Risk Factors for Recurrent Vitreous Hemorrhage in Type 2 Diabetes Mellitus Patients after Posterior Vitrectomy.

(Background) The aim of this study was to determine the factors related to recurrent vitreous hemorrhage (RVH) in a sample of proliferative diabetic retinopathy (PDR) patients. (Methods) This was a retrospective, review-based study. We studied 183 eyes from 121 type 2 diabetes patients with PDR. We recorded the duration of diabetes, history of hypertension, retinal photocoagulation status, posterior vitreous status, mean HbA1c and hemoglobin levels, renal function, and systemic complications associated with diabetes. We also recorded surgical variables-the presence of tractional retinal detachment, the application of segmentation and diathermy on fibrovascular proliferative tissue, and the use of silicone oil-to study which independent variables were significantly related to the presence of RVH. (Results) The duration of diabetes (p = 0.028), hemoglobin level (p = 0.02), status of the posterior vitreous (p = 0.03), retinal photocoagulation status (p = 0.002), and the presence of tractional retinal detachment (p = 0.03) were significantly associated with the presence of RVH. On the other hand, the use of diathermy was associated with fewer RVH events (p < 0.005). In addition, patients with diabetic polyneuropathy, myocardial infarction, and ischemia in the lower limbs exhibited more vitreous hemorrhage events (p < 0.001). (Conclusions) Patients with PDR and a longer diabetes duration, anemia, attached posterior vitreous, deficient retinal photocoagulation, and prior cardiovascular events were more prone to RVH.

Differences in Multifocal Electroretinogram Study in Two Populations of Type 1 and Type 2 Diabetes Mellitus Patients without Diabetic Retinopathy.

(1) Background: Diabetic retinopathy (DR) is a diabetes mellitus (DM) complication where neurodegeneration plays a significant role. The aim of our study was to determine the differences between type 1 DM (T1DM) and 2 DM (T2DM) in the multifocal electroretinogram (mERG).; (2) Methods: A mERG study was performed in two groups, a T1DM group with 72 eyes of 36 patients compared with 72 eyes of 36 patients with T2DM, randomly selected from our DM databases, without DR. We studied how HbA1c and DM duration affects amplitude and implicit time of mERG; (3) Results: the study of DM duration shows patients with T1DM have lower amplitude values compared to T2DM patients, although implicit time increases in patients with T2DM. HbA1c over 7% only affects T1DM patients with an increase of implicit time; (4) Conclusions: the retinas of patients with T1DM seem more sensitive to changes in HbA1c levels than in patients with DMT2, although the duration of diabetes affects both types of DM patients.

Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population.

Aim: The aim of the present study was to build a clinical decision support system (CDSS) that can predict the presence of diabetic retinopathy (DR) in type 1 diabetes (T1DM) patients. Material and Method: We built two versions of our CDSS to predict the presence of any-type DR and sight-threatening DR (STDR) in T1DM patients. The first version was trained using 324 T1DM and 826 T2DM patients. The second was trained with only the 324 T1DM patients. Results: The first version achieved an accuracy (ACC) = 0.795, specificity (SP) = 83%, and sensitivity (S) = 65.7% to predict the presence of any-DR, and an ACC = 0.918, SP = 87.1% and S = 87.8% for STDR. The second model achieved ACC = 0.799, SP = 87.5% and S = 86.3% when predicting any-DR and ACC = 0.937, SP = 90.9% and S = 83.0% for STDR. Conclusion: The two models better predict STDR than any-DR in T1DM patients. We will need a larger sample to strengthen our results.

Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database.

BACKGROUND: The aim of the present study was to test our deep learning algorithm (DLA) by reading the retinographies. METHODS: We tested our DLA built on convolutional neural networks in 14,186 retinographies from our population and 1200 images extracted from MESSIDOR. The retinal images were graded both by the DLA and independently by four retina specialists. Results of the DLA were compared according to accuracy (ACC), sensitivity (S), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC), distinguishing between identification of any type of DR (any DR) and referable DR (RDR). RESULTS: The results of testing the DLA for identifying any DR in our population were: ACC = 99.75, S = 97.92, SP = 99.91, PPV = 98.92, NPV = 99.82, and AUC = 0.983. When detecting RDR, the results were: ACC = 99.66, S = 96.7, SP = 99.92, PPV = 99.07, NPV = 99.71, and AUC = 0.988. The results of testing the DLA for identifying any DR with MESSIDOR were: ACC = 94.79, S = 97.32, SP = 94.57, PPV = 60.93, NPV = 99.75, and AUC = 0.959. When detecting RDR, the results were: ACC = 98.78, S = 94.64, SP = 99.14, PPV = 90.54, NPV = 99.53, and AUC = 0.968. CONCLUSIONS: Our DLA performed well, both in detecting any DR and in classifying those eyes with RDR in a sample of retinographies of type 2 DM patients in our population and the MESSIDOR database.

The Effect of HbA1c Variability as a Risk Measure for Microangiopathy in Type 1 Diabetes Mellitus.

BACKGROUND: To measure the relationship between variability in HbA1c and microalbuminuria (MA) and diabetic retinopathy (DR) in the long term. METHODS: A prospective case-series study, was conducted on 366 Type 1 Diabetes Mellitus patients with normoalbuminuria and without diabetic retinopathy at inclusion. The cohort was followed for a period of 12 years. The Cox survival analysis was used for the multivariate statistical study. The effect of variability in microangiopathy (retinopathy and nephropathy) was evaluated by calculating the standard deviation of HbA1c (SD-HbA1c), the coefficient of variation of HbA1c (CV-HbA1c), average real variability (ARV-HbA1c) and variability irrespective of the mean (VIM-HbA1c) adjusted for the other known variables. RESULTS: A total of 106 patients developed diabetic retinopathy (29%) and 73 microalbuminuria (19.9%). Overt diabetic nephropathy, by our definition, affected only five patients (1.36%). Statistical results show that the current age, mean HbA1c, SD-HbA1c and ARV-HbA1c are significant in the development of diabetic retinopathy. Microalbuminuria was significant for current age, mean HbA1c, CV-HbA1c and ARV-HbA1c. CONCLUSIONS: By measuring the variability in HbA1c, we can use SD-HbA1c and ARV-HbA1c as possible targets for judging which patients are at risk of developing DR and MA, and CV-HbA1c as the target for severe DR.

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

AIMS: To determine the relationship between diabetic nephropathy and diabetic retinopathy on a population of type 2 diabetes mellitus patients. METHODS: 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. RESULTS: 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 m2 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 m2 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 m2 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 m2 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). CONCLUSIONS: The UACR has a better association with diabetic retinopathy than the eGFR, although both are important risk factors for diabetic retinopathy.

Differences in incidence of diabetic retinopathy between type 1 and 2 diabetes mellitus: a nine-year follow-up study.

BACKGROUND/AIMS: To determine the incidence of any diabetic retinopathy (any-DR), sight-threatening diabetic retinopathy (STDR) and diabetic macular oedema (DMO) and their risk factors in type 1 diabetes mellitus (T1DM) over a screening programme. METHODS: Nine-year follow-up, prospective population-based study of 366 patients with T1DM and 15 030 with T2DM. Epidemiological risk factors were as follows: current age, age at DM diagnosis, sex, type of DM, duration of DM, arterial hypertension, levels of glycosylated haemoglobin (HbA1c), triglycerides, cholesterol fractions, serum creatinine, estimated glomerular filtration rate (eGFR) and urine albumin to creatinine ratio (UACR). RESULTS: Sum incidence of any-DR was 47.26% with annual incidence 15.16±2.19% in T1DM, and 26.49% with annual incidence 8.13% in T2DM. Sum incidence of STDR was 18.03% with annual incidence 5.77±1.21% in T1DM, and 7.59% with annual incidence 2.64±0.15% in T2DM. Sum incidence of DMO was 8.46% with annual incidence 2.68±038% in patients with T1DM and 6.36% with annual incidence 2.19±0.18% in T2DM. Cox's survival analysis showed that current age and age at diagnosis were risk factors at p<0.001, as high HbA1c levels at p<0.001, LDL cholesterol was significant at p<0.001, eGFR was significant at p<0.001 and UACR at p=0.017. CONCLUSIONS: The incidence of any-DR and STDR was higher in patients with T1DM than those with T2DM. Also, the 47.26% sum incidence of any-DR in patients with T1DM was higher than in a previous study (35.9%), which can be linked to poor metabolic control of DM. Our results suggest that physicians should be encouraged to pay greater attention to treatment protocols for T1DM in patients.

Diabetic Macular Edema Pathophysiology: Vasogenic versus Inflammatory.

Diabetic macular edema (DME) can cause blindness in diabetic patients suffering from diabetic retinopathy (DR). DM parameters controls (glycemia, arterial tension, and lipids) are the gold standard for preventing DR and DME. Although the vascular endothelial growth factor (VEGF) is known to play a role in the development of DME, the pathological processes leading to the onset of this disease are highly complex and the exact sequence in which they occur is still not completely understood. Angiogenesis and inflammation have been shown to be involved in the pathogenesis of this disease. However, it still remains to be clarified whether angiogenesis following VEGF overexpression is a cause or a consequence of inflammation. This paper provides a review of the data currently available, focusing on VEGF, angiogenesis, and inflammation. Our analysis suggests that angiogenesis and inflammation act interdependently during the development of DME. Knowledge of DME etiology seems to be important in treatments with anti-VEGF or anti-inflammatory drugs. Current diagnostic techniques do not permit us to differentiate between both etiologies. In the future, diagnosing the physiopathology of each patient with DME will help us to select the most effective drug.