Detection of Progression of Glaucomatous Visual Field Damage Using the Point-Wise Method with the Binomial Test

Abstract

PurposeTo compare the performance of newly proposed point-wise linear regression (PLR) with the binomial test (binomial PLR) against mean deviation (MD) trend analysis and permutation analyses of PLR (PoPLR), in detecting global visual field (VF) progression in glaucoma.Methods15 VFs (Humphrey Field Analyzer, SITA standard, 24-2) were collected from 96 eyes of 59 open angle glaucoma patients (6.0 ± 1.5 [mean ± standard deviation] years). Using the total deviation of each point on the 2nd to 16th VFs (VF2-16), linear regression analysis was carried out. The numbers of VF test points with a significant trend at various probability levels (p<0.025, 0.05, 0.075 and 0.1) were investigated with the binomial test (one-side). A VF series was defined as “significant” if the median p-value from the binomial test was <0.025. Similarly, the progression analysis was carried out using only second to sixth VFs (VF2-6). The performance of each method was evaluated using the ‘consistency measures’; proportion both significant (PBS): both VF series (VF2-6 and VF2-16) were “significant”, proportion both were not significant (PBNS): both were “not significant”, proportion inconsistently significant (PIS): VF2-16 was “not significant” but VF2-6 was “significant”. A similar analysis was carried out using VF2-7 and VF2-15 series, and the performance was compared with MD trend analysis and PoPLR.ResultsThe PBS of the binomial PLR method (0.14 to 0.86) was significantly higher than MD trend analysis (0.04 to 0.89) and PoPLR (0.09 to 0.93). The PIS of the proposed method (0.0 to 0.17) was significantly lower than the MD approach (0.0 to 0.67) and PoPLR (0.07 to 0.33). The PBNS of the three approaches were not significantly different.ConclusionsThe binomial BLR method gives more consistent results than MD trend analysis and PoPLR, hence it will be helpful as a tool to ‘flag’ possible VF deterioration.

Investigating the usefulness of a cluster-based trend analysis to detect visual field progression in patients with open-angle glaucoma

“…The following ‘consistency measures’ were used to measure performance: (1) proportion both progressing (PBP): trend analysis based on the subset of VFs was significant and negative, and trend analysis of the complete VF series (VF 1-10 ) was also significant and negative, (2) proportion both not progressing (PBNP): trend analysis of the subset of VFs was negative and not significant, and trend analysis of VF 1-10 was negative and not significant, (3) proportion inconsistent progression (PIP): trend analysis of reduced series suggested progression but trend analysis of VF 1-10 did not. In the current analysis, trend analysis of the longest VF series (VF 1-10 ) represents a surrogate for the ground truth; therefore, PBP, PIP and PBNP are proxy metrics for the true positive rate (sensitivity), the false positive rate and the true negative rate (specificity), respectively 16. The different metrics (PBP, PBNP and PIP) were compared between cluster trend analysis and mTD trend analysis, using a pairwise comparison method.…”

Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning

“…Additionally, MLAs have been used to classify and detect patterns of VF defects corresponding to retinal nerve fiber layer anatomy [22]. With regard to detecting progression, prior efforts have produced MLAs that have outperformed standardized global indices such as MD and, in some cases, human experts in identifying glaucoma progression [6,20,[22][23][24][25][26][27][28][29][30][31].…”

Redefining clinical outcomes and endpoints in glaucoma

“…Developing from this, permutation analyses of pointwise linear regression (PoPLR) has since been shown to be able to provide an overall statistical significance value of an individual patient's visual field deterioration by comparing the observed series to many different order permutations [162]. In combining PLR with a binomial test, it has been proposed the results can also be improved in consistency [113]. Most recently, analysis with non-stationary Weibull error regression and spatial enhancement (ANSWERS) has been developed to take into account increasing variability in sensitivity as the differential light sensitivity reduces, as well as spatial correlation between test locations, providing increased sensitivity for visual field progression when compared to PoPLR [257].…”