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Lisha Deng, Stuart Keith Gardiner, Shaban Demirel; Reducing variability of visual field sensitivities in glaucoma through spatial filtering. Invest. Ophthalmol. Vis. Sci. 2014;55(13):964.
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© ARVO (1962-2015); The Authors (2016-present)
To determine if spatial filtering that uses functional and structural test data reduces variability while assessing glaucomatous visual field progression.
Humphrey visual field (VF) data (SITA Std, 24-2) and confocal scanning laser ophthalmoscopy (HRT) data from 112 eyes of 62 participants with high-risk ocular hypertension or mild to moderate glaucoma were included. All eyes had a minimum of 5 visits with mean follow up 5.3 years (range: 1.9-11.5 years). For each of the 52 non-blind spot VF locations, the filtered value (Ãj ) at location j was Ãj =0.5(Aj +Âj ), where Aj and Âj are the observed and predicted sensitivities. The predicted value was Âj = ∑i=137 čixi , where predictors xi included sensitivities at the other 25 locations in the same hemi-field and the logarithm of rim area at twelve 30° optic disc sectors. An independent dataset containing VF and HRT data from 1057 eyes of 637 clinic patients was used to estimate the čis. Least Absolute Shrinkage And Selection Operator was used to estimate čis, limited to 8 non-zero positive čis to simplify the filter and avoid over-fitting. At each location per eye, the trend over time was modeled by a linear model (LM), and a non-linear model (NLM) of the form A=a+cet , using filtered or unfiltered data from visits 1 to (n-1). The standard deviations (SD) of residuals from the trends, and prediction errors (PE; deviation at visit n between predicted and observed sensitivities) were compared between Â and A. The analyses were repeated after truncating VF data so that thresholds <15dB were set to equal 15dB to reduce noise.
SD of the residuals about the trend were reduced by filtering at all 52 VF locations (p<0.001) for all analyses. PE2 were reduced by filtering at 44 and 48 VF locations (p<0.05) for LM analyses on observed and truncated data respectively, and at all 52 VF locations (p<0.05) for both NLM analyses. Truncating data before filtering reduced the variability (p<0.01) at 38 and 40 VF locations for LM and NLM analyses.
Filtering which uses functional and structural test data can reduce variability in predicting pointwise sensitivities in longitudinal sequences of VF data, and improve the accuracy of predictions. Truncating data before filtering can further reduce variability.
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