Abstract
Purpose :
The poor clinical outcome associated with uveal melanoma (UM) highlights the need for disease prevention through identification of patients at risk of developing UM. However, low- and high-risk patient profiles have yet to be defined. Herein, we aimed to systematically review studies with the highest level of evidence evaluating UM risk factors in order to construct a patient risk profile.
Methods :
Two trained reviewers independently screened OVID, Pubmed, Embase and Web of Science from their respective inception dates until April 2015 using a combination of keywords and MeSH terms. Eligible studies were meta-analyses or systematic reviews that provided pooled odds ratios (OR) of potential risk factors for UM development or enough information to calculate them independently. Methodological quality was evaluated using the Assessment of Multiple Systematic Reviews (AMSTAR) tool.
Results :
Four meta-analyses with a mean methodological quality score of 65.9% (min: 54.5%; max: 72.7%) were included. The following significant risk factors were identified: atypical cutaneous nevi (OR 2.82, 95%CI 1.10-7.26), welding (OR 2.05, 95%CI 1.20-3.51), occupational cooking (OR 1.81, 95%CI 1.33-2.46), fair skin color (OR 1.80, 95%CI 1.31-2.47), light eye color (OR 1.75, 95%CI 1.31-2.34), common cutaneous nevi (OR 1.74, 95%CI 1.27-2.39), propensity to sunburn (OR 1.64, 95%CI 1.29-2.09), iris nevi (OR 1.53, 95%CI 1.03-2.27), and cutaneous freckles (OR 1.27, 95%CI 1.09-1.49). Non-significant risk factors included outdoor leisure activity, occupational sunlight exposure, latitude of birth, and hair color.
Conclusions :
Moderate quality of evidence determined nine significant risk factors for developing UM that can be used to estimate a patient’s susceptibility to the disease. Knowledge of these variables and their relative importance may assist in the elaboration of a formal risk assessment tool that would allow ophthalmologists to implement personalized preventive measures based on individual patient risk.
This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.