Optimisation of an automated drusen-quantifying software for the analysis of drusen distribution in patients with age-related macular degeneration

Genetic risk score has added value over initial clinical grading stage in predicting disease progression in age-related macular degeneration

Several prediction models for progression of age-related macular degeneration (AMD) have been developed, but the added value of using genetic information in those models in addition to clinical characteristics is ambiguous. In this prospective cohort study, we explored the added value of genetics using a genetic risk score (GRS) based on 52 AMD-associated variants, in addition to the clinical severity grading at baseline as quantified by validated drusen detection software, to predict disease progression in 177 AMD patients after 6.5 years follow-up. The GRS was strongly associated with the drusen coverage at baseline (P < 0.001) and both the GRS and drusen coverage were associated with disease progression. When the GRS was added as predictor in addition to the drusen coverage, R² increased from 0.46 to 0.56. This improvement by the GRS was predominantly seen in patients with a drusen coverage <15%. In patients with a larger drusen coverage, the GRS had less added value to predict progression. Thus, genetic information has added value over clinical characteristics in predicting disease progression in AMD, but only in patients with a less severe disease stage. Patients with a high GRS should be made aware of their risk and could be selected for clinical trials for arresting progression.

Platform-Independent Cirrus and Spectralis Thickness Measurements in Eyes with Diabetic Macular Edema Using Fully Automated Software

PURPOSE

We determine whether the automated segmentation software, Duke Optical Coherence Tomography Retinal Analysis Program (DOCTRAP), can measure, in a platform-independent manner, retinal thickness on Cirrus and Spectralis spectral domain optical coherence tomography (SD-OCT) images in eyes with diabetic macular edema (DME) under treatment in a clinical trial.

METHODS

Automatic segmentation software was used to segment the internal limiting membrane (ILM), inner retinal pigment epithelium (RPE), and Bruch's membrane (BM) in SD-OCT images acquired by Cirrus and Spectralis commercial systems, from the same eye, on the same day during a clinical interventional DME trial. Mean retinal thickness differences were compared across commercial and DOCTRAP platforms using intraclass correlation (ICC) and Bland-Altman plots.

RESULTS

The mean 1 mm central subfield thickness difference (standard error [SE]) comparing segmentation of Spectralis images with DOCTRAP versus HEYEX was 0.7 (0.3) μm (0.2 pixels). The corresponding values comparing segmentation of Cirrus images with DOCTRAP versus Cirrus software was 2.2 (0.7) μm. The mean 1 mm central subfield thickness difference (SE) comparing segmentation of Cirrus and Spectralis scan pairs with DOCTRAP using BM as the outer retinal boundary was -2.3 (0.9) μm compared to 2.8 (0.9) μm with inner RPE as the outer boundary.

CONCLUSIONS

DOCTRAP segmentation of Cirrus and Spectralis images produces validated thickness measurements that are very similar to each other, and very similar to the values generated by the corresponding commercial software in eyes with treated DME.

TRANSLATIONAL RELEVANCE

This software enables automatic total retinal thickness measurements across two OCT platforms, a process that is impractical to perform manually.