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Published on: March 29, 2022
Characteristics of a Large Database of Healthy Eyes From Optometry Practices: Implications for a Real-World Reference
Donald C Hood1,2, Mary Durbin3, Sol La Bruna4
1Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY, USA.
Purpose:
To compare an optical coherence tomography (OCT) real-world reference database (RW-RDB) of "healthy" eyes obtained from optometry practices to a commercial reference database (RDB).
Methods:
OCT scans from 6804 individuals 18 years and older were sampled from a larger database tested at 10 optometry practices involved in refractive and screening services. Employing a reading center method, OCT scans from both eyes of 4932 (4.9K) individuals were judged to be of acceptable quality with an absence of pathology. The 4.9K RW-RDB was compared to a commercial RDB with 398 eyes (398 RDB).
Results:
The means and distributions of global circumpapillary retinal nerve fiber layer (G-cpRNFL) and global ganglion cell layer (G-GCL) thickness, as well as five key anatomical parameters affecting cpRNFL thickness, were not significantly different for all but one parameter (fovea-to-disc distance) and one thickness metric (G-cpRNFL). In both cases, the difference amounted to less than 1.5%. By design, the number of 4.9K RW-RDB eyes 70 years and older (724, 14.7%) was greater than for the 398 RDB (40, 10.1%). The error bands on the 5% and 1% quantile regression lines (QRLs) were substantially narrower for the 4.9K RW-RDB.
Conclusions:
The 398 RDB and 4.9K RW-RDB have similar characteristics and appear to come from a similar population. However, the large size of the 4.9K RW-RDB leads to narrower error bands of the QRLs, which has the potential to increase accuracy.
Translational Relevance:
The larger RW-RDB offers the opportunity to better characterize healthy eyes for clinical diagnosis and clinical trials by furthering our understanding of the patterns of artifacts, exploring covariates, developing separate RW-RDBs, and/or improving AI models.
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