[article]
| Titre : |
DSM-5 based algorithms for the Autism Diagnostic Interview-Revised for children ages 4-17?years |
| Type de document : |
Texte imprimé et/ou numérique |
| Auteurs : |
Linnea A. LAMPINEN, Auteur ; Shuting ZHENG, Auteur ; Lindsay OLSON, Auteur ; Vanessa H. BAL, Auteur ; Audrey E. THURM, Auteur ; Amy N. ESLER, Auteur ; Stephen M. KANNE, Auteur ; So Hyun KIM, Auteur ; Catherine LORD, Auteur ; China PARENTEAU, Auteur ; Kerri P. NOWELL, Auteur ; Jane E. ROBERTS, Auteur ; Nicole TAKAHASHI, Auteur ; Somer L. BISHOP, Auteur |
| Article en page(s) : |
p.1403-1413 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Autism autism spectrum disorder ADI-R diagnosis sensitivity specificity diagnostic instruments |
| Index. décimale : |
PER Périodiques |
| Résumé : |
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. |
| En ligne : |
https://doi.org/10.1111/jcpp.14159 |
| Permalink : |
https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=566 |
in Journal of Child Psychology and Psychiatry > 66-9 (September 2025) . - p.1403-1413
[article] DSM-5 based algorithms for the Autism Diagnostic Interview-Revised for children ages 4-17?years [Texte imprimé et/ou numérique] / Linnea A. LAMPINEN, Auteur ; Shuting ZHENG, Auteur ; Lindsay OLSON, Auteur ; Vanessa H. BAL, Auteur ; Audrey E. THURM, Auteur ; Amy N. ESLER, Auteur ; Stephen M. KANNE, Auteur ; So Hyun KIM, Auteur ; Catherine LORD, Auteur ; China PARENTEAU, Auteur ; Kerri P. NOWELL, Auteur ; Jane E. ROBERTS, Auteur ; Nicole TAKAHASHI, Auteur ; Somer L. BISHOP, Auteur . - p.1403-1413. Langues : Anglais ( eng) in Journal of Child Psychology and Psychiatry > 66-9 (September 2025) . - p.1403-1413
| Mots-clés : |
Autism autism spectrum disorder ADI-R diagnosis sensitivity specificity diagnostic instruments |
| Index. décimale : |
PER Périodiques |
| Résumé : |
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. |
| En ligne : |
https://doi.org/10.1111/jcpp.14159 |
| Permalink : |
https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=566 |
|
DSM-5 based algorithms for the Autism Diagnostic Interview-Revised for children ages 4-17?years in Journal of Child Psychology and Psychiatry, 66-9 (September 2025)
