Objectives: To detect factors capable of predicting the development of macular edema (ME) throughout the disease course in patients affected by non-infectious uveitis (NIU). Methods: Predictive factors leading to the development of ME were analyzed through regression analysis. The functional impact of ME on best corrected visual acuity (BCVA) was also examined. Results: A total of 1,160 NIU patients (1,857 eyes) were analyzed. ME was observed in 148 (12.76%), affecting 211 eyes. It was significantly more frequent in patients with non-anterior NIU (p < 0.0001, RR = 4.01), retinal vasculitis (p < 0.0001), and other structural complications (p = 0.0005). Gender, HLA-B*27 and/or HLA-B*51 positivity, and ethnicity did not show any significant impact on the prevalence of ME (p = 0.635, p = 0.372, p = 0.193, respectively). Four variables were associated with ME development during NIU course: the non-anterior anatomical pattern (p < 0.0001, OR = 4.01), the presence of retinal vasculitis (p = 0.028, OR = 1.68), complications other than ME (p = 0.044, OR = 1.51) and immunosuppressive treatment (p = 0.010, OR 1.69) while the diagnosis of Beh & ccedil;et disease-related uveitis was less likely to be associated with ME development (p = 0.24, OR 0.545). Mean +/- SD BCVA was significantly lower in eyes with ME (0.82 +/- 0.30) compared to eyes without ME (0.71 +/- 0.33). Conclusion: ME can develop across all NIU types, but is more likely in cases involving the posterior segment and retinal vasculitis. Regular and focused monitoring is recommended for these high-risk patients. The study also highlights the limited predictive value of demographic and HLA-related factors, helping refine clinical risk stratification and predictive modeling in NIU.
Sota, J., Mejía-Salgado, G., Guerriero, S., Ragab, G., Costi, S., Paroli, M.P., et al. (2025). Predictors of uveitic macular edema and functional prognostic outcomes: real-life data from the international AIDA Network uveitis registry. FRONTIERS IN MEDICINE, 12 [10.3389/fmed.2025.1609613].
Predictors of uveitic macular edema and functional prognostic outcomes: real-life data from the international AIDA Network uveitis registry
Sota, Jurgen;Gaggiano, Carla;Gentileschi, Stefano;Caggiano, Valeria;Sbalchiero, Jessica;Balistreri, Alberto;Frediani, Bruno;Cantarini, Luca;Fabiani, Claudia
2025-01-01
Abstract
Objectives: To detect factors capable of predicting the development of macular edema (ME) throughout the disease course in patients affected by non-infectious uveitis (NIU). Methods: Predictive factors leading to the development of ME were analyzed through regression analysis. The functional impact of ME on best corrected visual acuity (BCVA) was also examined. Results: A total of 1,160 NIU patients (1,857 eyes) were analyzed. ME was observed in 148 (12.76%), affecting 211 eyes. It was significantly more frequent in patients with non-anterior NIU (p < 0.0001, RR = 4.01), retinal vasculitis (p < 0.0001), and other structural complications (p = 0.0005). Gender, HLA-B*27 and/or HLA-B*51 positivity, and ethnicity did not show any significant impact on the prevalence of ME (p = 0.635, p = 0.372, p = 0.193, respectively). Four variables were associated with ME development during NIU course: the non-anterior anatomical pattern (p < 0.0001, OR = 4.01), the presence of retinal vasculitis (p = 0.028, OR = 1.68), complications other than ME (p = 0.044, OR = 1.51) and immunosuppressive treatment (p = 0.010, OR 1.69) while the diagnosis of Beh & ccedil;et disease-related uveitis was less likely to be associated with ME development (p = 0.24, OR 0.545). Mean +/- SD BCVA was significantly lower in eyes with ME (0.82 +/- 0.30) compared to eyes without ME (0.71 +/- 0.33). Conclusion: ME can develop across all NIU types, but is more likely in cases involving the posterior segment and retinal vasculitis. Regular and focused monitoring is recommended for these high-risk patients. The study also highlights the limited predictive value of demographic and HLA-related factors, helping refine clinical risk stratification and predictive modeling in NIU.| File | Dimensione | Formato | |
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https://hdl.handle.net/11365/1299519
