Abstract
Birth defects are prenatal morphological or functional anomalies. Associations among them are studied to identify their etiopathogenesis. The graph theory methods allow analyzing relationships among a complete set of anomalies. A graph consists of nodes which represent the entities (birth defects in the present work), and edges that join nodes indicating the relationships among them. The aim of the present study was to validate the graph theory methods to study birth defect associations. All birth defects monitoring records from the Estudio Colaborativo Latino Americano de Malformaciones Congénitas gathered between 1967 and 2017 were used. From around 5 million live and stillborn infants, 170,430 had one or more birth defects. Volume-adjusted Chi-Square was used to determine the association strength between two birth defects and to weight the graph edges. The complete birth defect graph showed a Log-Normal degree distribution and its characteristics differed from random, scale-free and small-world graphs. The graph comprised 118 nodes and 550 edges. Birth defects with the highest centrality values were nonspecific codes such as Other upper limb anomalies. After partition, the graph yielded 12 groups; most of them were recognizable and included conditions such as VATER and OEIS associations, and Patau syndrome. Our findings validate the graph theory methods to study birth defect associations. This method may contribute to identify underlying etiopathogeneses as well as to improve coding systems.
Highlights
Birth defects (BD) are prenatal morphological or functional anomalies, classified as major or minor according to their clinical or biological significance
Its edge density was 0.08, and its degree and EV centralities were 0.28 and 0.78, respectively. This BD graph (BDG) differed from the three models taken as reference (Table 1)
The median BDG values (CC, Degree assortativity (DA), and Average short path length (ASPL)) were not within the 95% confidence interval of the random graphs generated with Erdos & Renyi (ER) [30], and Barabasi & Albert (BA) [31] models, nor within random graphs generated with the same degree and weight distributions (SDD); the Telesford et al (2011) small-world index for the BDG was -0.30
Summary
The aim of the present study was to validate the graph theory methods to study birth defect associations. The aim of the present study was to verify the ability of the graph theory methods to identify already known BD associations, and thereby to consider its inclusion as a further tool used in BD surveillance
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