2021 Publications
Identifying developmental stuttering and associated comorbidities in electronic health records and creating a phenome risk classifier.
Pruett Et Al, J Fluency Disord 2021
Purpose: This study aimed to identify cases of developmental stuttering and associated comorbidities in de-identified electronic health records (EHRs) at Vanderbilt University Medical Center, and, in turn, build and test a stuttering prediction model.
2020 Publications
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
Courtney E Walters Jr Et al, J Speech Lang Hear Res. 2020
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.
2019 Publications
The Role of Effortful Control in Stuttering Severity in Children: Replication Study
Shelly Jo Kraft Et. Al, Am J Speech Lang Pathol. 2019
Purpose: Because of the clinical significance of the initial study's findings, a replication study with a different, larger cohort of children who stutter was warranted to validate the reported outcomes. Method The current study assesses 98 children who stutter, ages 2;4 to 12;6 (years; months, M = 6;7), recruited from Perth, Australia. Results The results support the previous findings of Kraft, Ambrose, and Chon (2014) , with effortful control remaining the sole significant contributor to variability in stuttering severity, as rated by both parents and clinicians.
The Role of Effortful Control in Stuttering Severity in Children: Replication Study
Shelly Jo Kraft Et. Al, Am J Speech Lang Pathol. 2019
Purpose: Because of the clinical significance of the initial study's findings, a replication study with a different, larger cohort of children who stutter was warranted to validate the reported outcomes. Method The current study assesses 98 children who stutter, ages 2;4 to 12;6 (years; months, M = 6;7), recruited from Perth, Australia. Results The results support the previous findings of Kraft, Ambrose, and Chon (2014) , with effortful control remaining the sole significant contributor to variability in stuttering severity, as rated by both parents and clinicians.