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Published on: May 26, 2023
Comparison of Novel Volumetric Microperimetry Metrics in Intermediate Age-Related Macular Degeneration: PINNACLE
Philipp Anders1,2,3,4, Ghislaine L Traber1, Maximilian Pfau1,2
1Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland.
Purpose:
To investigate and compare novel volumetric microperimetry (MP)-derived metrics in intermediate age-related macular degeneration (iAMD), as current MP metrics show high variability and low sensitivity.
Methods:
This is a cross-sectional analysis of microperimetry baseline data from the multicenter, prospective PINNACLE study (ClinicalTrials.gov NCT04269304). The Visual Field Modeling and Analysis (VFMA) software and an open-source implementation (OSI) were applied to calculate MP-derived hill-of-vison (HOV) surface plots and the total volume (VTOT) beneath the plots. Bland-Altman plots were used for methodologic comparison, and the association of retinal sensitivity metrics with explanatory variables was tested with mixed-effects models.
Results:
In total, 247 eyes of 189 participants (75 ± 7.3 years) were included in the analysis. The VTOT output of VFMA and OSI exhibited a significant difference (P < 0.0001). VFMA yielded slightly higher coefficients of determination than OSI and mean sensitivity (MS) in univariable and multivariable modeling, for example, in association with low-luminance visual acuity (LLVA) (marginal R2/conditional R2: VFMA 0.171/0.771, OSI 0.162/0.765, MS 0.133/0.755). In the multivariable analysis, LLVA was the only demonstrable predictor of VFMA VTOT (t-value, P-value: -7.5, <0.001) and MS (-6.5, <0.001).
Conclusions:
The HOV-derived metric of VTOT exhibits favorable characteristics compared to MS in evaluating retinal sensitivity. The output of VFMA and OSI is not exactly interchangeable in this cross-sectional analysis. Longitudinal analysis is necessary to assess their performance in ability-to-detect change.
Translational Relevance:
This study explores new volumetric MP endpoints for future application in therapeutic trials in iAMD and reports specific characteristics of the available HOV software applications.
Insights
Novel volumetric microperimetry (MP) metrics show promise for evaluating retinal sensitivity in intermediate age-related macular degeneration (iAMD). Total volume (VTOT) metrics offer advantages over mean sensitivity (MS) for iAMD assessment.
Area of Science:
- Ophthalmology
- Retinal Imaging
- Macular Degeneration Research
Background:
- Current microperimetry (MP) metrics for intermediate age-related macular degeneration (iAMD) exhibit high variability and low sensitivity.
- Novel volumetric MP-derived metrics are being investigated to improve assessment of retinal sensitivity in iAMD.
Purpose of the Study:
- To investigate and compare novel volumetric microperimetry (MP)-derived metrics in intermediate age-related macular degeneration (iAMD).
- To evaluate the performance of different software applications in calculating these new metrics.
Main Methods:
- Cross-sectional analysis of baseline data from the prospective PINNACLE study.
- Calculation of MP-derived hill-of-vision (HOV) surface plots and total volume (VTOT) using Visual Field Modeling and Analysis (VFMA) software and an open-source implementation (OSI).
- Comparison of VTOT outputs using Bland-Altman plots and mixed-effects models to assess association with explanatory variables like low-luminance visual acuity (LLVA).
Main Results:
- A significant difference was observed between VFMA and OSI VTOT outputs (P < 0.0001).
- VFMA demonstrated slightly higher coefficients of determination than OSI and mean sensitivity (MS) in association with LLVA.
- Low-luminance visual acuity (LLVA) was the only significant predictor for both VFMA VTOT and MS in multivariable analysis.
Conclusions:
- The HOV-derived VTOT metric shows favorable characteristics for evaluating retinal sensitivity in iAMD compared to MS.
- VFMA and OSI software outputs are not interchangeable in cross-sectional analysis.
- Longitudinal studies are required to determine the performance of these metrics in detecting changes over time.
