Details

Autor(en) / Beteiligte

• Urata, Carla N.
• Mariottoni, Eduardo B.
• Jammal, Alessandro A.
• Ogata, Nara G.
• Thompson, Atalie C.
• Berchuck, Samuel I.
• Estrela, Tais
• Medeiros, Felipe A.

Titel

Comparison of Short- And Long-Term Variability in Standard Perimetry and Spectral Domain Optical Coherence Tomography in Glaucoma

Ist Teil von

• American journal of ophthalmology, 2020-02, Vol.210, p.19-25

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2020

Quelle

MEDLINE

Beschreibungen/Notizen

• To assess short- and long-term variability on standard automated perimetry (SAP) and spectral domain optical coherence tomography (SD-OCT) in glaucoma. Prospective cohort. Ordinary least squares linear regression of SAP mean deviation (MD) and SD-OCT global retinal nerve fiber layer (RNFL) thickness were fitted over time for sequential tests conducted within 5 weeks (short-term testing) and annually (long-term testing). Residuals were obtained by subtracting the predicted and observed values, and each patient's standard deviation (SD) of the residuals was used as a measure of variability. Wilcoxon signed-rank test was performed to test the hypothesis of equality between short- and long-term variability. A total of 43 eyes of 43 glaucoma subjects were included. Subjects had a mean 4.5 ± 0.8 SAP and OCT tests for short-term variability assessment. For long-term variability, the same number of tests were performed and results annually collected over an average of 4.0 ± 0.8 years. The average SD of the residuals was significantly higher in the long-term than in the short-term period for both tests: 1.05 ± 0.70 dB vs. 0.61 ± 0.34 dB, respectively (P < 0.001) for SAP MD and 1.95 ± 1.86 μm vs. 0.81 ± 0.56 μm, respectively (P < 0.001) for SD-OCT RNFL thickness. Long-term variability was higher than short-term variability on SD-OCT and SAP. Because current event-based algorithms for detection of glaucoma progression on SAP and SD-OCT have relied on short-term variability data to establish their normative databases, these algorithms may be underestimating the variability in the long-term and thus may overestimate progression over time.