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. Author manuscript; available in PMC: 2017 Jun 23.
Published in final edited form as: Ophthalmology. 2015 Dec 19;123(3):625–34.e1. doi: 10.1016/j.ophtha.2015.10.030

Figure 2. Change in area of retinal nonperfusion following monthly injections of ranibizumab between baseline and month 6 versus pro re nata ranibizumab thereafter in subjects with (A) branch retinal vein occlusion (B) central retinal vein occlusion.

Figure 2

Patients with macular edema due to branch retinal vein occlusion (BRVO) or central retinal vein occlusion (CRVO) were treated with monthly injections of ranibizumab between baseline and month 6 (BL-M6) followed by pro re nata (prn) injections of ranibizumab between M6 and month 12 (M6–M12), between M12 and month 24 (M12–M24), and between M24 and month 36 (M24–M36). Change in area of retinal nonperfusion (RNP) was graded as reduced, unchanged or increased between each time point. A greater percentage of BRVO (A) and CRVO (B) patients showed a reduction in area of RNP and a lower percentage showed an increase in area of RNP when receiving monthly injections of ranibizumab (BL-M6) than during subsequent periods when receiving prn ranibizumab. In (B), 0% of patients in the M6–M12, M12–M24, and M24–M36 time points showed reduction in RNP and hence only the white bar (BL-M6) bar appears in the reduced category.

(A) *p=0.008 for difference in percentage of patients with a reduction in area of RNP in BL-M6 period versus each of the other periods; **p=0.009 for difference in percentage of patients with an increase area of RNP in BL-M6 period and each of the other periods using a multinomal logistic regression model with Huber/White/sandwich estimator of variance to account for the correlation among repeated measures from the same patients.

(B) *p<0.0001 for difference in percentage of patients with a reduction in area of RNP in BL-M6 period versus each of the other periods; ** p=0.0006 for difference in percentage of patients with an increase area of RNP in BL-M6 period and each of the other periods using a multinomal logistic regression model with Huber/White/sandwich estimator of variance to account for the correlation among repeated measures from the same patients.