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Ganglion Cell Complex Analysis in Glaucoma Patients: What Can It Tell Us?青光眼患者的神经节细胞复合体分析:它能告诉我们什么?
Eye Brain. 2020 Jan 31;12:33-44. doi: 10.2147/EB.S226319. eCollection 2020.
2
Questioning the "SPIN and SNOUT" rule in clinical testing.质疑临床检测中的“SPIN与SNOUT”规则。
Arch Physiother. 2019 Mar 7;9:4. doi: 10.1186/s40945-019-0056-5. eCollection 2019.
3
Can Macula and Optic Nerve Head Parameters Detect Glaucoma Progression in Eyes with Advanced Circumpapillary Retinal Nerve Fiber Layer Damage?黄斑和视神经头参数能否检测到有广泛的环周视网膜神经纤维层损伤的眼中青光眼的进展?
Ophthalmology. 2018 Dec;125(12):1907-1912. doi: 10.1016/j.ophtha.2018.05.020. Epub 2018 Jun 19.
4
Diagnostic ability of macular ganglion cell-inner plexiform layer thickness in glaucoma suspects.黄斑神经节细胞-内丛状层厚度对青光眼可疑患者的诊断能力
Medicine (Baltimore). 2017 Dec;96(51):e9182. doi: 10.1097/MD.0000000000009182.
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Short wave-automated perimetry (SWAP) versus optical coherence tomography in early detection of glaucoma.短波自动视野计(SWAP)与光学相干断层扫描在青光眼早期检测中的比较
Clin Ophthalmol. 2016 Sep 19;10:1819-1824. doi: 10.2147/OPTH.S116073. eCollection 2016.
6
Can ganglion cell complex assessment on cirrus HD OCT aid in detection of early glaucoma?Cirrus HD光学相干断层扫描上的神经节细胞复合体评估能否有助于早期青光眼的检测?
Saudi J Ophthalmol. 2015 Jul-Sep;29(3):201-4. doi: 10.1016/j.sjopt.2015.02.007. Epub 2015 Mar 23.
7
Macular structure-function relationship at various spatial locations in glaucoma.青光眼不同空间位置的黄斑结构-功能关系
Br J Ophthalmol. 2015 Oct;99(10):1412-8. doi: 10.1136/bjophthalmol-2014-306538. Epub 2015 Mar 31.
8
Comparing spectral-domain optical coherence tomography and standard automated perimetry to diagnose glaucomatous optic neuropathy.比较频域光学相干断层扫描和标准自动视野计在诊断青光眼性视神经病变中的应用。
J Glaucoma. 2015 Jun-Jul;24(5):e69-74. doi: 10.1097/IJG.0000000000000048.
9
Macular ganglion cell analysis for early detection of glaucoma.黄斑神经节细胞分析用于青光眼的早期检测。
Ophthalmology. 2014 Aug;121(8):1508-15. doi: 10.1016/j.ophtha.2014.02.019. Epub 2014 Apr 3.
10
Improving glaucoma detection using spatially correspondent clusters of damage and by combining standard automated perimetry and optical coherence tomography.利用空间对应的损伤簇和结合标准自动视野计和光学相干断层扫描来提高青光眼检测的准确率。
Invest Ophthalmol Vis Sci. 2014 Jan 29;55(1):612-24. doi: 10.1167/iovs.13-12351.

标准自动视野计、短波长自动视野计、视网膜神经纤维层厚度分析和节细胞层厚度分析在青光眼早期检测中的诊断能力比较。

Comparison of diagnostic ability of standard automated perimetry, short wavelength automated perimetry, retinal nerve fiber layer thickness analysis and ganglion cell layer thickness analysis in early detection of glaucoma.

机构信息

PBMA's H.V. Desai Eye Hospital, Pune, Maharashtra, India.

RNT Medical College, Udaipur, Rajasthan, India.

出版信息

Indian J Ophthalmol. 2021 May;69(5):1108-1112. doi: 10.4103/ijo.IJO_2409_20.

DOI:10.4103/ijo.IJO_2409_20
PMID:33913843
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8186634/
Abstract

PURPOSE

The aim of this study was to compare the diagnostic ability of macular ganglion cell layer (GCL) analysis using spectral domain optical coherence tomography against retinal nerve fiber layer analysis (RNFL), short-wavelength automated perimetry (SWAP), and standard automated perimetry (SAP) in early detection of glaucoma.

METHODS

Participants fulfilling the inclusion criteria were consecutively enrolled from the glaucoma clinic of tertiary care eye hospital in Western India from November 2015 to October 2016. The subjects underwent a detailed evaluation by trained glaucoma specialists. On suspicion of glaucoma, the patients underwent SAP, SWAP, and SD-OCT for GCL and RNFL analysis.

RESULTS

There were 91 patients in total of which experts classified 54 eyes into GON and 37 eyes into nonglaucomatous group. Sensitivity of SAP (42.59%) was significantly lower (P < 0.05) than that of average GCL thickness (79.63%) and average RNFL thickness (72.22%). Specificity and positive LR of SWAP (97.3% and 19.19, respectively) and SAP (94.6% and 7.88, respectively) were greater than those of GCL (81.08% and 4.21) and RNFL (67.57% and 2.23) parameters. Negative LR of average GCL thickness (0.25) was superior to that of average RNFL thickness (0.411), SWAP (0.495), and SAP (0.607).

CONCLUSION

Macular GCL parameters perform better than RNFL parameters in patients with early glaucomatous damage. There is superior ability of SWAP over SAP in detecting glaucomatous changes in glaucoma suspect group. GCL thickness analysis has higher sensitivity and negative likelihood ratio, whereas SWAP had higher specificity and positive likelihood ratio. Thus, combining both tests can lead to better diagnostic ability for early glaucomatous damage.

摘要

目的

本研究旨在比较使用谱域光相干断层扫描(SD-OCT)进行黄斑神经节细胞层(GCL)分析与视网膜神经纤维层(RNFL)分析、短波长自动视野计(SWAP)和标准自动视野计(SAP)在早期青光眼诊断中的诊断能力。

方法

2015 年 11 月至 2016 年 10 月,我们连续从印度西部三级护理眼科医院的青光眼诊所招募符合纳入标准的患者。接受过专门培训的青光眼专家对患者进行详细评估。怀疑青光眼时,对患者进行 SAP、SWAP 和 SD-OCT 检查,以进行 GCL 和 RNFL 分析。

结果

共纳入 91 例患者,其中专家将 54 只眼分为 GON 组,37 只眼分为非青光眼组。SAP 的敏感性(42.59%)明显低于平均 GCL 厚度(79.63%)和平均 RNFL 厚度(72.22%)(P<0.05)。SWAP 的特异性和阳性 LR(97.3%和 19.19)和 SAP(94.6%和 7.88)分别大于 GCL(81.08%和 4.21)和 RNFL(67.57%和 2.23)参数。平均 GCL 厚度的阴性 LR(0.25)优于平均 RNFL 厚度(0.411)、SWAP(0.495)和 SAP(0.607)。

结论

在早期青光眼损害患者中,黄斑 GCL 参数比 RNFL 参数表现更好。在青光眼疑似患者中,SWAP 比 SAP 具有更好的检测青光眼改变的能力。GCL 厚度分析具有更高的敏感性和阴性似然比,而 SWAP 具有更高的特异性和阳性似然比。因此,联合两种检测方法可以更好地诊断早期青光眼损害。