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

Area of Science:

• Pediatric Epidemiology
• Medical Informatics
• Genetics

Background:

• Syndromic and nonsyndromic birth defects may have different causes.
• Classifying syndromic status is resource-intensive for population-based registries.
• Existing registries often lack systematic methods for identifying infants with syndromes.

Purpose of the Study:

• To develop and validate criteria for systematically identifying infants with suspected syndromes.
• To assess the prevalence of syndromic conditions in a large birth defects registry.
• To quantify the potential bias introduced by including syndromic cases in risk factor analyses.

Main Methods:

• Developed criteria for syndrome classification based on type and required effort (e.g., text search).
• Applied the algorithm to the Texas Birth Defects Registry (TBDR) data (1999-2014).
• Utilized a bias analysis tool to estimate the impact of including syndromic cases on prevalence ratios.

Main Results:

• 15% of 207,880 infants with birth defects in the TBDR had suspected syndromes.
• The proportion of suspected syndromes varied significantly by defect type (e.g., 28.5% for atrioventricular septal defects to 98.9% for pyloric stenosis).
• Inclusion of syndromic cases in analyses could introduce up to 50.0% bias in prevalence ratios.

Conclusions:

• A novel framework enables systematic identification of infants with syndromic conditions.
• Implementation can harmonize syndromic classification across registries.
• This approach can reduce bias in epidemiological studies of birth defects and their risk factors.