19 Mar Age-Related Macular Degeneration: Algorithm and App as Practical Prediction Tools
MedicalResearch.com: What are the main findings of the study?
Answer: In this study, we found that advanced age-related macular
degeneration (AMD) is predictable by using clinically readily available
information. We devised a simple algorithm to summarize the clinical
predictors and showed the validity of our prediction model in both
clinic-based and community-based cohorts. We also develop an
application (App) for the iPhone and iPad as a practical tool for our
MedicalResearch.com: Were any of the findings unexpected?
Answer: It has been recognized that genetic factors play a significant
role in the development of age-related macular
degeneration. However, we found that even without
genetic information, a combination of readily available, noninvasive,
clinical risk factor information provided in the patient history and eye
examinations allows clinicians to predict eye-specific risk for advanced
age-related macular degeneration at an accuracy comparable to that in models including genetic predictors.
MedicalResearch.com: What should clinicians and patients take away from your report?
Answer: Our data showed that, in addition to age, gender, education
level, race, and smoking status, a fundus photographic eye examination
is of great value in the prediction of advanced age-related macular
degeneration. Patients aged 55+ years should consult their eye doctors to schedule regular eye examinations to rule out early retina signs, such as pigment abnormality
MedicalResearch.com: What recommendations do you have for future research as a result of this study?
Answer: Many systemic disorders have retinal manifestations that are
valuable in screening, diagnosis, staging, and management of the
conditions, such as diabetes, cardiovascular disease, etc. While
physicians’ primary concern may be in these systemic disorders, they can
play a key role in the early identification of blinding retinal
diseases, such as age-related macular degeneration. Therefore, an easy-to-use retina imaging and grading system in conjunction with our prediction model will be very
helpful for screening of patients at risk of developing advanced AMD.
The value of such a system will be even more profound if the system
could be tractable enough for public health practitioners or nurses to
use in communities.