Text-Based Identification of Herpes Zoster Ophthalmicus with Ocular Involvement in the Electronic Health Record: A Population-Based Study

Abstract

Background: Diagnosis codes are inadequate for accurately identifying herpes zoster ophthalmicus (HZO). Manual review of medical records is expensive and time-consuming, resulting in a lack of population-based data on HZO. Methods: We conducted a retrospective cohort study, including 87 673 patients aged ≥50 years who had a new HZ diagnosis and associated antiviral prescription between 2010 and 2018. We developed and validated an automated natural language processing (NLP) algorithm to identify HZO with ocular involvement (ocular HZO). We compared the characteristics of NLP-identified ocular HZO, nonocular HZO, and non-HZO cases among HZ patients and identified the factors associated with ocular HZO among HZ patients. Results: The NLP algorithm achieved 94.9% sensitivity and 94.2% specificity in identifying ocular HZO cases. Among 87 673 incident HZ cases, the proportion identified as ocular HZO was 9.0% (n = 7853) by NLP and 2.3% (n = 1988) by International Classification of Diseases codes. In adjusted analyses, older age and male sex were associated with an increased risk of ocular HZO; Hispanic and black race/ethnicity each were associated with a lower risk of ocular HZO compared with non-Hispanic white. Conclusions: The NLP algorithm achieved high accuracy and can be used in large population-based studies to identify ocular HZO, avoiding labor-intensive chart review. Age, sex, and race were strongly associated with ocular HZO among HZ patients. We should consider these risk factors when planning for zoster vaccination.