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Classification of isolated versus multiple birth defects: An automated process for population-based registries.

Renata H Benjamin1, Joanne M Nguyen2, Margaret Drummond-Borg2

  • 1Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Houston, Texas, USA.

American Journal of Medical Genetics. Part A
|May 21, 2024
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Summary

Researchers developed automated methods to classify birth defects as isolated or multiple, considering developmental relationships. This improves accuracy in analyzing birth defect causes and implications for prior studies.

Keywords:
algorithmsbirth defectscase classificationcongenital abnormalitiesregistries

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Area of Science:

  • Epidemiology
  • Medical Genetics
  • Public Health

Background:

  • Epidemiologic studies often distinguish between isolated and multiple birth defects.
  • Classifying defects as isolated versus multiple can be challenging, especially when considering developmentally related co-occurring conditions.

Purpose of the Study:

  • To develop and apply automated classification procedures for differentiating isolated from multiple birth defects, accounting for developmentally related defects.
  • To improve the accuracy of birth defect analyses in large registries.

Main Methods:

  • Developed automated classification algorithms to identify isolated versus multiple birth defects.
  • Applied these procedures to a dataset of 235,544 nonsyndromic cases from the Texas Birth Defects Registry (1999-2018).
  • Analyzed the proportion of isolated defects and identified developmentally related co-occurring defects for specific conditions.

Main Results:

  • 89% of nonsyndromic cases were classified as having isolated defects.
  • Significant proportions of cases with spina bifida (44%), lower limb reduction defects (44%), and holoprosencephaly (32%) had developmentally related defects.
  • Proportions of isolated defects varied widely (25%-92%) across 43 specific defects.

Conclusions:

  • Automated classification accounting for developmental relationships is crucial for accurate birth defect analysis.
  • Findings highlight the need to consider isolated versus multiple defects and developmental relationships in risk factor association studies.
  • Implications for interpreting previous birth defect research and registry methodologies.