Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120,389 Type 2 Diabetics over 11 Years Follow-Up.

(1) Background: Although DR screening is effective, one of its most significant problems is a lack of attendance. The aim of the present study was to demonstrate the effectiveness of our algorithm in predicting the development of any type of DR and referable DR. (2) Methods: A retrospective study with an 11-year follow-up of a population of 120,389 T2DM patients was undertaken. (3) Results: Applying the results of the algorithm showed an AUC of 0.93 (95% CI, 0.92-0.94) for any DR and 0.90 (95% CI, 0.89-0.91) for referable DR. Therefore, we achieved a promising level of agreement when applying our algorithm. (4) Conclusions: The algorithm is useful for predicting which patients may develop referable forms of DR and also any type of DR. This would allow a personalized screening plan to be drawn up for each patient.

Effect of Lipids on Diabetic Retinopathy in a Large Cohort of Diabetic Patients after 10 Years of Follow-Up.

(1) Background: Diabetic retinopathy (DR) remains the leading cause of low vision and blindness in young adults of working age. Although the most important risk factors-such as the duration of diabetes mellitus (DM) and glycemic control measured by HbA1c-are known, the effects of lipids are not as clear. The aim of the present study is to analyze the effects of lipids on the development of DR. (2) Methods: This is a retrospective study of a population of 175,645 DM2 patients, during the period 2010 to 2020, in which the effects of different lipid factors are studied. (3) Results: The variables that most influenced the development of DR in our study, based on significance and cumulative hazard (CH), were arterial hypertension (CH 1.217, p < 0.001), HbA1c levels (CH 1.162, p = 0.001), microalbuminuria (CH 1.012, p < 0.001), LDL-C cholesterol (CH 1.007, p = 0.012), TC/HDL-C index (CH 1.092, p < 0.001), No-HDL-C/HDL-C index (CH 1.065, p = 0.002), the use of statins (CH 1.001, p = 0.005), and body mass index (CH 1.007, p < 0.001). (4) Conclusions: LDL-cholesterol, TC/HDL-C, and No-HDL-C/HDL-C indices are related to the development of DR, and there is a protective effect of HDL-cholesterol and the use of fibrates.

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.

Impact of the COVID-19 Pandemic on the Metabolic Control of Diabetic Patients in Diabetic Retinopathy and Its Screening.

(1) Background: Diabetic retinopathy (DR) is a complication of diabetes mellitus (DM), screening programs of which have been affected by the COVID-19 pandemic. The aim of the present study was to determine the impact of the COVID-19 pandemic on the screening of diabetes patients in our healthcare area (HCA). (2) Methods: We carried out a retrospective study of patients with DM who had attended the DR screening program between January 2015 and June 2022. We studied attendance, DM metabolic control and DR incidence. (3) Results: Screening for DR decreased in the first few months of the pandemic. The incidence of mild and moderate DR remained stable throughout the study, and we observed little increase in severe DR, proliferative DR and neovascular glaucoma during 2021 and 2022. (4) Conclusions: The current study shows that during the COVID-19 pandemic, screening program attendance decreased during the year 2020, which then recovered in 2021. Regarding the most severe forms of DR, a slight increase in cases was observed, beginning in the year 2021. Nevertheless, we aimed to improve the telemedicine systems, since the conditions of a significant proportion of the studied patients worsened during the pandemic; these patients are likely those who were already poorly monitored.

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.

Changes in the Epidemiology of Diabetic Retinopathy in Spain: A Systematic Review and Meta-Analysis.

BACKGROUND: The aim of the present study was to determine the prevalence and incidence of diabetic retinopathy (DR) and its changes in the last 20 years in type 2 diabetes mellitus (T2DM) patients in Spain. METHODS: A systematic review with a meta-analysis was carried out on the studies published between 2001-2020 on the prevalence and incidence of DR and sight-threatening diabetic retinopathy (STDR) in Spain. The articles included were selected from four databases and publications of the Spanish Ministry of Health and Regional Health Care System (RHCS). The meta-analysis to determine heterogeneity and bias between studies was carried out with the MetaXL 4.0. RESULTS: Since 2001, we have observed an increase in the detection of patients with DM, and at the same time, screening programs for RD have been launched; thus, we can deduce that the increase in the detection of patients with DM, many of them in the initial phases, far exceeds the increased detection of patients with DR. The prevalence of DR was higher between 2001 and 2008 with values of 28.85%. These values decreased over the following period between 2009 and 2020 with a mean of 15.28%. Similarly the STDR prevalence decrease from 3.67% to 1.92% after 2008. The analysis of the longitudinal studies determined that the annual DR incidence was 3.83%, and the STDR annual incidence was 0.41%. CONCLUSION: In Spain, for T2DM, the current prevalence of DR is 15.28% and 1.92% forSTDR. The annual incidence of DR is 3.83% and is 0.41% for STDR.

Real-world outcomes of a clinical decision support system for diabetic retinopathy in Spain.

Objective: The aim of present study was to evaluate our clinical decision support system (CDSS) for predicting risk of diabetic retinopathy (DR). We selected randomly a real population of patients with type 2 diabetes (T2DM) who were attending our screening programme. Methods and analysis: The sample size was 602 patients with T2DM randomly selected from those who attended the DR screening programme. The algorithm developed uses nine risk factors: current age, sex, body mass index (BMI), duration and treatment of diabetes mellitus (DM), arterial hypertension, Glicated hemoglobine (HbA1c), urine-albumin ratio and glomerular filtration. Results: The mean current age of 67.03±10.91, and 272 were male (53.2%), and DM duration was 10.12±6.4 years, 222 had DR (35.8%). The CDSS was employed for 1 year. The prediction algorithm that the CDSS uses included nine risk factors: current age, sex, BMI, DM duration and treatment, arterial hypertension, HbA1c, urine-albumin ratio and glomerular filtration. The area under the curve (AUC) for predicting the presence of any DR achieved a value of 0.9884, the sensitivity of 98.21%, specificity of 99.21%, positive predictive value of 98.65%, negative predictive value of 98.95%, α error of 0.0079 and ß error of 0.0179. Conclusion: Our CDSS for predicting DR was successful when applied to a real population.

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.

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.

Validation of a Deep Learning Algorithm for Diabetic Retinopathy.

Background:To validate our deep learning algorithm (DLA) to read diabetic retinopathy (DR) retinographies.Introduction:Currently DR detection is made by retinography; due to its increasing diabetes mellitus incidence we need to find systems that help us to screen DR.Materials and Methods:The DLA was built and trained using 88,702 images from EyePACS, 1,748 from Messidor-2, and 19,230 from our own population. For validation a total of 38,339 retinographies from 17,669 patients (obtained from our DR screening databases) were read by a DLA and compared by four senior retina ophthalmologists for detecting any-DR and referable-DR. We determined the values of Cohen's weighted Kappa (CWK) index, sensitivity (S), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV), and errors type I and II.Results:The results of the DLA to detect any-DR were: CWK = 0.886 ± 0.004 (95% confidence interval [CI] 0.879-0.894), S = 0.967%, SP = 0.976%, PPV = 0.836%, and NPV = 0.996%. The error type I = 0.024, and the error type II = 0.004. Likewise, the referable-DR results were: CWK = 0.809 (95% CI 0.798-0.819), S = 0.998, SP = 0.968, PPV = 0.701, NPV = 0.928, error type I = 0.032, and error type II = 0.001.Discussion:Our DLA can be used as a high confidence diagnostic tool to help in DR screening, especially when it might be difficult for ophthalmologists or other professionals to identify. It can identify patients with any-DR and those that should be referred.Conclusions:The DLA can be valid to aid in screening of DR.

A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application.

BACKGROUND: The aim of this study was to build a clinical decision support system (CDSS) in diabetic retinopathy (DR), based on type 2 diabetes mellitus (DM) patients. METHOD: We built a CDSS from a sample of 2,323 patients, divided into a training set of 1,212 patients, and a testing set of 1,111 patients. The CDSS is based on a fuzzy random forest, which is a set of fuzzy decision trees. A fuzzy decision tree is a hierarchical data structure that classifies a patient into several classes to some level, depending on the values that the patient presents in the attributes related to the DR risk factors. Each node of the tree is an attribute, and each branch of the node is related to a possible value of the attribute. The leaves of the tree link the patient to a particular class (DR, no DR). RESULTS: A CDSS was built with 200 trees in the forest and three variables at each node. Accuracy of the CDSS was 80.76%, sensitivity was 80.67%, and specificity was 85.96%. Applied variables were current age, gender, DM duration and treatment, arterial hypertension, body mass index, HbA1c, estimated glomerular filtration rate, and microalbuminuria. DISCUSSION: Some studies concluded that screening every 3 years was cost effective, but did not personalize risk factors. In this study, the random forest test using fuzzy rules permit us to build a personalized CDSS. CONCLUSIONS: We have developed a CDSS that can help in screening diabetic retinopathy programs, despite our results more testing is essential.

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.

Multifocal electroretinography changes at the 1-year follow-up in a cohort of diabetic macular edema patients treated with ranibizumab.

PURPOSE: To determine the changes in the multifocal electroretinogram (mfERG) at 1 year in a clinical series of diabetic macular edema (DME) patients treated with ranibizumab (RNBZ) using a pro re nata protocol. METHODS: We analyzed a clinical series of 35 eyes of 35 patients with DME at baseline and after treating them with RNBZ over 1 year, in order to determine the change in the macular function, which was assessed by means of the response density and the implicit time of the first-order kernel (FOK) P1 wave of the mfERG at the foveola (R1), fovea (R2) and parafovea (R3). These electrophysiological parameters were studied taking into account different independent variables, such as DME type, degree of diabetic retinopathy (DR), level of preservation of both the ellipsoid zone (IS/OS) and the external limiting membrane (ELM) and changes in central retinal thickness (CRT) and total macular volume (TMV). We also studied the relationship between the response density and the best-corrected visual acuity (BCVA). RESULTS: Eyes with cystic and spongiform DME showed better response density with respect to the serous type (p < 0.001) at baseline. Similarly, eyes with high IS/OS and ELM preservation rates showed higher initial response density compared to the others (p < 0.001). Eyes with moderate DR had better response density compared to those with severe and proliferative DR (p = 0.001). At the beginning of the study, those eyes with proliferative and severe DR showed longer implicit times with respect to those with moderate DR (p = 0.04). The response density significantly increased in eyes that anatomically restored the IS/OS and the ELM after being treated with RNBZ (both p < 0.001). Similarly, eyes with spongiform DME further improved the response density with respect to those with cystic and serous DME (p < 0.001). On the contrary, eyes with hard exudates showed less improvement in their response density at the end of the study (p < 0.001). We observed a significant relationship between BCVA and the response density achieved at the end of the study (p = 0.012). Eyes with severe and proliferative DR significantly shortened implicit time compared to those with moderate DR (p = 0.04). CONCLUSIONS: The multifocal electroretinogram allowed us to differentiate groups of eyes with DME according to their electrophysiological profile, both initially and after being treated with RNBZ. Ranibizumab increased the response density in all DME types included in the study, with a maximum response in those eyes with spongiform type. Once treated with RNBZ, the macular electrophysiological activity improved in eyes that had a well-preserved ellipsoid zone and ELM. The presence of hard exudates was a limitation to the response density achieved at the foveola.

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.

Laser treatment for diabetic macular edema in the 21st century.

Diabetic macular edema (DME) is the leading cause of blindness in the diabetic population. The diabetes Control and Complications Trial reported that 27% of patients affected by type 1 diabetes develop DME within 9 years of onset. Other studies have shown that in patients with type 2 diabetes, the prevalence increased from 3% to 28% within 5 years of diagnosis to twenty years after the onset. At the present time, despite the enthusiasm for evaluating several new treatments for DME, including the intravitreal therapies for DME (e.g., corticosteroids, and anti-VEGF drugs), laser photocoagulation remains the current gold standard and the only treatment with proven efficacy in a wide range of clinical trials for this condition. Despite being the standard technique for comparison and evaluation of the emerging treatments, we have generally poor understanding of the ETDRS recommendations, and we often forget about the results of laser in DME. The purpose of this review is to update our knowledge on laser photocoagulation for DME with an extensive review of the ETDRS results and discuss the laser techniques. Furthermore, we will describe the new developments in laser systems and review the current indications and results. Finally, we will discuss the results of laser treatments versus the current pharmacological therapies. We conclude by trying to provide a general overview that which laser treatment must be indicated and what types of lasers are currently recommended.

Relationship between diabetic retinopathy, microalbuminuria and overt nephropathy, and twenty-year incidence follow-up of a sample of type 1 diabetic patients.

PURPOSE: To determine the incidence and relationship of diabetic retinopathy (DR), microalbuminuria and overt nephropathy (ON). METHOD: A 20-year prospective study, in a cohort of 110 consecutive type 1 diabetes mellitus (DM) patients, without diabetic retinopathy or microalbuminuria at enrolment in 1990. RESULTS: The 20-year incidence of any DR was 70.91%, microalbuminuria 42.72%, and ON was 23.63%. Regarding the risk factors: pre pubertal age at diagnosis was significant for DR and ON, LDL-cholesterol and CT/HDL-cholesterol were significant for DR but not for microalbuminuria or ON. The relationship between DR and ON demonstrated that DR was a significant risk factor for ON, but ON was significant for sight-threatening DR. At the end of the study, two major groups of patients were formed: patients with DR only and patients with DR and ON. For the development of only DR we can assume that the most important risk factor is the duration of DM, followed by the high levels of HbA1c, pre-pubertal age at onset, and arterial hypertension; and for the development of ON and DR simultaneously, risk factors are higher levels of HbA1c, arterial hypertension, DM duration and pre-pubertal age at onset. CONCLUSIONS: In the current study, two major groups of patients have been formed, those who developed only DR and those who developed DR and ON. For the former, incidence increased as DM duration increased, and for the latter incidence appeared to be closely related to levels of HbA1c.

Resultados de la implantación de la cámara no midriática sobre la población diabética / Results of non-mydriatic fundus camera screening in a diabetic population

Objetivo: Determinar el impacto de la implantación de sistemas de cribado de retinopatía diabética (RD) mediante cámara no midriática (CNM) en una población con diabetes mellitus (DBT). Métodos: Estudio prospectivo de 6 años de duración, sobre el cribado oportunístico de una población de 12 801 pacientes con DBT. Resultados: Se revisaron 10 047 pacientes con DBT, un 78.48% de los individuos con DBT censados. En 86 (0.86%) pacientes no se pudo interpretar la imagen y debieron ser referidos a las consultas de oftalmología. Un total de 1 908 pacientes (19%) requirió dilatación pupilar. A los 6 años se detectó RD en 1 410 pacientes, con una incidencia anual del 6.15%; la forma leve fue la más frecuente, con un 77.94% de casos. La incidencia de edema macular diabético fue del 4.84% anual. Se verificó la presencia de otras enfermedades en 995 pacientes (9.91%). Conclusiones: Podemos extraer que el cribado mediante CNM es altamente útil para poder acceder a una gran parte de la población diabética, en especial aquella que acude con escasa frecuencia al oftalmólogo, lo que nos permite diagnosticar un número importante de individuos susceptibles de tratamiento láser para evitar que presenten ceguera.

Resultados de la implantación de la cámara no midriática sobre la población diabética / Results of non-mydriatic fundus camera screening in a diabetic population

Objetivo: Determinar el impacto de la implantación de sistemas de cribado de retinopatía diabética (RD) mediante cámara no midriática (CNM) en una población con diabetes mellitus (DBT). Métodos: Estudio prospectivo de 6 años de duración, sobre el cribado oportunístico de una población de 12 801 pacientes con DBT. Resultados: Se revisaron 10 047 pacientes con DBT, un 78.48% de los individuos con DBT censados. En 86 (0.86%) pacientes no se pudo interpretar la imagen y debieron ser referidos a las consultas de oftalmología. Un total de 1 908 pacientes (19%) requirió dilatación pupilar. A los 6 años se detectó RD en 1 410 pacientes, con una incidencia anual del 6.15%; la forma leve fue la más frecuente, con un 77.94% de casos. La incidencia de edema macular diabético fue del 4.84% anual. Se verificó la presencia de otras enfermedades en 995 pacientes (9.91%). Conclusiones: Podemos extraer que el cribado mediante CNM es altamente útil para poder acceder a una gran parte de la población diabética, en especial aquella que acude con escasa frecuencia al oftalmólogo, lo que nos permite diagnosticar un número importante de individuos susceptibles de tratamiento láser para evitar que presenten ceguera. (AU)