Retinal Nerve Fiber Thickness vs. Optic Disc Algorithms for Detecting Glaucoma

Retinal Nerve Fiber Thickness vs. Optic Disc Algorithms for Detecting Glaucoma
The early detection of glaucoma would result in better preservation of visual field in most patients. Manassakorn et al. compared the performance of the retinal nerve fiber layer (RNFL) thickness and optic disc algorithms as determined by optical coherence tomography (OCT) to detect glaucoma in an observational cross-sectional study at an academic tertiary-care center.

The authors considered one eye from each of 42 control subjects and 65 patients with open-angle glaucoma with visual acuity of > 20/40, and no other ocular pathologic condition was selected.

Two OCT algorithms were used: “fast RNFL thickness” and “fast optic disc.” Area under the receiver operating characteristic curves and sensitivities at fixed specificities were determined. Discriminating ability of the average RNFL thickness and RNFL thickness in clock-hour sectors and quadrants was compared with the parameters that were derived from the fast optic disc algorithm. Classification and regression trees were used to determine the best combination of parameters for the detection of glaucoma.

The average visual field mean deviation was 0.0 and –5.3 decibels in the control and glaucoma groups, respectively. The RNFL thickness at the 7 o’clock sector, inferior quadrant, and the vertical cup/diopter ratio had the highest area under the receiver operating characteristic curves. At 90 percent specificity, the best sensitivities from each algorithm were 86 percent for RNFL thickness at the 7 o’clock sector and 79 percent for horizontal integrated rim width (estimated rim area). The combination of inferior quadrant RNFL thickness and vertical cup/diopter ratio achieved the best classification (misclassification rate, 6.2 percent).

The fast optic disc algorithm performed as well as the fast RNFL thickness algorithm for discrimination of glaucoma from normal eyes. A combination of the two algorithms may provide enhanced diagnostic performance.

Multifocal Visual Evoked Potential a Sensitive, Specific Tool for Detecting Optic Neuritis

Multifocal Visual Evoked Potential a Sensitive, Specific Tool for Detecting Optic Neuritis

Multifocal visual evoked potentials (mVEP) is a method to diagnose optic pathway conditions by assessing visual evoked potentials as responses from multiple individual segments of the field of vision, allowing for objective information about amplitude to be combined with information on latency.

Optic neuritis usually develops in association with an autoimmune disorder that may be triggered by an infection. In some people, signs and symptoms of optic neuritis may be an indication of multiple sclerosis, a condition resulting in inflammation and damage to nerves in your brain and spinal cord.

Most people who experience a single episode of optic neuritis eventually recover their vision. Treatment with steroid medications may speed up vision recovery after optic neuritis.

Fraser et al. conducted a cross-sectional study to determine if mVEP can detect evidence of optic neuritis (ON) and whether the results can differentiate between ON as a manifestation of multiple sclerosis (MS) and purely inflammatory ON.

The authors found that not only was mVEP a sensitive and specific tool for detecting optic neuritis, but there existed a significant difference in latency analysis findings between patient groups classified according to the McDonald MS criteria.

They conclude that if this latency pattern does reflect future clinical course, then the mVEP could provide a means of identifying those with a greater risk of future MS in the early post-acute stage of ON from those with white matter changes on MRI, thus helping physicians to determine optimal treatment strategies.