Prediction of visual field defects from macular optical coherence tomography in glaucoma using cluster analysis

PURPOSE: To assess the accuracy of cluster analysis‐based models in predicting visual field (VF) defects from macular ganglion cell‐inner plexiform layer (GCIPL) measurements in glaucomatous and healthy cohorts. METHODS: GCIPL measurements were extracted from posterior pole optical coherence tomography (OCT), from locations corresponding to central VF test grids. Models incorporating cluster analysis methods and corrections for age and fovea to optic disc tilt were developed from 493 healthy participants, and 5th and 1st percentile limits of GCIPL thickness were derived. These limits were compared with pointwise 5th and 1st percentile limits by calculating sensitivities and specificities in an additional 40 normal and 37 glaucomatous participants, as well as applying receiver operating characteristic (ROC) curve analyses to assess the accuracy of predicting VF results from co‐localised GCIPL measurements. RESULTS: Clustered models demonstrated globally low sensitivity, but high specificity in the glaucoma cohort (0.28–0.53 and 0.77–0.91, respectively), and high specificity in the healthy cohort (0.91–0.98). Clustered models showed similar sensitivities and superior specificities compared with pointwise methods (0.41–0.65 and 0.71–0.98, respectively). There were significant differences in accuracy between clusters, with relatively poor accuracy at peripheral macular locations (p < 0.0001 for all comparisons). CONCLUSIONS: Cluster analysis‐based models incorporating age correction and holistic consideration of fovea to optic disc tilt demonstrated superior performance in predicting VF results to pointwise methods in both glaucomatous and healthy eyes. However, relatively low sensitivity and poorer performance at the peripheral macula indicate that OCT in isolation may be insufficient to predict visual function across the macula accurately. With modifications to criteria for abnormality, the concepts suggested by the described normative models may guide prioritisation of VF assessment requirements, with the potential to limit excessive VF testing.