A scoping review of electronic phenotyping methodologies used to identify peripheral artery disease in observational studies.

Billing data including International Classification of Diseases (ICD) codes are increasingly used to identify cohorts of patients with peripheral artery disease (PAD) in electronic health records (EHRs) and administrative claims databases (ACDs). However, the validity of common PAD phenotyping approaches is a central challenge to the utilization of EHR and ACD data. We present a scoping review of contemporary PAD observational studies to describe the electronic phenotyping strategies employed in PAD identification and propose recommendations for improvement. We searched two databases, MEDLINE and Web of Science, identifying a total of 748 articles that underwent title and abstract review. Of these articles, 163 met the criteria for full-text review, with 84 articles ultimately included in the study. We demonstrate that 19.0% of eligible studies utilized ICD, Ninth Revision (ICD-9) codes, 11.9% utilized ICD, Tenth Revision (ICD-10) codes, and 69.0% of studies utilized a combination of ICD-9 and ICD-10 codes in their electronic phenotyping methodology. Of the included studies, 76.2% utilized a single-code query approach for electronic phenotyping despite low diagnostic yield, and 21.4% utilized rule-based methods. Only five studies utilized logistic regression modeling, despite the demonstrated effectiveness of this method. The current study demonstrates high utilization of unreliable electronic phenotyping methods such as single-code-based queries, which severely limits research quality. Improvements in electronic phenotyping methods are necessary to leverage data from EHRs and ACDs for high-quality research.