Validity of Algorithms to Identify Patients With Glaucoma Using the Japanese Claims Data.

PRCIS

Diagnostic or antiglaucoma drug records in the Japanese claims data showed a high validity in identifying glaucoma patients. Specific subtypes were identified with high specificity and negative predictive values but low sensitivity and positive predictive values.

PURPOSE

Despite the widespread use of administrative claims data in epidemiological research on glaucoma, only a few studies have investigated the validity of the methods in defining patients with glaucoma using diagnoses and drug records. We aimed to evaluate the validity of these algorithms in identifying patients with glaucoma using the Japanese claims data.

METHODS

Two ophthalmologists independently reviewed the medical charts and administrative claims data of 500 randomly selected patients who visited the Department of Ophthalmology of an academic hospital in 2019. We constructed 12 algorithms to identify patients with any type and specific subtypes of glaucoma using the claims records of diagnosis, antiglaucoma drugs, and visual field tests. We regarded the diagnosis of glaucoma based on the medical charts as the reference standard and calculated the sensitivity, specificity, and positive and negative predictive values of each algorithm based on the claims data.

RESULTS

The algorithms of ≥1 diagnostic record per year and ≥1 antiglaucoma drug record per year exhibited sensitivities of 94.6% and 89.2%, respectively, and specificities of 88.9% and 98.3%, respectively. An increase in the frequency of records resulted in a decreased sensitivity and slightly increased specificity. The addition of visual field tests did not improve the validity. The algorithms for specific subtypes of glaucoma exhibited high specificity and relatively low sensitivity.

CONCLUSION

Diagnostic or antiglaucoma drug records in the Japanese claims data were useful for identifying patients with glaucoma. Researchers should select identification algorithms based on the study design.