APT-DLD: An algorithm for identifying DLD in EHRs (Walters et al., 2020)
Creators: name: Walters, Courtney E.
name: Rachana Nitin
name: Margulis, Katherine
name: Boorom, Olivia
name: Gustavson, Daniel E.
name: Bush, Catherine T.
name: Davis, Lea K.
name: Below, Jennifer E.
name: Cox, Nancy J.
name: Camarata, Stephen M.
name: Gordon, Reyna L.
Publisher: name: ASHA journals
Year: 2020
Subjects: Language

description: <b>Purpose: </b>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.<b>Method: </b>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 (<i>N</i> = 973) using expert SLP manual phenotype coding as a gold-standard comparison and then applied and further validated in a replication sample of <i>N</i> = 13,652 EHRs.<b>Results: </b>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.<b>Conclusions: </b>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.<b><br></b><b>Supplemental Material S1.</b> Developmental language disorder (DLD) manual chart review rubric.<br><b>Supplemental Material S2. </b>Intercoder reliability for research assistant coders and SLP coders for 10% of the discovery sample.<br><b>Supplemental Material S3. </b>Determining the DLD phenotype among EHRs retrieved from a broad search for LD symptoms. <br>Walters, C. E., Jr., Nitin, R., Margulis, K., Boorom, O., Gustavson, D. E., Bush, C. T., Davis, L. K., Below, J. E., Cox, N. J., Camarata, S. M., &amp; Gordon, R. L. (2020). Automated Phenotyping Tool for identifying developmental language disorder cases in health systems data (APT-DLD): A new research algorithm for deployment in large-scale electronic health record systems. <i>Journal of Speech, Language, and Hearing Research</i>. https://doi.org/10.1044/2020_JSLHR-19-00397