Zhu Haogang, Crabb David P, Fredette Marie-Josée, Anderson Douglas R, Garway-Heath David F
Department of Optometry and Visual Science, City University London, London, England.
Arch Ophthalmol. 2011 Sep;129(9):1167-74. doi: 10.1001/archophthalmol.2011.112. Epub 2011 May 9.
To evaluate a new method of quantifying and visualizing discordance between structural and functional measurements in glaucomatous eyes by predicting the visual field (VF) from retinal nerve fiber layer thickness (RNFLT) using a bayesian radial basis function.
Five GDx VCC RNFLT scans and 5 Humphrey 24-2 Swedish Interactive Thresholding Algorithm VF tests were performed for 50 glaucomatous eyes from 50 patients. A best-available estimate (BAE) of the true VF was calculated as the pointwise median of these 5 replications. This BAE VF was compared with every RNFLT-predicted VF from the bayesian radial basis function and every measured VF. Predictability of VFs from RNFLT was established from previous data. A structure-function pattern discordance map and a structure-function discordance index (scores of 0-1) were established from the predictability limits for each structure-function measurement pair to quantify and visualize the discordance between the structure-predicted and measured VFs.
The mean absolute difference between the structure-predicted and BAE VFs was 3.9 dB. The mean absolute difference between measured and BAE VFs was 2.6 dB. The mean (SD) structure-function discordance index score was 0.34 (0.11). Ninety-seven (39%) of the structure-predicted VFs showed significant discordance (structure-function discordance index score >0.3) from measured VFs.
On average, the bayesian radial basis function predicts the BAE VF from RNFLT slightly less well than a measured VF from the 5 VFs composing the BAE VF. The pattern discordance map highlights locations with structure-function discordance, with the structure-function discordance index providing a summary index. These tools may help clinicians trust the mutually confirmatory structure-function measurements with good concordance or identify unreliable ones with poor concordance.
通过使用贝叶斯径向基函数从视网膜神经纤维层厚度（RNFLT）预测视野（VF），评估一种量化和可视化青光眼患者眼睛结构与功能测量之间不一致性的新方法。
对50例患者的50只青光眼眼睛进行了5次GDx VCC RNFLT扫描和5次Humphrey 24 - 2瑞典交互式阈值算法VF测试。将这5次重复测量的逐点中位数计算为真实VF的最佳可用估计值（BAE）。将该BAE VF与来自贝叶斯径向基函数的每个RNFLT预测VF以及每个测量VF进行比较。根据先前数据确定从RNFLT预测VF的可预测性。从每个结构 - 功能测量对的可预测性极限建立结构 - 功能模式不一致图和结构 - 功能不一致指数（0 - 1分），以量化和可视化结构预测VF与测量VF之间的不一致性。
结构预测VF与BAE VF之间的平均绝对差为3.9 dB。测量VF与BAE VF之间的平均绝对差为2.6 dB。结构 - 功能不一致指数评分的平均值（标准差）为0.34（0.11）。97个（39%）结构预测VF与测量VF显示出显著不一致（结构 - 功能不一致指数评分>0.3）。
平均而言，贝叶斯径向基函数从RNFLT预测BAE VF的效果略逊于由构成BAE VF的5次VF测量值。模式不一致图突出显示了结构 - 功能不一致的位置，结构 - 功能不一致指数提供了一个汇总指标。这些工具可能有助于临床医生信任具有良好一致性的相互验证的结构 - 功能测量结果，或识别一致性差的不可靠测量结果。
