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Correlating Perimetric Indices with Three Nerve Fiber Layer Thickness Measures

Purpose To determine which of three estimates of retinal nerve fiber layer thickness (RNFLT) correlate best with visual field sensitivity measured using standard automated perimetry (SAP). Methods Data were collected from 400 eyes of 209 participants enrolled in the Portland Progression Project. These individuals ranged from high-risk suspects to having non–end-stage glaucoma. In each eye, three measures of average RNFLT (spectral domain optical coherence tomography [SDOCT], scanning laser polarimetry [SLP], confocal scanning laser tomography [CSLT]) and SAP (Humphrey HFAII) were performed on the same day. Mean deviation (MD), mean sensitivity (MS), and pattern standard deviation (PSD) were linearized using the equations MDLin = 10(MD*0.1), MSLin = 10(MS*0.1), and PSDLin = 10(PSD*−0.1). Correlations between each of the estimates of RNFLT and each of the functional metrics were calculated (nine total). Pearson correlations and generalized estimating equations (GEE) were used to calculate the strength and significance of the correlations. Results Linearized MS had the strongest correlation with SDOCT (r = 0.57), intermediate with SLP (r = 0.40), and weakest with CSLT (r = 0.13). When multiple RNFLT measures were included in a GEE model to predict MSLin, SDOCT was consistently predictive (p < 0.001) whereas CSLT was never predictive in these multivariate models. Similar findings were observed for MDLin and PSDLin. Conclusions Average RNFLT estimated from SDOCT predicts SAP status significantly better than average RNFLT estimated from SLP or CSLT.