Algorithms for the identification of ophthalmic diseases in medico-administrative databases: A systematic review.

CONTEXT: Medico-administrative databases (MADs) are increasingly used in comparative effectiveness research. OBJECTIVE: To conduct a systematic review of algorithms used for the identification of key ophthalmic diseases in MADs: age-related macular degeneration (AMD), diabetic retinopathy (DR)/ diabetic macular edema (DME), glaucoma, cataract and uveitis. METHODS: We searched PubMed between June 30, 2016, and August 6, 2024, using keywords related to MADs and the ophthalmic diseases of interest. Two reviewers independently selected studies and extracted data. RESULTS: From the 1719 references identified, 315 were selected describing a total of 523 algorithms. Approximately half of the study objectives were related to the identification of factors associated with the onset of ophthalmic diseases, exacerbation, or hospitalization (48%, n = 151). Only 2% (n = 6) focused exclusively on the development and/or validation of algorithms. Most studies were from Taiwan (36%, n = 113), Korea (27%, n = 84) and the United States (20%, n = 64). From the 523 algorithms, a validation was mentioned for 47 (9%). After regrouping close algorithms, 433 different algorithms were identified concerning glaucoma (34%, n = 146), DR/DME (26%, n = 114), AMD (18%, n = 80), cataract (11%, n = 47) and uveitis (11%, n = 46). About half of these algorithms used diagnosis codes only (58%, n = 251), while others combined diagnosis codes and procedures (16%, n = 69), or diagnosis codes and drugs (12%, n = 50). CONCLUSION: This systematic review showed heterogeneity between algorithms used to identify the same pathology, raising the question of which ones are more appropriate to use in a particular context. Moreover, most algorithms were not validated despite the potential impact on study results.