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.

Purpose Data mining algorithms using electronic health records (EHRs) are useful in large-scale population-wide studies to classify etiology and comorbidities ( Casey et al., 2016 ). Here, we apply this approach to developmental language disorder (DLD), a prevalent communication disorder whose risk factors and epidemiology remain largely undiscovered. Method We first created a reliable system for manually identifying DLD in EHRs based on speech-language pathologist (SLP) diagnostic expertise. We then developed and validated an automated algorithmic procedure, called, Automated Phenotyping Tool for identifying DLD cases in health systems data (APT-DLD), that classifies a DLD status for patients within EHRs on the basis of ICD (International Statistical Classification of Diseases and Related Health Problems) codes. APT-DLD was validated in a discovery sample ( N = 973) using expert SLP manual phenotype coding as a gold-standard comparison and then applied and further validated in a replication sample of N = 13,652 EHRs. Results In the discovery sample, the APT-DLD algorithm correctly classified 98% (concordance) of DLD cases in concordance with manually coded records in the training set, indicating that APT-DLD successfully mimics a comprehensive chart review. The output of APT-DLD was also validated in relation to independently conducted SLP clinician coding in a subset of records, with a positive predictive value of 95% of cases correctly classified as DLD. We also applied APT-DLD to the replication sample, where it achieved a positive predictive value of 90% in relation to SLP clinician classification of DLD. Conclusions APT-DLD is a reliable, valid, and scalable tool for identifying DLD cohorts in EHRs. This new method has promising public health implications for future large-scale epidemiological investigations of DLD and may inform EHR data mining algorithms for other communication disorders. Supplemental Material https://doi.org/10.23641/asha.12753578