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N.G. Strouthidis, A. Scott, A.C. Viswanathan, D.P. Crabb, D.F. Garway–Heath; Monitoring Glaucomatous Visual Field Progression; The Effect Of A Novel Spatial Filter . Invest. Ophthalmol. Vis. Sci. 2006;47(13):1127.
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© ARVO (1962-2015); The Authors (2016-present)
To assess the impact of a novel visual field spatial filtering technique on the detection of glaucomatous progression.
198 ocular hypertensive (OHT) and 21 control subjects were examined prospectively (1993 – 2001) with regular full threshold Humphrey visual field (VF) and HRT testing. VF progression was assessed by point–wise linear regression (PLR) of sensitivity/time in PROGRESSOR for Windows software modified to include a novel spatial filter (Gardiner SK – Vision Research 2004). Standard criteria (slope > 1 dB/year, p < 0.01) were applied to both ‘raw’ and ‘filtered’ VF series. Specificity was estimated as the proportion of progressing control subjects and as the proportion of significantly improving subjects. HRT progression was assessed by linear regression of sectoral rim area/time (Strouthidis NG – ARVO 2005), using criteria of slope > 1 %/year, p < 0.05 (low variability series), p < 0.01 (high variability series). Series variability was quantified from the residual standard deviation from the linear regression analysis. The agreement between VF and HRT progression within the OHT group was compared using filtered and unfiltered VF data.
Specificity was estimated at 85.7 – 95.4 % using unfiltered standard PLR (10 improving subjects and 3 progressing controls), compared to 96.8 % – 100 % using filtered standard PLR (7 improving subjects and no progressing controls). 64 OHT subjects were identified as progressing by unfiltered standard PLR, 21 of whom also progressed by HRT. 62 OHT subjects progressed by filtered standard PLR, with 20 also progressing by HRT.
Application of the spatial filter improves estimated specificity with only a minimal diminution of presumed sensitivity. This filter may therefore be useful in the monitoring of glaucomatous progression, although it has yet to be applied to longitudinal SITA data.
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