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

IMPORTANCE: Developmental 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. OBJECTIVE: To identify clinically relevant conditions tha...

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Autores principales: Nitin, Rachana, Shaw, Douglas M., Rocha, Daniel B., Walters, Courtney E., Chabris, Christopher F., Camarata, Stephen M., Gordon, Reyna L., Below, Jennifer E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857086/
https://www.ncbi.nlm.nih.gov/pubmed/36580336
http://dx.doi.org/10.1001/jamanetworkopen.2022.48060
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author Nitin, Rachana
Shaw, Douglas M.
Rocha, Daniel B.
Walters, Courtney E.
Chabris, Christopher F.
Camarata, Stephen M.
Gordon, Reyna L.
Below, Jennifer E.
author_facet Nitin, Rachana
Shaw, Douglas M.
Rocha, Daniel B.
Walters, Courtney E.
Chabris, Christopher F.
Camarata, Stephen M.
Gordon, Reyna L.
Below, Jennifer E.
author_sort Nitin, Rachana
collection PubMed
description IMPORTANCE: Developmental 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. OBJECTIVE: To identify clinically relevant conditions that co-occur with DLD at the population level. DESIGN, SETTING, AND PARTICIPANTS: This 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 MEASURES: Common and rare comorbidities of DLD at the population level were identified using EHRs and a phecode-based enrichment analysis. RESULTS: Comorbidity 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 RELEVANCE: This 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.
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spelling pubmed-98570862023-02-03 Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping Nitin, Rachana Shaw, Douglas M. Rocha, Daniel B. Walters, Courtney E. Chabris, Christopher F. Camarata, Stephen M. Gordon, Reyna L. Below, Jennifer E. JAMA Netw Open Original Investigation IMPORTANCE: Developmental 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. OBJECTIVE: To identify clinically relevant conditions that co-occur with DLD at the population level. DESIGN, SETTING, AND PARTICIPANTS: This 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 MEASURES: Common and rare comorbidities of DLD at the population level were identified using EHRs and a phecode-based enrichment analysis. RESULTS: Comorbidity 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 RELEVANCE: This 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. American Medical Association 2022-12-29 /pmc/articles/PMC9857086/ /pubmed/36580336 http://dx.doi.org/10.1001/jamanetworkopen.2022.48060 Text en Copyright 2022 Nitin R et al. JAMA Network Open. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the CC-BY License.
spellingShingle Original Investigation
Nitin, Rachana
Shaw, Douglas M.
Rocha, Daniel B.
Walters, Courtney E.
Chabris, Christopher F.
Camarata, Stephen M.
Gordon, Reyna L.
Below, Jennifer E.
Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping
title Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping
title_full Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping
title_fullStr Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping
title_full_unstemmed Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping
title_short Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping
title_sort association of developmental language disorder with comorbid developmental conditions using algorithmic phenotyping
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857086/
https://www.ncbi.nlm.nih.gov/pubmed/36580336
http://dx.doi.org/10.1001/jamanetworkopen.2022.48060
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