Identifying syndromes in studies of structural birth defects: Guidance on classification and evaluation of potential impact. Issue 1 (26th October 2022)

• Record Type: Journal Article
• 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.
• Is Part Of: American journal of medical genetics. Volume 191:Issue 1(2023)
• Page Start: 190
• Page End: 204
• Publication Date: 2022-10-26
• Subjects: birth defects -- chromosomal abnormalities -- nonsyndromic -- prevalence ratios -- syndromes; Medical genetics -- Periodicals; 616.14205
• DOI: 10.1002/ajmg.a.63014
• Languages: English
• ISSNs: 1552-4825

Abstract

Abstract: 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.