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

Author:

Nitin Rachana12,Shaw Douglas M.3,Rocha Daniel B.45,Walters Courtney E.267,Chabris Christopher F.8,Camarata Stephen M.9,Gordon Reyna L.1231011,Below Jennifer E.312

Affiliation:

1. Vanderbilt Brain Institute, Vanderbilt University, Nashville, Tennessee

2. Department of Otolaryngology–Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee

3. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee

4. Phenomic Analytics and Clinical Data Core, Geisinger, Danville, Pennsylvania

5. NewYork-Presbyterian Hospital, New York

6. Vanderbilt University Neuroscience Program, Vanderbilt University, Nashville, Tennessee

7. Loma Linda School of Medicine, Loma Linda University, Loma Linda, California

8. Geisinger Health System, Lewisburg, Pennsylvania

9. Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, Tennessee

10. Department of Psychology, Vanderbilt University, Nashville, Tennessee

11. Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, Tennessee

12. Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee

Abstract

ImportanceDevelopmental language disorder (DLD) is a common (with up to 7% prevalence) yet underdiagnosed childhood disorder whose underlying biological profile and comorbidities are not fully understood, especially at the population level.ObjectiveTo identify clinically relevant conditions that co-occur with DLD at the population level.Design, Setting, and ParticipantsThis case-control study used an electronic health record (EHR)–based population-level approach to compare the prevalence of comorbid health phenotypes between DLD cases and matched controls. These cases were identified using the Automated Phenotyping Tool for Identifying Developmental Language Disorder algorithm of the Vanderbilt University Medical Center EHR, and a phenome enrichment analysis was used to identify comorbidities. An independent sample was selected from the Geisinger Health System EHR to test the replication of the phenome enrichment using the same phenotyping and analysis pipeline. Data from the Vanderbilt EHR were accessed between March 2019 and October 2020, while data from the Geisinger EHR were accessed between January and March 2022.Main Outcomes and MeasuresCommon and rare comorbidities of DLD at the population level were identified using EHRs and a phecode-based enrichment analysis.ResultsComorbidity analysis was conducted for 5273 DLD cases (mean [SD] age, 16.8 [7.2] years; 3748 males [71.1%]) and 26 353 matched controls (mean [SD] age, 14.6 [5.5] years; 18 729 males [71.1%]). Relevant phenotypes associated with DLD were found, including learning disorder, delayed milestones, disorders of the acoustic nerve, conduct disorders, attention-deficit/hyperactivity disorder, lack of coordination, and other motor deficits. Several other health phenotypes not previously associated with DLD were identified, such as dermatitis, conjunctivitis, and weight and nutrition, representing a new window into the clinical complexity of DLD.Conclusions and RelevanceThis study found both rare and common comorbidities of DLD. Comorbidity profiles may be leveraged to identify risk of additional health challenges, beyond language impairment, among children with DLD.

Publisher

American Medical Association (AMA)

Subject

General Medicine

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