首页|Macular sector‐wise decision tree model for the prediction of parafoveal scotoma not detected by 24‐2 visual field test
Macular sector‐wise decision tree model for the prediction of parafoveal scotoma not detected by 24‐2 visual field test
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NSTL
Wiley
Abstract Background Development of a macular sector‐wise decision tree model (DTM) for the prediction of parafoveal scotoma. Methods This prospective study enrolled 126 patients with early‐stage open‐angle glaucoma (mean deviation ≥?6 decibels) without the signs of parafoveal scotoma on the 24‐2 visual field (VF) test (i.e., any abnormalities at the four innermost points). Based on the central 36 points of the 10‐2 pattern deviation plot, patients were classified as being with or without 10‐2 parafoveal scotoma. For the discrimination of patients from those without 10‐2 parafoveal scotoma, a macular ganglion cell‐inner plexiform layer (mGCIPL) sector‐wise DTM analysis was performed. Results Among 126 eyes without 24‐2 parafoveal scotoma, 10‐2 parafoveal scotoma was detected in 77 (61.1%) eyes. The balanced accuracy of DTM was best in the inferotemporal sector (0.9286; 95% CI, 0.7458–0.9697) and worst in the inferior sector (0.8373; 0.6484–0.9204). DTM revealed that even in the absence of VF abnormalities at the innermost 4 points on the 24‐2 test, (1) 10‐2 parafoveal scotoma should be strongly suspected when the adjacent 24‐2 perifoveal point in the correlated sector is abnormal; (2) if the 24‐2 perifoveal point is normal, and if the probability colour codes of the correlated mGCIPL sector are green, the probability of 10‐2 parafoveal scotoma is very low. Conclusions In clinical practice, the evaluation of the 24‐2 perifoveal test points along with the probability colour codes of mGCIPL can be a useful decision‐support tool in determining whether 10‐2 tests are needed for a given patient.
central scotomadecision treesglaucomavisual field test
Sooyeon Choe、Ahnul Ha、Jin Wook Jeoung、Ki Ho Park、Young Kook Kim
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Department of Ophthalmology,Seoul National University College of Medicine
NSTL
Wiley
