A Pattern-Based OCT Metric for Glaucoma Detection.

Abstract

Purpose: To develop and test a novel optical coherence tomography (OCT) metric for the detection of glaucoma based on a logistic regression model (LRM) and known patterns of glaucomatous damage.

Methods: The six variables of the LRM were based on characteristic patterns of damage seen on the OCT thickness maps of the ganglion cell layer plus inner plexiform layer (GCL+) and retinal nerve fiber layer (RNFL). Two cohorts were used to develop the LRM. The healthy cohort consisted of 400 individuals randomly selected from a real-world reference database (RW-RDB) of OCT widefield scans from 4932 eyes/individuals obtained from 10 optometry practices. The glaucoma cohort consisted of 207 individuals from the same 10 practices but with OCT reports with evidence of optic neuropathy consistent with glaucoma (ON-G). Specificity was assessed with 396 eyes/individuals from a commercial RDB. Sensitivity was assessed with individuals with ON-G from different optometry practices.

Results: For the new LRM metric, the partial area under the reciever operating characteristic curve (AUROC) for specificity >90% was 0.92, and the sensitivity at 95% specificity was 88.8%. These values were significantly greater than those of a previously reported LRM metric (0.82 and 78.1%, respectively) and two common OCT thickness metrics: global circumpapillary RNFL (0.77 and 57.5%, respectively), and global GCL+IPL (0.72 and 47.6%, respectively).

Conclusions: The new metric outperformed other OCT metrics for detecting glaucomatous damage.

Translational relevance: The new metric has the potential to improve the accuracy of referrals from primary care to specialist care via risk scores and calculators, as well as glaucoma definitions for clinical trials. The individual variables of this model may also aid clinical diagnosis.