Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping

Area of Science:

• Pediatric Health
• Genetics and Genomics
• Clinical Informatics

Background:

• Developmental Language Disorder (DLD) is a prevalent childhood condition affecting up to 7% of children.
• The full biological profile and comorbidities of DLD remain incompletely understood, particularly at a population level.
• Accurate identification of DLD comorbidities is crucial for comprehensive patient care and research.

Purpose of the Study:

• To identify clinically significant health conditions that co-occur with DLD in a large population.
• To leverage electronic health records (EHRs) for population-level comorbidity analysis in DLD.
• To explore novel associations between DLD and other health phenotypes.

Main Methods:

• A case-control study utilizing EHR data from Vanderbilt University Medical Center and Geisinger Health System.
• Developmental Language Disorder (DLD) cases were identified using the Automated Phenotyping Tool for Identifying Developmental Language Disorder algorithm.
• Phenome enrichment analysis and phecode-based enrichment analysis were employed to identify comorbidities.

Main Results:

• Analysis included 5,273 DLD cases and 26,353 matched controls.
• Identified comorbidities include learning disorders, delayed milestones, acoustic nerve disorders, ADHD, and motor deficits.
• Novel associations were found with dermatitis, conjunctivitis, and nutritional factors, highlighting DLD's complexity.

Conclusions:

• This study identified both common and rare comorbidities associated with Developmental Language Disorder (DLD).
• Understanding these comorbidity profiles can help identify children at risk for additional health challenges.
• The findings offer new insights into the multifaceted clinical presentation of DLD.