RETINAL LAYER RESPONSE TO RANIBIZUMAB DURING TREATMENT OF DIABETIC MACULAR EDEMA: Thinner is Not Always Better.

PURPOSE To identify individual retinal layer thickness changes associated with visual acuity gain in diabetic macular edema treated with ranibizumab using layer segmentation on high-resolution optical coherence tomography scans. METHODS Retrospective observational case series. Thirty-three treatment-naive eyes with diabetic macular edema were imaged by spectral domain optical coherence tomography at monthly visits while receiving intravitreal ranibizumab treatment as needed, guided by visual acuity. Thickness changes of individual layers after 1 year were quantitatively analyzed and correlated with visual acuity gain. RESULTS The mean best-corrected visual acuity improvement at 1 year was 6.2 (SEM ± 1.5) Early Treatment Diabetic Retinopathy Study letters, and central retinal thickness decreased by 66 ± 18 μm. In the central subfield, there was a significant decrease of thickness for all layers (P < 0.05) except the outer nuclear layer. Multiple linear regression analysis revealed that thickness decrease of the inner retina was associated with better visual acuity, whereas for the outer retina the opposite was true. The best estimate of final visual acuity (R = 0.817, P < 0.001) was obtained, by including baseline visual acuity and thickness change of the inner and outer plexiform layers in the model. CONCLUSION Whereas thickness decrease of the inner retina was positively associated with visual acuity gain, the opposite was found for the outer retina. This might be indirect evidence for recovery of the outer retina during ranibizumab treatment.This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially.

Influence of previous surgery on patient-rated outcome after surgery for degenerative disorders of the lumbar spine.

PURPOSE Few studies have used multivariate models to quantify the effect of multiple previous spine surgeries on patient-oriented outcome after spine surgery. This study sought to quantify the effect of prior spine surgery on 12-month postoperative outcomes in patients undergoing surgery for different degenerative disorders of the lumbar spine. METHODS The study included 4940 patients with lumbar degenerative disease documented in the Spine Tango Registry of EUROSPINE, the Spine Society of Europe, from 2004 to 2015. Preoperatively and 12 months postoperatively, patients completed the multidimensional Core Outcome Measures Index (COMI; 0-10 scale). Patients' medical history and surgical details were recorded using the Spine Tango Surgery 2006 and 2011 forms. Multiple linear regression models were used to investigate the relationship between the number of previous surgeries and the 12-month postoperative COMI score, controlling for the baseline COMI score and other potential confounders. RESULTS In the adjusted model including all cases, the 12-month COMI score showed a 0.37-point worse value [95 % confidence intervals (95 % CI) 0.29-0.45; p < 0.001] for each additional prior spine surgery. In the subgroup of patients with lumbar disc herniation, the corresponding effect was 0.52 points (95 % CI 0.27-0.77; p < 0.001) and in lumbar degenerative spondylolisthesis, 0.40 points (95 % CI 0.17-0.64; p = 0.001). CONCLUSIONS We were able to demonstrate a clear "dose-response" effect for previous surgery: the greater the number of prior spine surgeries, the systematically worse the outcome at 12 months' follow-up. The results of this study can be used when considering or consenting a patient for further surgery, to better inform the patient of the likely outcome and to set realistic expectations.

WGTTEST: Stata module to test the impact of sampling weights in regression analysis

wgttest performs a test proposed by DuMouchel and Duncan (1983) to evaluate whether the weighted and unweighted estimates of a regression model are significantly different.