted and repetitive patterns of behaviour, interests or activities subscale (Cronbach's Alpha = 0.74). Acceptable internal reliability was found for the overall DISCO score and the subscales of social communication and social interaction and the restricted and repetitive patterns of behaviour, interests or activities. Test-retest showed good stability of diagnosis over time.

DISCUSSION

This study supports that the DISCO-11 shows potential as a valid and reliable instrument that can be used both for clinical and research purposes.

How has DSM-5 Affected Autism Diagnosis? A 5-Year Follow-Up Systematic Literature Review and Meta-analysis

We conducted a 5-year follow-up systematic review and meta-analysis to determine change in frequency of autism spectrum disorder (ASD) diagnosis since diagnostic and statistical manual 5 (DSM-5) publication and explore the impact of Social Communication Disorder (SCD). For 33 included studies, use of DSM-5 criteria suggests decreases in diagnosis for ASD [20.8% (16.0-26.7), p < 0.001], DSM-IV-TR Autistic Disorder [10.1% (6.2-16.0), p < 0.001], and Asperger's [23.3% (12.9-38.5), p = 0.001]; pervasive developmental disorder-not otherwise specified decrease was not significant [46.1% (34.6-58.0), p = 0.52]. Less than one-third [28.8% (13.9-50.5), p = 0.06] of individuals diagnosed with DSM-IV-TR but not DSM-5 ASD would qualify for SCD. Findings suggest smaller decreases in ASD diagnoses compared to earlier reviews. Future research is needed as concerns remain for impaired individuals without a diagnosis.

Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster

Asperger syndrome (AS) is subtype of autism spectrum disorder (ASD). Diagnosis and pathological analysis of AS through resting-state fMRI data is one of the hot topics in brain science. We employed a new model called the genetic-evolutionary random Support Vector Machine cluster (GE-RSVMC) to classify AS and normal people, and search for lesions. The model innovatively integrates the methods of the cluster and genetic evolution to improve the performance of the model. We randomly selected samples and sample features to construct GE-RSVMC, and then used the cluster to classify and extract lesions according to classification results. The model was validated by data of 157 participants (86 AS and 71 health controls) in ABIDE database. The classification accuracy of the model reached to 97.5% and we discovered the brain regions with significant differences, such as the Angular gyrus (ANG.R), Precuneus (PCUN.R), Caudate nucleus (CAU.R), Cuneus (CUN.R) and so on. Our method provides a new perspective for the diagnosis and treatment of AS, and a universal framework for other brain science research as the model has excellent generalization performance.