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敏感性可靠的限制视野检测算法对重新测试变异性的影响

Gardiner SK, Mansberger SL

期刊名称:Investigative ophthalmology & visual science

卷期:2016年第57卷第13期

摘要

目的:我们以前已发现,视野严重损害位置(<15-19 dB)的敏感性不可靠,且高度可变。该研究评估了一种检测算法,一种在约1000%的对比度下,损伤位置不存在非常高对比度刺激的算法,这种算法集中在剩余的部位,可取得更准确的估计。方法:1名经过培训的眼科技术人员对36名参与者的36只眼睛进行了两次两种不同的检测算法:ZEST0,灵敏性在035 dB范围内,ZEST15,灵敏性在1535 dB之间。相同算法两次运行之间的差异作为重新检测变异的指标。使用一种随机效应模型比较算法之间的差异。结果:ZEST15估计重新检验方差为ZEST0检验方差的53.1%,95%可信区间(50.5-55.7%)。在所有测试灵敏度≥17dB的部位中,ZEST15的变异为ZEST0检验方差的86.4%,95%可信区间(79.3-94.0%)。结论:限制可能的灵敏度估计范围可降低重新检测的可变性,不仅在严重损伤部位,在灵敏度更高的部位也是如此。未来的视野算法应避免严重受损部位的高对比度刺激。鉴于对大多数临床用途而言,低灵敏度无法可靠的检测,因此,重点研究受损较小部位的更简易检测法似乎更为有效。

Purpose:We have previously shown that sensitivities obtained at severely damaged visual field locations (<15-19 dB) are unreliable and highly variable. This study evaluates a testing algorithm that does not present very high contrast stimuli in damaged locations above approximately 1000% contrast, but instead concentrates on more precise estimation at remaining locations.Methods:A trained ophthalmic technician tested 36 eyes of 36 participants twice with each of two different testing algorithms: ZEST0, which allowed sensitivities within the range 0 to 35 dB, and ZEST15, which allowed sensitivities between 15 and 35 dB but was otherwise identical. The difference between the two runs for the same algorithm was used as a measure of test-retest variability. These were compared between algorithms using a random effects model with homoscedastic within-group errors whose variance was allowed to differ between algorithms.Results:The estimated test-retest variance for ZEST15 was 53.1% of the test-retest variance for ZEST0, with 95% confidence interval (50.5%-55.7%). Among locations whose sensitivity was 17 dB on all tests, the variability of ZEST15 was 86.4% of the test-retest variance for ZEST0, with 95% confidence interval (79.3%-94.0%).Conclusions:Restricting the range of possible sensitivity estimates reduced test-retest variability, not only at locations with severe damage but also at locations with higher sensitivity. Future visual field algorithms should avoid high-contrast stimuli in severely damaged locations. Given that low sensitivities cannot be measured reliably enough for most clinical uses, it appears to be more efficient to concentrate on more precise testing of less damaged locations.


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