Autism Diagnostic Interview-revised Adi-r Pdf

The current study aimed to investigate the Autism Diagnostic Interview-Revised (ADI-R) algorithms for toddlers and young preschoolers (Kim and Lord, J Autism Dev Disord 42(1):82–93, ) in a non-US sample from ten sites in nine countries (n = 1,104). The construct validity indicated a good fit of the algorithms. The diagnostic validity was lower, with satisfactorily high specificities but moderate sensitivities. Young children with clinical ASD and lower language ability were largely in the mild-to-moderate or moderate-to-severe concern ranges of the ADI-R, nearly half of the older and phrase speech ASD-group fell into the little-to-no concern range. Although broadly the findings support the toddler algorithms, further work is required to understand why they might have different properties in different samples to further inform research and clinical use. Participants All sites of the COST-ESSEA network (consisting of 80 scientists in 23 countries) were invited to contribute existing databases to participate in the current study. Ten sites that had relevant data to contribute participated, resulting in the collection of 1,187 cases outside the US.

To be included in the current sample, the toddlers and young preschoolers had to be between 12 months and 47 months 30 days old with nonverbal mental ages from 10 months and higher, had to have an ADI-R available with scores on all domains as specified for developmental cell and had to have received a best clinical estimate diagnosis (BCE), resulting in an N of 1,104. Additionally, research reliability of administration and scoring of the ADI-R was required. The sample (74.0% males) had a mean age of 34.6 months (SD = 8.06). Just over half of the children (56.1%) had a BCE ASD diagnosis.

Autism Diagnostic Interview, Revised (ADI-R) M. LeCouteur, M.B.B. Used in research for decades, this comprehensive interview provides a thorough. Autism Diagnostic Interview-Revised. Has a cutoff; a child must meet or exceed cutoffs in all four areas to receive an ADI-R classification of 'autism.'

Due to the young age of the sample and in line with DSM-5, no differentiation was made between autistic disorder (AD) and non-autism ASD (formerly, pervasive developmental disorder). Another 24.5% had a non-spectrum diagnosis (NS) and 19.5% were typically developing (TD). The 12-20/NV21-47 cell mainly consisted of children with ASD (N = 263) with 60 children with NS disorders and only seven TD children, included for determining sensitivity and specificity. The SW21-47 cell contained 192 cases with ASD, 90 with NS disorders and 42 TD. In the PH21-47 cell, 36.4% had an ASD (N = 164), with almost equally many with NS disorders (120) and TD (166).

The non-spectrum diagnoses were classified following Kim and Lord () as: language delay (N = 112), nonspecific intellectual disability (N = 39), Attention Deficit/Hyperactivity Disorder (ADHD; N = 34), nonspecific developmental delay (N = 28), anxiety or internalizing emotion regulation problems (N = 27), externalizing emotion regulation problems (N = 10), attachment (N = 2) and other (N = 18). In Table, the participant characteristics are presented for the total sample. Diagnostic procedures per site The sites included children from various backgrounds: some samples were based on diagnostic assessment of toddlers/children considered ‘at risk’ of ASD following screening in general or high risk populations (NL Nijmegen, part of the UK, Spain, Finland), whereas others were based on diagnostic assessment of clinical referrals for ASD or other developmental problems based on parental and/or professional concern (Sweden, NL Utrecht, Iceland, Macedonia, France). The children from Israel were included for research into the relationship between use of medication by mothers during pregnancy and social communicative development and temperament of their children after birth.

They were not considered at risk for ASD for research or clinically and were recruited from the general population, however, a large proportion was born prematurely. These children were included for determining sensitivity and specificity.

Additionally, as shown in Table, not all of the sites had data in all three developmental cells, or the numbers were too small for reliable and valid analyses with the revised algorithms. The sample of N = 7 in the 12-20/NV21-47 TD cell is very small even in the total sample. Also, the composition of the data differed over sites. For example, while most sites included children with ASD as the majority (over 58% in eight out of 10 sites, with five over two-thirds), in the other two subsamples TD was dominant. Another example is that the Finnish sample contained children who were clinically referred for concerns on ASD based on population screening, but who were not diagnosed with ASD (yet) after a thorough diagnostic procedure. At the same time, the sample from Israel contained children who were not specifically at risk for ASD.

Autism Diagnostic Interview-Revised (ADI-R) All toddlers and young preschoolers in the study had been administered an ADI-R, by a trained psychologist, psychiatrist or speech and language pathologist with research reliability in administration and scoring of the interview. Most often the standard ADI-R was administered and in 249 cases (Israel and UK CHAT study) the toddler ADI-R was administered.

In Sweden, the Netherlands, Finland, Spain, France and Israel, an officially translated, approved and published ADI-R was available. In Iceland and Macedonia a translated and approved version of the ADI-R was available although this had not been published. The mean ADI-R domain scores (Table ) varied over the sites. These scores did not seem to be systematically related to recruitment method.

For example, the first two samples differed in background, yet had relatively comparable mean domain scores. Compared to the US samples, in the current sample, ASD children had relatively low scores on the SA/SC domain, especially in the PH21-47 cell. Additionally, the NS children from the current sample seemed to have relatively high scores on the SA/SC domain. Furthermore, all RRB scores seemed to be relatively low. However, the differences between the current and the US samples could not be formally tested, since the original datasets of the US samples would have been needed for that. Non-verbal Level of Functioning Level of nonverbal cognitive functioning was available for 983 cases (89%), most often measured with the Mullen Scales of Early Learning (MSEL; Mullen ), the Merrill-Palmer–Revised Scales of Development (Roid and Sampers ), or the PEP-R (Schopler et al. For the Mullen, NVIQ was based on fine motor (FM) and visual reception (VR) age equivalents: NVIQ = (mean age equivalent on FM and VR/chronological age in months) × 100.

For the Merrill-Palmer, NVIQ was calculated as (mean age equivalent on cognitive and fine motor/chronological age in months) * 100. For the PEP-R, NVIQ was calculated as: (mean developmental age in months on all subscales except for the verbal scale/chronological age in months) * 100. The mean NVIQ differs over the sites, ranging from 40.4 to 113. This is important, since the level of NVIQ might have influenced scores on the ADI-R if these were correlated in the current sample. In that case, the differences in NVIQ might explain the differences in mean domain scores on the ADI-R. Pearson r correlations seemed to indicate that the domain scores were slightly more related to NVIQ in the UK and Spain samples than in the Sweden and NL Nijmegen samples (Sweden:.00 through.30; NL Nijmegen:.10 through.29; UK:.19 through.65; Spain:.17 through.58).

Macedonia had the highest domain scores and the lowest NVIQ, however, the n was too small for Pearson r correlation (5 in SW, 15 in PH cell). Confirmatory Factor Analyses Table shows the proposed three factor solution of the revised algorithm in the current sample. This solution had satisfactory indices of goodness of fit in all developmental cells: Comparative Fit Indices (CFI) ranged from.889 to.929 (CFI between.9 and 1.0 indicates a good fit of the proposed model) and the Root Mean Square Error Approximations (RMSEA) ranged from.055 to.063 (RMSEA below.08 indicates a satisfactory goodness of fit). Correlations between factors were.68–.90 for the 12-20/NV21-47 cell,.64–.92 for the SW21-47 cell and.67–.83 for the PH21-47 cell.

In all cells, correlations between the SA/SC factor and the IGP/RPI factor were the highest. Sensitivity and specificity of cutoff criteria ADI-R Toddler algorithms for ASD versus non-spectrum In the 12-20/NV21-47 cell, specificity for ASD was high,.93 for the clinical and.95 for the research algorithm cutoff. Sensitivity in this cell was.78 for the clinical and.66 for the research cutoff. In the SW21-47 cell, the clinical cutoff was associated with a specificity of.70, with a sensitivity of.80, and the research cutoff resulted in a higher specificity (.89) with a low sensitivity of. Cambridge Advanced Learner S Dictionary Download Apk. 53. In the PH21-47 cell, the specificity was again highest for the research criteria (.93) with a sensitivity of.45 only, and lower for the clinical criteria (.81), with a sensitivity of.56. Further investigation of the separate sensitivities was undertaken for those sites with a sample size of over a hundred cases and enough children with ASD and NS (Sweden; NL Nijmegen; UK; and Spain). The large majority of the data from Israel represented TD, therefore, sensitivity and specificity were not calculated for this sample.

Sensitivities varied over the sites: in the Netherlands and Sweden.31–.47 for research cutoffs and.47–.71 for clinical cutoffs; in the UK and Spain.64–.91 for research cutoffs and.64–.98 for clinical cutoffs. Based on the ROC analyses, the Areas under the Curve (AuC) indicated that the algorithms as proposed by Kim and Lord () were valid when comparing a clinical diagnosis of ASD versus non-spectrum [AuC.93 (95% CI.90–.97) for 12-20/NV21-47; AuC.83 (95% CI.78–.88) for SW21-47; AuC.77 (95% CI.71–.82) for PH21-47].

These analyses investigated a continuous measure of criterion related validity, based on the total scores of two or three domains (the total scores on the proposed algorithms in each cell). Note that the domains were not examined separately. Free Vpn For Free Download on this page. Experimentally adding the IGP domain items to the total score for the 12-20/NV21-47 and SW21-47 cell resulted in an AuC that resembled the one based on the two domain total score [.94 (95% CI.90–.97) for 12-20/NV21-47.84 (95% CI.79–.89) for SW21-47]. Excluding the RPI domain items from the total score for the PH21-47 cell also resulted in a comparable AuC (.78; 95% CI.73–.84). Adding or omitting the IGP/RPI domain items thus did not seem to affect the sensitivity or specificity over the range of total scores on two or three domains combined in the current sample. Ranges of Concern The ranges of concern as defined by Kim and Lord (), aiming for 80% of the children with ASD in the ranges of mild-to-moderate or moderate-to-severe concern and 95% of the TD children in the little-to-no concern range, seemed more or less applicable to the 12-20/NV21-47 and SW21-47 developmental cells in the current sample: In the 12-20/NV21-47 cell, 77.2% of the 246 children with ASD fell into the ranges of mild-to-moderate or moderate-to-severe concern and in the SW21-47 cell 79.7%. Of the TD children 90.5% in the SW21-47 cell fell into the no-to-little concern range.

In the 12-20/NV21-47 there were only 7 children in the TD group, therefore the number in this cell is too small to analyze reliably. Of the NS children, 6.6% in the 12-20/NV21-47 and 30% in the SW21-47 cell fell into the risk ranges, percentages that fell within the ranges in the Michigan sample (30–33%; 2012) and CPEA/STAART sample (6–16%; 2013). For the PH21-47 cell the results were somewhat different. Whereas 98.2% of the TD group and 80.8% of the NS children fell into the little-to-no concern range, only 56.1% of the children with ASD fell into one of the risk ranges. This means that 43.9% of children diagnosed with an ASD in the current sample fell into the little-to-no concern range, with total scores of 12 or lower on the ADI-R algorithm.

Logistic Regressions Logistic regressions could only be performed for children for who NVIQ was available. With logistic regressions, the contribution of the individual domains to a clinical classification of ASD versus NS was investigated, with all other domains, age and NVIQ in the analyses. In the current sample, the SA/SC domains contributed significantly to a clinical diagnosis of ASD versus NS in all developmental cells [12-20/NV21-47 odds ratio (OR) 1.44, 95% CI 1.20–1.72, p. Discussion The current paper aims to make a modest contribution to the literature by examining aspects of the validity of the ADI-R algorithms for toddlers and preschoolers (Kim and Lord ) in an independent and large non-US sample (N = 1,104).

With respect to construct validity, the three factor structure as found by Kim and Lord () fitted the data well. In the current sample, the specific items fitted well into the specific ADI-R toddler and preschooler domains, in line with the values of Kim and Lord and the replication studies (Kim et al. The fit indices of the three factor model were satisfactory to good, resembling the ones in the US samples and indicating that the new ADI-R algorithm structure can be applied to the non-US data. Correlations between factors were comparable to those in the CPEA/STAART ( r =.69–.94; Kim et al.

) and NIMH samples ( r =.55–.99; Kim and Lord ) indicating the same high correlations between the three factors. In particular the high correlations between SA/SC and IGP/RPI indicated that these domains were not independent from each other. Another finding that corroborated the construct validity was the relatively low correlation between the algorithm scores and age and level of cognitive functioning. The levels of these correlations were comparable to those in the Michigan study (r. Limitations Although the total sample size of the current study was large, the sample consisted of children from many different sites and is thus not a true replication study, given the different methodologies for assessment and diagnostic procedures, and ascertainment of samples. The sites provided a wide variety of samples recruited for different purposes (clinical referral/screening, first line/second line); in diagnostic groupings (some TD only, others NS only, others mainly ASD); from several populations (prediagnosed/undiagnosed; specialized departments/generic departments); with a range in severity of symptoms, age distribution (very young only versus broader), number of participants and level of cognitive functioning. However, unfortunately the individual sample sizes were too small to allow any additional analysis for any individual sites.

Conclusion The current study indicates that the construct validity of the algorithms for toddlers and preschoolers as proposed by Kim and Lord () was applicable in a large, independent, non-US sample. The selected ADI-R items fitted into the proposed domains SA/SC, RRB and IGP/RPI in the non-US sample as well as in the US sample. This indicates that the theoretical concept of the ADI-R in toddlers and young preschoolers seemed to be the same for US and non-US samples. However, in the current sample somewhat lower diagnostic validity was found, with satisfactorily high specificities but only moderate sensitivities. Although children with a clinical ASD diagnosis in the 12-20/NV21-47 and SW21-47 cells were largely recognized as children in the mild-to-moderate or moderate-to-severe concern ranges, nearly half of the children with a clinical ASD diagnosis in the PH21-47 cell fell into the little-to-no concern range.