It is the month of snowflakes, eggnog, and holiday cheer. Thank goodness! I suspect many of us need some cheering up at the end of this tumultuous year. In 2017, the intervals between natural disasters, from forest fires to Hurricanes Harvey and Irma, seemed to be filled distressingly often with man-made scandals, violence, and political mean-spiritedness.
To counteract those memories, and in recognition of the holiday spirit that appreciates the ghosts of retina past, I am dedicating this blog post to a few highlights from the last year’s wealth of JAMA Ophthalmology retina articles. Quick, obvious disclaimer—these are highlights based on my individual judgment alone. But I gleaned some interesting insights from these articles that were particularly relevant or held promise for the future, so I thought you might enjoy them as well.
The clinical trial evidence overwhelmingly supports the use of intravitreous antivascular endothelial growth factor (anti-VEGF) injections as first-line therapy for diabetic macular edema and age-related macular degeneration. Nonetheless, many of us retina specialists struggle with patients who don’t seem to respond optimally to anti-VEGF therapy. One option for eyes that are incomplete responders to anti-VEGF is to switch agents in the hope that these eyes will respond better to another type of anti-VEGF. But how do we know that eyes that are switched are truly benefiting from the new agent versus simply continued VEGF blockade? Ferris et al evaluated data from the CATT trial and the Diabetic Retinopathy Clinical Research Network Protocol I to better understand visual and anatomic outcomes in eyes that continue therapy with the same anti-VEGF agent after being judged “treatment failures.” They found that even in eyes with poor initial response over 3 or 6 months of anti-VEGF treatment, there were average gains of 3 to 5 letter improvement in vision and decreases in retinal thickness of 40 to 70 µm over the next 3 months of anti-VEGF therapy without switching. These data highlight the possibility of visual improvement with continued anti-VEGF therapy over the long term. The findings also emphasize the importance of choosing appropriate comparison groups as we evaluate alternative treatments for eyes that don’t appear to be responding optimally to anti-VEGF therapy.
My first blog post, just over a year ago now, asked whether optical coherence tomography (OCT) angiography was overrated or underappreciated. To tell the truth, I think the jury is still out on this question. Nonetheless, we saw a multitude of OCT angiography articles this year that have begun parsing out the potential value of this advanced imaging technique in specific types of retinal vascular disease and in segmentation of the different capillary plexuses. OCT angiography provides maps of the perfused retinal vasculature, but is not informative about retinal blood velocity or flow. An alternative approach using en face Doppler OCT to evaluate total retinal blood flow was detailed by the Fujimoto group in the March issue of JAMA Ophthalmology. This preliminary study found reductions in retinal blood flow in eyes with diabetic macular edema as compared with nondiabetic, control eyes. The technique is in its infancy, but could potentially provide useful clues about the role of blood flow in the onset and progression of retinal vascular disease.
A glimpse of the future arrived in articles that described outcomes after retinal gene therapy and automated grading algorithms for retinal images developed through deep convolutional neural networks. In the first of these papers, limited iatrogenic macular detachment was performed to deliver gene therapy for choroideremia in 5 patients. Resolution of the detachment occurred in all patients by 1 week and visual recovery or improvement was seen within 1 month after the procedure. The relatively rapid structural and functional recovery supports feasibility of this procedure for future gene therapy efforts. Deep learning algorithms for retinal image grading are also on the early path to clinical utility. The application of deep convolutional neural network methods to grading of images for retinal diseases, including age-related macular degeneration and diabetic retinopathy, may allow more efficient and cost-effective approaches to population screening and monitoring for vision-threatening conditions.
When I get discouraged by reading the headlines or watching the events on the nightly news, I can at least find refuge in the steady progress we’ve made in the field of retina. In the holiday season, visions of sugarplums do not dance in my head as much as thoughts of the patients whose vision we save with each successive advance. JAMA Ophthalmology and other journals play a critical role in furthering progress in our management and understanding of retinal pathology. At the end of 2017, I am looking forward to the forthcoming scientific discoveries we will read and ponder in 2018.