GDx神经纤维厚度分析仪在正常中老年国人中的应用评价

Assessment of nerve fiber layer in healthy middle-aged Chinese with scanning laser polarimetry

目的评价GDx神经纤维厚度分析仪在正常中老年国人中的检测意义。方法采用GDx神经纤维厚度分析仪两种检测模式检测正常中老年国人的视网膜神经纤维层(RNFL)厚度。测量参数:TSNIT平均延迟、上方平均延迟、下方平均延迟、TSNIT标准差、神经纤维指数(NFI)。结果VCC模式:5项参数分别为56.17±5.25,68.35±7.20,67.31±8.06,23.46±4.31,17.15±10.09。ECC模式:5项参数分别为54.15±5.12,67.28±8.33,67.91±8.70,25.80±3.72,18.88±10.51。两种模式参数检测结果均在正常范围,除下方平均延迟差异有统计学意义外其余4项参数之间的差异无统计学意义,所有检测参数指标与年龄均无统计学意义的相关性。结论GDx神经纤维厚度分析仪对正常中老年国人RNFL厚度分布的测定结果符合生理解剖特点。VCC和ECC两种模式对检测正常中老年国人的RNFL厚度差异无统计学意义。

Objective GDx is a new scanning laser polarimetry instrument. It can perform objective and repeatable measurement on the retinal nerve fiber layer. Present study was to evaluate the retinal nerve fiber layer （RNFL） thickness with scanning laser polarimetry in healthy middle-aged Chinese. Methods Ninety eyes of 45 healthy subjects aged from 42 to 68 years （mean 54：1：9.2 years） underwent RNFL scanning with the GDx variable corneal compensation（GDx-VCC） and enhanced corneal compensation（GDx-ECC） algorithms in scanning laser polarimetry. GDx parameters including temporal-, superior- and nasal-inferior-temporal （TSNIT） average delay, superior average delay, inferior average delay, TSNIT standard deviation, and nerve fiber indicator （NFI） were analyzed. Results Mean values of TSNIT average delay, superior average delay, inferior average delay,TSNIT standard deviation and NFI under GDx-VCC algorithm were 56.17 ± 5.25,68.35 ± 7.20,67.31 ± 8.06, 23.46 ±4.31 and 17.15 ± 10. 09 ;while the GDx-ECC parameter values were 54. 15 ± 5.12,67.28 ± 8.33,67.91 ± 8.70,25.80 ±3.72,18.88 ±10. 5 1. The differences of RNFL values in TSNIT average, superior average, TSNIT standard deviation and NFI from GDx-VCC and GDx-ECC algorithms were not statistically significant except of the inferior average. All parameter data were below normal ranges and were not correlated with age. Conclusion RNFL values in the healthy middle-aged Chinese obtained by GDx are in agreement with normal physiological and anatomical traits. The parameters from GDx-VCC are similar to those from GDx-ECC algorithms.