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Neuropediatrics 2019; 50(03): 164-169
DOI: 10.1055/s-0039-1685216
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Neurodevelopmental Disorders and Array-Based Comparative Genomic Hybridization: Sensitivity and Specificity using a Criteria Checklist for Genetic Test Performance

Alfonso Amado-Puentes
1   Department of Pediatrics, Child Neurology Unit, Álvaro Cunqueiro Hospital, Vigo, Pontevedra, Spain
,
Alfredo Reparaz-Andrade
2   Department of Clinical Analysis, Cytogenetics Unit, Álvaro Cunqueiro Hospital, Vigo, Pontevedra, Spain
,
Aida Del Campo-García
1   Department of Pediatrics, Child Neurology Unit, Álvaro Cunqueiro Hospital, Vigo, Pontevedra, Spain
,
Manuel Óscar Blanco-Barca
1   Department of Pediatrics, Child Neurology Unit, Álvaro Cunqueiro Hospital, Vigo, Pontevedra, Spain
,
Ángel Salgado-Barreira
3   Methodology and Statistics Unit, Galicia Sur Health Research Institute (IIS Galicia Sur), Vigo, Pontevedra, Spain
,
Víctor Del Campo-Pérez
4   Department of Preventive Medicine and Public Health, Álvaro Cunqueiro Hospital, Vigo, Pontevedra, Spain
,
José Ramón Fernández-Lorenzo
5   Department of Pediatrics, Álvaro Cunqueiro Hospital, Vigo, Pontevedra, Spain
› Author Affiliations Funding This work was supported by grants from the Spanish Pediatric Neurology Society (SENEP) and Mutual Médica Foundation.
Further Information

Publication History

16 January 2018

28 February 2019

Publication Date:
02 April 2019 (online)

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Abstract

Background Array-based comparative genomic hybridization (aCGH) is a molecular analysis method for identifying chromosomal anomalies or copy number variants (CNVs) correlating with clinical phenotypes. The aim of our study was to identify the most significant clinical variables associated with a positive outcome of aCGH analyses to develop a simple predictive clinical score.

Methods We conducted a cross-sectional study in a tertiary center comparing the genotype and phenotype of the cases. A score was developed using multivariate logistic regression. The best score cutoff point, sensitivity, specificity, positive and negative predictive values, and area under the curve were calculated with the receiver operating characteristic curve.

Results aCGH identified structural chromosomal alterations responsible for the disorder in 13.7% (95% confidence interval [CI]: 10.9–16.5) of our sample (570 patients analyzed by aCGH). Based on the most frequent phenotypic characteristics among patients with a pathogenic CNV, we have created a checklist with the following items: alteration of the cranial perimeter, stature < percentile (p) 3, weight < p3, presence of brain malformations, ophthalmological malformations, two or more dysmorphic features in the same patient, and autism spectrum disorder diagnosis. Using a score ≥1.5 as the cutoff point for the test, we obtained a sensitivity of 82.4% (95% CI: 73.1–91.8) and a specificity of 54.2% (95% CI: 49.7–58.7).

Conclusion All individuals with a score of 1.5 or higher should be genetically screened by aCGH. This approach can improve clinical indications for aCGH in patients with neurodevelopmental disorders, but the scoring system should be validated in an external group.

Keywords

neurodevelopmental - genetics - array-based CGH - autism spectrum disorders - scale - checklist
 

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