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Testing the accuracy of an observation-based classifier for rapid detection of autism risk.

Duda M, Kosmicki JA, Wall DP - Transl Psychiatry (2014)

Bottom Line: We tested OBC outcomes against the outcomes provided by the original and current ADOS algorithms, the best estimate clinical diagnosis, and the comparison score severity metric associated with ADOS-2.The OBC was significantly correlated with the ADOS-G (r=-0.814) and ADOS-2 (r=-0.779) and exhibited >97% sensitivity and >77% specificity in comparison to both ADOS algorithm scores.The correlation between the OBC score and the comparison score was significant (r=-0.628), suggesting that the OBC provides both a classification as well as a measure of severity of the phenotype.

View Article: PubMed Central - PubMed

Affiliation: Division of Systems Medicine, Department of Pediatrics, Stanford University, Stanford, CA, USA.

ABSTRACT
Current approaches for diagnosing autism have high diagnostic validity but are time consuming and can contribute to delays in arriving at an official diagnosis. In a pilot study, we used machine learning to derive a classifier that represented a 72% reduction in length from the gold-standard Autism Diagnostic Observation Schedule-Generic (ADOS-G), while retaining >97% statistical accuracy. The pilot study focused on a relatively small sample of children with and without autism. The present study sought to further test the accuracy of the classifier (termed the observation-based classifier (OBC)) on an independent sample of 2616 children scored using ADOS from five data repositories and including both spectrum (n=2333) and non-spectrum (n=283) individuals. We tested OBC outcomes against the outcomes provided by the original and current ADOS algorithms, the best estimate clinical diagnosis, and the comparison score severity metric associated with ADOS-2. The OBC was significantly correlated with the ADOS-G (r=-0.814) and ADOS-2 (r=-0.779) and exhibited >97% sensitivity and >77% specificity in comparison to both ADOS algorithm scores. The correspondence to the best estimate clinical diagnosis was also high (accuracy=96.8%), with sensitivity of 97.1% and specificity of 83.3%. The correlation between the OBC score and the comparison score was significant (r=-0.628), suggesting that the OBC provides both a classification as well as a measure of severity of the phenotype. These results further demonstrate the accuracy of the OBC and suggest that reductions in the process of detecting and monitoring autism are possible.

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Related in: MedlinePlus

Correspondence between the observation-based classifier (OBC) score and the Autism Diagnostic Observation Schedule (ADOS). Correlation to the original ADOS-G (a) and the revised ADOS-2 (b) algorithm was high, r=−0.814 and −0.779, respectively.
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fig1: Correspondence between the observation-based classifier (OBC) score and the Autism Diagnostic Observation Schedule (ADOS). Correlation to the original ADOS-G (a) and the revised ADOS-2 (b) algorithm was high, r=−0.814 and −0.779, respectively.

Mentions: Fifty-three individuals were misclassified as non-spectrum by the OBC when the ADOS-G classified them as spectrum cases (Figure 1a). Of these, only two met thresholds for autism classifications. Of the remaining 51, 36 had borderline domain and total scores for autism spectrum classification and 1 was labeled within the research database as having a clinical diagnosis of non-spectrum.


Testing the accuracy of an observation-based classifier for rapid detection of autism risk.

Duda M, Kosmicki JA, Wall DP - Transl Psychiatry (2014)

Correspondence between the observation-based classifier (OBC) score and the Autism Diagnostic Observation Schedule (ADOS). Correlation to the original ADOS-G (a) and the revised ADOS-2 (b) algorithm was high, r=−0.814 and −0.779, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150240&req=5

fig1: Correspondence between the observation-based classifier (OBC) score and the Autism Diagnostic Observation Schedule (ADOS). Correlation to the original ADOS-G (a) and the revised ADOS-2 (b) algorithm was high, r=−0.814 and −0.779, respectively.
Mentions: Fifty-three individuals were misclassified as non-spectrum by the OBC when the ADOS-G classified them as spectrum cases (Figure 1a). Of these, only two met thresholds for autism classifications. Of the remaining 51, 36 had borderline domain and total scores for autism spectrum classification and 1 was labeled within the research database as having a clinical diagnosis of non-spectrum.

Bottom Line: We tested OBC outcomes against the outcomes provided by the original and current ADOS algorithms, the best estimate clinical diagnosis, and the comparison score severity metric associated with ADOS-2.The OBC was significantly correlated with the ADOS-G (r=-0.814) and ADOS-2 (r=-0.779) and exhibited >97% sensitivity and >77% specificity in comparison to both ADOS algorithm scores.The correlation between the OBC score and the comparison score was significant (r=-0.628), suggesting that the OBC provides both a classification as well as a measure of severity of the phenotype.

View Article: PubMed Central - PubMed

Affiliation: Division of Systems Medicine, Department of Pediatrics, Stanford University, Stanford, CA, USA.

ABSTRACT
Current approaches for diagnosing autism have high diagnostic validity but are time consuming and can contribute to delays in arriving at an official diagnosis. In a pilot study, we used machine learning to derive a classifier that represented a 72% reduction in length from the gold-standard Autism Diagnostic Observation Schedule-Generic (ADOS-G), while retaining >97% statistical accuracy. The pilot study focused on a relatively small sample of children with and without autism. The present study sought to further test the accuracy of the classifier (termed the observation-based classifier (OBC)) on an independent sample of 2616 children scored using ADOS from five data repositories and including both spectrum (n=2333) and non-spectrum (n=283) individuals. We tested OBC outcomes against the outcomes provided by the original and current ADOS algorithms, the best estimate clinical diagnosis, and the comparison score severity metric associated with ADOS-2. The OBC was significantly correlated with the ADOS-G (r=-0.814) and ADOS-2 (r=-0.779) and exhibited >97% sensitivity and >77% specificity in comparison to both ADOS algorithm scores. The correspondence to the best estimate clinical diagnosis was also high (accuracy=96.8%), with sensitivity of 97.1% and specificity of 83.3%. The correlation between the OBC score and the comparison score was significant (r=-0.628), suggesting that the OBC provides both a classification as well as a measure of severity of the phenotype. These results further demonstrate the accuracy of the OBC and suggest that reductions in the process of detecting and monitoring autism are possible.

Show MeSH
Related in: MedlinePlus