This is a retrospective, international, multicenter, observational cohort study comprising 16 study sites. Institutional review board (IRB) approval was obtained through the individual IRBs at the participating institutes for a retrospective consecutive chart review. This research adhered to the tenets of the Declaration of Helsinki.
Study participants
Medical records of patients from January 1st, 2010, to June 30th, 2017 with a diagnosis of DME were reviewed. The following were set as inclusion criteria, with all criteria being met: (1) age 18 years or older; (2) type 1 or 2 diabetes mellitus; study eye with (3) center-involving DME (DME defined by retinal thickness of > 250 µm in the central subfield thickness (CST)) and intra ± subretinal fluid on spectral-domain optical coherence tomography (SD-OCT). (4) Best corrected visual acuity ≤ 0.1 logMAR (≥ 0.8 decimal acuity, ≥ 20/25 or ≥ 80 EDTRS letters).
Exclusion criteria were (1) concomitant ocular disease that could cause macular edema (including choroidal neovascularization from any cause, retinal vein occlusion, uveitis and recent intraocular surgery); (2) any concomitant ocular or neurological condition that could affect vision except cataract; (3) laser panretinal photocoagulation (PRP) < 6 months prior to baseline; (4) intravitreal therapy < 3 months prior to study inclusion; and (5) intravitreal therapy during follow-up for proliferative diabetic retinopathy (PDR).
Data collection
For eligible patients, the following data were collected from their medical charts: demographic data (i.e., age, sex); duration of diabetes; stage of diabetic retinopathy [non-proliferative (NPDR) or PDR]; previous DME treatments (macular laser, intravitreal anti-VEGF injections, triamcinolone acetonide, DEX implant), previous laser PRP; lens status at baseline and 12 months; VA and CST at baseline, 3, 6, 9 and 12 months; and further treatment during follow-up (including macular laser, intravitreal anti-VEGF injections, triamcinolone acetonide, and DEX implant), laser PRP, and cataract surgery.
Outcome measures
Main outcome measures were the mean change in VA and CST from baseline to month 12. Secondary outcome measures included the mean change in VA and CST from baseline to month 6, the proportion of eyes which maintained vision (VA loss < 5 letters or VA gain), VA loss ≥ 5 letters, ≥ 10 letters, ≥ 15 letters, VA of ≥ 0.2 logMAR (≤ 75 letters, ≤ 20/32 Snellen equivalent) and VA of ≥ 0.3 logMAR (≤ 70 letters, ≤ 20/40 Snellen equivalent) at 12 months.
OCT analysis
All eyes were imaged with SD-OCT (Heidelberg Spectralis, Heidelberg, Germany; Optovue Avanti, Fremont, USA; Topcon 3D OCT-2000, Tokyo; Japan; or Cirrus, Zeiss, Oberkochen, Germany, Canon-OCT HS100, Tokyo, Japan). Quantitative assessment of DME-included CST calculated automatically by the instrument. Additionally, for all study participants the horizontal B-scans encompassing the fovea were exported. These images were graded for any disruption to the ellipsoid zone (EZ) by three independent and masked graders (CB, MI, MR).
Statistical analysis
Variables are expressed as mean ± standard deviation (SD). To control for the correlated nature of our data, we used a generalized estimating equations (GEE) procedure. Differences in VA and CST between baseline and month 6 or month 12 were analyzed by univariable linear regression. Difference in outcome measures between the subgroups were assessed by including the following confounding baseline variables: (1) age, (2) gender, (3) stage of diabetic retinopathy (NPDR vs. PDR), (4) duration of diabetes, (5) EZ disruption at baseline, (6) lens status at baseline and (7) after 12 months, (8) treatment naivety, (9) conduction of PRP during follow-up, and (10) baseline VA (for VA outcomes) and baseline CST (for CST outcomes). Variables with p ≤ 0.15 in the univariable analysis were included in the final GEE model. A backward selection procedure was applied that retained only those variables with p < 0.05. For continuous outcome variables, a linear regression model and for a binary outcome a logistic regression model was applied. Markov chain Monte Carlo multiple imputation procedure with 100 run imputations was used to impute missing data. Statistical analysis was performed with SPSS Statistics 22 (IBM, Armonk, NY, USA).