Identifying syndromes in studies of structural birth defects: Guidance on classification and evaluation of potential impact

Structural birth defects that occur in infants with syndromes may be etiologically distinct from those that occur in infants in whom there is not a recognized pattern of malformations; however, population‐based registries often lack the resources to classify syndromic status via case reviews. We dev...

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Bibliographic Details
Published in:American journal of medical genetics. Part A 2023-01, Vol.191 (1), p.190-204
Main Authors: Benjamin, Renata H., Mitchell, Laura E., Scheuerle, Angela E., Langlois, Peter H., Canfield, Mark A., Drummond‐Borg, Margaret, Nguyen, Joanne M., Agopian, A. J.
Format: Article
Language:English
Subjects:
Bias
Birth defects
chromosomal abnormalities
Classification
Congenital Abnormalities - diagnosis
Congenital Abnormalities - epidemiology
Congenital Abnormalities - genetics
Congenital defects
Heart Septal Defects
Humans
Infant
Infants
nonsyndromic
Prevalence
prevalence ratios
Registries
Risk factors
Stenosis
Syndrome
syndromes
Texas - epidemiology
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Summary:Structural birth defects that occur in infants with syndromes may be etiologically distinct from those that occur in infants in whom there is not a recognized pattern of malformations; however, population‐based registries often lack the resources to classify syndromic status via case reviews. We developed criteria to systematically identify infants with suspected syndromes, grouped by syndrome type and level of effort required for syndrome classification (e.g., text search). We applied this algorithm to the Texas Birth Defects Registry (TBDR) to describe the proportion of infants with syndromes delivered during 1999–2014. We also developed a bias analysis tool to estimate the potential percent bias resulting from including infants with syndromes in studies of risk factors. Among 207,880 cases with birth defects in the TBDR, 15% had suspected syndromes and 85% were assumed to be nonsyndromic, with a range across defect types from 28.5% (atrioventricular septal defects) to 98.9% (pyloric stenosis). Across hypothetical scenarios varying expected parameters (e.g., nonsyndromic proportion), the inclusion of syndromic cases in analyses resulted in up to 50.0% bias in prevalence ratios. In summary, we present a framework for identifying infants with syndromic conditions; implementation might harmonize syndromic classification across registries and reduce bias in association estimates.
ISSN:1552-4825
1552-4833
1552-4833
DOI:10.1002/ajmg.a.63014