Prevalence of Diabetic Retinopathy and Blindness in Indonesian Adults With Type 2 Diabetes

Sasongko, Muhammad Bayu; Widyaputri, Felicia; Agni, Angela Nurini; Wardhana, Firman Setya; Kotha, Satyaprabha; Gupta, Prateek; Widayanti, Tri Wahyu; Supanji, Supanji; Widyaningrum, Rifa; Wong, Tien Yin; Kawasaki, Ryo; Wang, Jie Jin

doi:10.1016/j.ajo.2017.06.019

Cited by 73 publications

(62 citation statements)

References 24 publications

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“…Like other studies, smoking was not associated with DR (P-value 0.992) [5,9,11,24,39]. The relationship between dyslipidemia and nephropathy has been proven in this study and other studies in Saudi Arabia and worldwide [8,21,[25][26][27][28]40].…”

Section: Discussionsupporting

confidence: 76%

“…National and international studies that have been conducted to determine the prevalence of diabetic retinopathy have reported values that range from 15% in some studies to 36% in others [19][20][21][22][23]. Studies from Saudi Arabia's southern region reported a prevalence of 36.4%, which is much higher than the previously reported prevalence of Health Prim Car, 2018 doi: 10.15761/HPC.1000134 11.3% in the same region by Al-Khaldi, et al [24].…”

Section: Discussionmentioning

confidence: 89%

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The Impact of Expanding the Screening Interval for Diabetic Retinopathy in Primary Care Centers

Bardisi¹,

Magliah²,

Alatta³

2018

Health Prim Car

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Objectives: This study aimed to estimate the prevalence of diabetic retinopathy (DR) and its determinant risk factors in people with diabetes attending primary care centers and to determine the impact of expanding the screening interval on the incidence and progression of (DR). Methodology:This study was a cross-sectional chart review, that was conducted in three randomly selected primary care centers. A total of 250 diabetic patients had three consecutive annual screenings for DR from April 2014 to April 2017. At the initial visit, the ophthalmological findings were recorded. For three successive yearly screening , the screening results were assessed to estimate the changes that occurred in the prevalence, incidence, and progression of diabetic retinopathy in addition to the degree of association with the most predictable risk factors.Results:The initial prevalence of DR was 15.2%. Over the three-consecutive annual screening, the DR prevalence increased to 19.6%, 22.4%, and 25.6%, respectively. Most cases (72%) of DR were of the mild non-proliferative diabetic retinopathy (NPDR) type. The annual incidence of DR was 4.4%, 2.8%, and 3.2%. The progression of DR occurred in 2 (0.08%) cases at the second screening interval and in 6 (2.4%) cases at the third screening interval. These changes were strongly associated with age, the duration of diabetes, HBA1C levels, hypertension, and insulin therapy (P-value 0.005). However, changes in the incidence and progression of DR over the three intervals were statistically non-significant (P-value 0.086).Conclusion: DR, a serious microvascular complication of diabetes mellitus, is an asymptomatic disease with a slow onset and gradual progression. DR requires regular screening for early detection. The screening interval can be extended up to every three years in the very low-risk groups without threatening vision.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 89%

The Impact of Expanding the Screening Interval for Diabetic Retinopathy in Primary Care Centers

Bardisi¹,

Magliah²,

Alatta³

2018

Health Prim Car

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“…This is largely because of the inadequate availability of resources, including trained eye care personnel and financing, to cope with the rapidly growing burden of diabetes. This is particularly important in major developing countries such as China and Indonesia (11)(12)(13).…”

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confidence: 99%

An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs

Keel

Liu³

et al. 2018

Diabetes Care

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202

139

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OBJECTIVEThe goal of this study was to describe the development and validation of an artificial intelligence-based, deep learning algorithm (DLA) for the detection of referable diabetic retinopathy (DR). RESEARCH DESIGN AND METHODSA DLA using a convolutional neural network was developed for automated detection of vision-threatening referable DR (preproliferative DR or worse, diabetic macular edema, or both). The DLA was tested by using a set of 106,244 nonstereoscopic retinal images. A panel of ophthalmologists graded DR severity in retinal photographs included in the development and internal validation data sets (n = 71,043); a reference standard grading was assigned once three graders achieved consistent grading outcomes. For external validation, we tested our DLA using 35,201 images of 14,520 eyes (904 eyes with any DR; 401 eyes with vision-threatening referable DR) from population-based cohorts of Malays, Caucasian Australians, and Indigenous Australians. RESULTSAmong the 71,043 retinal images in the training and validation data sets, 12,329 showed vision-threatening referable DR. In the internal validation data set, the area under the curve (AUC), sensitivity, and specificity of the DLA for vision-threatening referable DR were 0.989, 97.0%, and 91.4%, respectively. Testing against the independent, multiethnic data set achieved an AUC, sensitivity, and specificity of 0.955, 92.5%, and 98.5%, respectively. Among false-positive cases, 85.6% were due to a misclassification of mild or moderate DR. Undetected intraretinal microvascular abnormalities accounted for 77.3% of all false-negative cases. CONCLUSIONSThis artificial intelligence-based DLA can be used with high accuracy in the detection of vision-threatening referable DR in retinal images. This technology offers potential to increase the efficiency and accessibility of DR screening programs.

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“…Detailed study sampling and methodology have previously been described (23)(24)(25). Briefly, JOGED.COM (Jogjakarta Eye Diabetic Study in the Community) was a community-based cross-sectional study recruiting participants aged .30 years with type 2 diabetes confirmed by their family physician (using one of the following criteria: fasting plasma glucose $126 mg/dL, random glucose $200 mg/dL, or A1C $6.5%).…”

Section: Methodsmentioning

confidence: 99%

“…Assessment of DR was performed by a trained grader and adjudicated by a retinal specialist masked to the patient's clinical status. We categorized DR following the modified Airlie House classification as mild or moderate nonproliferative DR (NPDR) and vision-threatening DR (VTDR) (24). Macular edema was categorized as presence or absence of clinically significant macular edema (CSME), depending on the appearance of retinal hard exudates within the central fovea area.…”

Section: Fundus Photography and Assessment Of Drmentioning

confidence: 99%

Estimated Resting Metabolic Rate and Body Composition Measures Are Strongly Associated With Diabetic Retinopathy in Indonesian Adults With Type 2 Diabetes

et al. 2018

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Prevalence of Diabetic Retinopathy and Blindness in Indonesian Adults With Type 2 Diabetes

Cited by 73 publications

References 24 publications

The Impact of Expanding the Screening Interval for Diabetic Retinopathy in Primary Care Centers

The Impact of Expanding the Screening Interval for Diabetic Retinopathy in Primary Care Centers

An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs

Estimated Resting Metabolic Rate and Body Composition Measures Are Strongly Associated With Diabetic Retinopathy in Indonesian Adults With Type 2 Diabetes

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