A study published in the Journal of Neurodevelopmental Disorders describes how researchers developed an algorithm to search for attention deficit hyperactivity disorders (ADHD) phenotypes in hospital-based electronic health records (EHR).
The study, ‘An electronic health record (EHR) phenotype algorithm to identify patients with attention deficit hyperactivity disorders (ADHD) and psychiatric comorbidities’, notes that as ADHD often presents with comorbidities, it is often “unclear whether the symptoms causing impairment are due to the comorbidity or the underlying ADHD”. Therefore, it is “highly important to establish an algorithm that identifies ADHD and comorbidities in order to improve research on ADHD using biorepository and other electronic record data”.
The researchers explain how they sought to develop an algorithm to isolate cases with ADHD from cases with ADHD and comorbidities “more effectively for efficient future searches in large biorepositories”. A multi-source algorithm was developed to provide a more complete view of the patient’s EHR.
Using the biobank at the Center for Applied Genomics at Children’s Hospital of Philadelphia, the study “mined EHRs from 2009 to 2016 using International Statistical Classification of Diseases and Related Health Problems (ICD) codes, medication history and keywords specific to ADHD, and comorbid psychiatric disorders to facilitate genotype-phenotype correlation efforts,” the study states. “Chart abstractions and behavioral surveys added evidence in support of the psychiatric diagnoses. Most notably, the algorithm did not exclude other psychiatric disorders, as is the case in many previous algorithms.”
With data available from 51,293 subjects, 5840 ADHD cases were discovered. 46 percent of those cases had ADHD alone, and 54 percent had ADHD with psychiatric conditions.
“The results indicate ICD codes coupled with medication searches revealed the most cases,” the researchers note. “We discovered ADHD-related keywords did not increase yield. However, we found including ADHD-specific medications increased our number of cases by 21 percent.”
The researchers conclude their new algorithm “demonstrated the feasibility of the electronic algorithm approach to accurately diagnose ADHD and comorbid conditions, verifying the efficiency of our large biorepository for further genetic discovery-based analyses.”
To read the study in full, click here.
