Abstract
Background
Diagnosis codes are inadequate for accurately identifying herpes zoster ophthalmicus. Manual review of medical records is expensive and time-consuming, resulting in a lack of population-based data on herpes zoster ophthalmicus.
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 2010-2018. We developed and validated an automated natural language processing (NLP) algorithm to identify herpes zoster ophthalmicus (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=7,853) by NLP and 2.3% (n=1,988) by ICD 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 to 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.