Topical azithromycin or ofloxacin for endophthalmitis prophylaxis after intravitreal injection.

Background: The number of patients who have undergone intravitreal injections has increased enormously in recent years, but a consensus is still lacking on prophylaxis for endophthalmitis. The aim of this prospective, observational study was to evaluate the prophylactic effect of azithromycin eye drops versus ofloxacin eye drops. Methods: The study was conducted in five hospitals in Spain and included all patients under going intravitreal injections of triamcinolone, bevacizumab, ranibizumab, or pegaptanib over one year. Patients received azithromycin 15 mg/g eye drops (twice daily on the day prior to injection and for another 2 days) or ofloxacin 3 mg/g eye drops (every 6 hours on the day prior to injection and for another 7 days). Results: In the azithromycin group, there were 4045 injections in 972 eyes of 701 patients. In the ofloxacin group, there were 4151 injections in 944 eyes of 682 patients. There were two cases of endophthalmitis (0.049%) in the azithromycin group and five (0.12%) in the ofloxacin group. The odds ratio of presenting with endophthalmitis in the ofloxacin group compared with the azithromycin group was 2.37 (95% confidence interval [CI] 1.32-3.72, P ,0.001). There were two cases of noninfectious uveitis after triamcinolone injection in the azithromycin group (0.049%) and two (0.048%) in the ofloxacin group; no significant differences were observed (odds ratio 0.902, 95% CI 0.622-1.407, P= 0.407). Conjunctival hyperemia was observed in 12 cases in the azithromycin group and none in the ofloxacin group. Conclusion: The risk of endophthalmitis was significantly greater with ofloxacin than with azithromycin. These findings provide a valuable addition to the ever-increasing pool of infor - mation on endophthalmitis prophylaxis after intravitreal injection, although further large-scale studies are required to provide definitive conclusions

Convexity shape constraints for retinal blood vessel segmentation and foveal avascular zone detection

Diabetic retinopathy (DR) has become a major worldwide health problem due to the increase in blindness among diabetics at early ages. The detection of DR pathologies such as microaneurysms, hemorrhages and exudates through advanced computational techniques is of utmost importance in patient health care. New computer vision techniques are needed to improve upon traditional screening of color fundus images. The segmentation of the entire anatomical structure of the retina is a crucial phase in detecting these pathologies. This work proposes a novel framework for fast and fully automatic blood vessel segmentation and fovea detection. The preprocessing method involved both contrast limited adaptive histogram equalization and the brightness preserving dynamic fuzzy histogram equalization algorithms to enhance image contrast and eliminate noise artifacts. Afterwards, the color spaces and their intrinsic components were examined to identify the most suitable color model to reveal the foreground pixels against the entire background. Several samples were then collected and used by the renowned convexity shape prior segmentation algorithm. The proposed methodology achieved an average vasculature segmentation accuracy exceeding 96%, 95%, 98% and 94% for the DRIVE, STARE, HRF and Messidor publicly available datasets, respectively. An additional validation step reached an average accuracy of 94.30% using an in-house dataset provided by the Hospital Sant Joan of Reus (Spain). Moreover, an outstanding detection accuracy of over 98% was achieved for the foveal avascular zone. An extensive state-of-the-art comparison was also conducted. The proposed approach can thus be integrated into daily clinical practice to assist medical experts in the diagnosis of DR

A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images

Retinal disorders are a severe health threat for older adults because they may lead to vision loss and blindness. Diabetic patients are particularly prone to suffer from Diabetic Retinopathy. Identifying relevant structural components in color fundus images like the optic disc (OD) is crucial to diagnose retinal diseases. Automatic OD detection is complex because of its location in an area where blood vessels converge, and color distribution is uneven. Several image processing techniques have been developed for OD detection so far, but vessel segmentation is sometimes required, increasing computational complexity and time. Moreover, precise OD segmentation methods utilize complex algorithms that need special hardware or extensive labeled datasets. We propose an OD detection approach based on the Modern Portfolio Theory of Markowitz to generate an innovative color fusion model. Specifically, the training phase calculates the optimal weights for each color channel. A fusion of weighted color channels is then applied in the testing phase. This approach acts as a powerful and real-time preprocessing stage. We use four heterogeneous datasets to validate the presented methodology. Three out of four datasets are publicly available (i.e., DRIVE, Messidor, and HRF), and the last corresponds to an in–house dataset acquired from Hospital Universitari Sant Joan de Reus (Spain). Two different segmentation methods are presented and compared with state-of-the-art computer vision techniques to analyze the model performance. An outstanding accuracy and overlap above 0.9 and 80%, respectively, and a minimal execution time of 0.05 s are reached. Therefore, our model could be integrated into daily clinical practice to accelerate the diagnosis of Diabetic Retinopathy due to its simplicity, performance, and speed

Validation of a diagnostic support system for diabetic retinopathy based on clinical parameters

Purpose: To validate a clinical decision support system (CDSS) that estimates risk of diabetic retinopathy (DR) and to personalize screening protocols in type 2 diabetes mellitus (T2DM) patients. Methods: We utilized a CDSS based on a fuzzy random forest, integrated by fuzzy decision trees with the following variables: current age, sex, arterial hypertension, diabetes duration and treatment, HbA1c, glomerular filtration rate, microalbuminuria, and body mass index. Validation was made using the electronic health records of a sample of 101,802 T2DM patients. Diagnosis was made by retinal photographs, according to EURODIAB guidelines and the International Diabetic Retinopathy Classification. Results: The prevalence of DR was 19,759 patients (19.98%). Results yielded 16,593 (16.31%) true positives, 72,617 (71.33%) true negatives, 3165 (3.1%) false positives, and 9427 (9.26%) false negatives, with an accuracy of 0.876 (95% confidence interval [CI], 0.858–0.886), sensitivity of 84% (95% CI, 83.46–84.49), specificity of 88.5% (95% CI, 88.29–88.72), positive predictive value of 63.8% (95% CI, 63.18–64.35), negative predictive value of 95.8% (95% CI, 95.68–95.96), positive likelihood ratio of 7.30, and negative likelihood ratio of 0.18. The type 1 error was 0.115, and the type 2 error was 0.16. Conclusions: We confirmed a good prediction rate for DR from a representative sample of T2DM in our population. Furthermore, the CDSS was able to offer an individualized screening protocol for each patient according to the calculated risk confidence value. Translational Relevance: Results from this study will help to establish a novel strategy for personalizing screening for DR according to patient risk factors.The authors thank Phil Hoddy for his language assistance and for editing and correcting the English text. Supported by Instituto de Investigaciones Carlos III (research projects PI18/00169, PI12/01535, and PI15/01150) and by the European Regional Development Fund (FEDER).The authors thank Phil Hoddy for his language assistance and for editing and correcting the English text. Supported by Instituto de Investigaciones Carlos III (research projects PI18/00169, PI12/01535, and PI15/01150) and by the European Regional Development Fund (FEDER)

Diabetic retinopathy as a predictor of cardiovascular morbidity and mortality in subjects with type 2 diabetes

This study aimed to evaluate the predictive value of diabetic retinopathy (DR) and its stages with the incidence of major cardiovascular events and all-cause mortality in type 2 diabetes mellitus (T2DM) persons in our large primary healthcare database from Catalonia (Spain). A retrospective cohort study with pseudo-anonymized routinely collected health data from SIDIAP was conducted from 2008 to 2016. We calculated incidence rates of major cardiovascular events [coronary heart disease (CHD), stroke, or both-macrovascular events] and all-cause mortality for subjects with and without DR and for different stages of DR. The proportional hazards regression analysis was done to assess the probability of occurrence between DR and the study events. About 22,402 T2DM subjects with DR were identified in the database and 196,983 subjects without DR. During the follow-up period among the subjects with DR, we observed the highest incidence of all-cause mortally. In the second place were the macrovascular events among the subjects with DR. In the multivariable analysis, fully adjusted for DR, sex, age, body mass index (BMI), tobacco, duration of T2DM, an antiplatelet or antihypertensive drug, and HbA1c, we observed that subjects with any stage of DR had higher risks for all of the study events, except for stroke. We observed the highest probability of all-cause death events (adjusted hazard ratios, AHRs: 1.34, 95% CI: 1.28; 1.41). In conclusion, our results show that DR is related to CHD, macrovascular events, and all-cause mortality among persons with T2DM

Glomerular Filtration Rate and/or Ratio of Urine Albumin to Creatinine as Markers for Diabetic Retinopathy : A Ten-Year Follow-Up Study

To determine the relationship between diabetic nephropathy and diabetic retinopathy on a population of type 2 diabetes mellitus patients. A prospective ten-year follow-up population-based study. We determined differences between estimated glomerular filtration rate (eGFR) using the chronic kidney disease epidemiology collaboration equation and urine albumin to creatinine ratio. Annual incidence of any-DR was 8.21 ± 0.60% (7.06%-8.92%), sight-threatening diabetic retinopathy (STDR) was 2.65 ± 0.14% (2.48%-2.88%), and diabetic macular edema (DME) was 2.21 ± 0.18% (2%-2.49%). Renal study results were as follows: UACR > 30 mg/g had an annual incidence of 7.02 ± 0.05% (6.97%-7.09%), eGFR < 60 ml/min/1.73 m 2 incidence was 5.89 ± 0.12% (5.70%-6.13%). Cox's proportional regression analysis of DR incidence shows that renal function studied by eGFR < 60 ml/min/1.73 m 2 was less significant (p = 0.04, HR 1.223, 1.098-1.201) than UACR ≥ 300 mg/g (p < 0.001, HR 1.485, 1.103-1.548). The study of STDR shows that eGFR < 60 ml/min/1.73 m 2 was significant (p = 0.02, HR 1.890, 1.267-2.820), UACR ≥ 300 mg/g (p < 0.001, HR 2.448, 1.595-3.757), and DME shows that eGFR < 60 ml/min/1.73 m 2 was significant (p = 0.02, HR 1.920, 1.287-2.864) and UACR ≥ 300 mg/g (p < 0.001, HR 2.432, 1.584-3.732). The UACR has a better association with diabetic retinopathy than the eGFR, although both are important risk factors for diabetic retinopathy