Lipid-lowering medication is associated with decreased risk of diabetic retinopathy and the need for treatment in patients with type 2 diabetes: A real-world observational analysis of a health claims database

2018 ◽  
Vol 20 (10) ◽  
pp. 2351-2360 ◽  
Author(s):  
Ryo Kawasaki ◽  
Tsuneo Konta ◽  
Kohji Nishida
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Real World ◽  
Health Claims ◽  
Lipid Lowering ◽  
Claims Database ◽  
Observational Analysis ◽  
Need For Treatment

scholarly journals Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera

2021 ◽  
pp. 193229682098556
Author(s):  
Fernando Korn Malerbi ◽  
Rafael Ernane Andrade ◽  
Paulo Henrique Morales ◽  
José Augusto Stuchi ◽  
Diego Lencione ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Diagnostic Accuracy ◽  
Real World ◽  
False Negative ◽  
Portable Devices ◽  
High Burden ◽  
Screening Programs

Background: Portable retinal cameras and deep learning (DL) algorithms are novel tools adopted by diabetic retinopathy (DR) screening programs. Our objective is to evaluate the diagnostic accuracy of a DL algorithm and the performance of portable handheld retinal cameras in the detection of DR in a large and heterogenous type 2 diabetes population in a real-world, high burden setting. Method: Participants underwent fundus photographs of both eyes with a portable retinal camera (Phelcom Eyer). Classification of DR was performed by human reading and a DL algorithm (PhelcomNet), consisting of a convolutional neural network trained on a dataset of fundus images captured exclusively with the portable device; both methods were compared. We calculated the area under the curve (AUC), sensitivity, and specificity for more than mild DR. Results: A total of 824 individuals with type 2 diabetes were enrolled at Itabuna Diabetes Campaign, a subset of 679 (82.4%) of whom could be fully assessed. The algorithm sensitivity/specificity was 97.8 % (95% CI 96.7-98.9)/61.4 % (95% CI 57.7-65.1); AUC was 0·89. All false negative cases were classified as moderate non-proliferative diabetic retinopathy (NPDR) by human grading. Conclusions: The DL algorithm reached a good diagnostic accuracy for more than mild DR in a real-world, high burden setting. The performance of the handheld portable retinal camera was adequate, with over 80% of individuals presenting with images of sufficient quality. Portable devices and artificial intelligence tools may increase coverage of DR screening programs.

Relationship between albuminuric CKD and diabetic retinopathy in a real-world setting of type 2 diabetes: Findings from No blind study

2019 ◽  
Vol 29 (9) ◽  
pp. 923-930 ◽  
Author(s):  
F.C. Sasso ◽  
P.C. Pafundi ◽  
A. Gelso ◽  
V. Bono ◽  
C. Costagliola ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Real World ◽  
Blind Study ◽  
Real World Setting

scholarly journals Association between spousal diabetes status and diabetic retinopathy in Chinese patients with type 2 diabetes

2019 ◽  
Vol 16 (5) ◽  
pp. 474-477
Author(s):  
Mei Yang ◽  
Yu Liu ◽  
Cuihong Wen ◽  
Beirui Wu ◽  
Xu Wan ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Chinese Patients ◽  
Lipid Lowering ◽  
Fundus Photography ◽  
Cross Sectional ◽  
Diabetes Status

Purpose: To evaluate the association between spousal diabetes status and the prevalence of diabetic retinopathy in Chinese patients with type 2 diabetes. Methods: A cross-sectional community-based study was performed in 1510 patients with type 2 diabetes in Shanghai, China. Non-mydriatic digital fundus photography was used to detect diabetic retinopathy. Spousal diabetes status was assessed using a standardised interview questionnaire. Results: The prevalence of diabetic retinopathy was significantly lower in patients who had diabetic spouses, compared with those who did not (20.2% vs 29.1%, p ⩽ 0.01). The fully adjusted odds ratio for diabetic retinopathy in those had diabetic spouses was decreased by 36% (odds ratio = 0.64, 95% confidence interval = 0.42–1.00, p = 0.048). The negative correlation between spousal diabetes status and diabetic retinopathy was presented in patients with the duration of diabetes ⩾ 10 years, those with HbA1c ⩾ 7% and those not using lipid-lowering drugs (odds ratio = 0.31, 95% confidence interval = 0.13–0.74, p = 0.0082; odds ratio = 0.50, 95% confidence interval = 0.27–0.94, p = 0.031; odds ratio = 0.58, 95% confidence interval = 0.37–0.92, p = 0.021, respectively). Conclusion: We demonstrated that spousal diabetes was associated with a lower diabetic retinopathy prevalence in Chinese patients with type 2 diabetes.

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