Literature DB >> 15606463

Quantitative analysis of retinopathy in type 2 diabetes: identification of prognostic parameters for developing visual loss secondary to diabetic maculopathy.

M N Hove1, J K Kristensen, T Lauritzen, T Bek.   

Abstract

PURPOSE: To describe whether quantitative assessment of early changes in the morphology of retinopathy lesions can predict development of vision-threatening diabetic maculopathy.
METHODS: We used a nested case-control study, and we studied 11 type 2 diabetes patients who had developed visual loss secondary to diabetic maculopathy. For each diabetes patient, we also studied three matched control patients who had been followed for a comparable period of time without developing visual loss. Fundus photographs describing the early development of retinopathy were digitized and subjected to a full manual quantitative grading on a computer monitor. Differences in the early development of retinal morphology were compared between the two groups. The outcome parameters were changes in the number and area of haemorrhages and exudates in different regions of the fundus, and the weighted distance of these lesions from the fovea and the optic disc.
RESULTS: In patients who developed visual loss secondary to diabetic maculopathy there was significant early progression in the total area and number of haemorrhages and exudates. The haemorrhages had progressed in all retinal areas except the area around the optic disc and the temporal vascular arcades. The exudates had progressed temporally from the fovea and in the retinal periphery.
CONCLUSIONS: The results suggest that a quantitative description of the regional development of early diabetic retinopathy may help in identifying patients who will later develop vision-threatening maculopathy.

Entities:  

Mesh:

Year:  2004        PMID: 15606463     DOI: 10.1111/j.1600-0420.2004.00364.x

Source DB:  PubMed          Journal:  Acta Ophthalmol Scand        ISSN: 1395-3907


  3 in total

Review 1.  Diameter Changes of Retinal Vessels in Diabetic Retinopathy.

Authors:  Toke Bek
Journal:  Curr Diab Rep       Date:  2017-08-08       Impact factor: 4.810

2.  A deep learning system for detecting diabetic retinopathy across the disease spectrum.

Authors:  Ling Dai; Liang Wu; Huating Li; Chun Cai; Qiang Wu; Hongyu Kong; Ruhan Liu; Xiangning Wang; Xuhong Hou; Yuexing Liu; Xiaoxue Long; Yang Wen; Lina Lu; Yaxin Shen; Yan Chen; Dinggang Shen; Xiaokang Yang; Haidong Zou; Bin Sheng; Weiping Jia
Journal:  Nat Commun       Date:  2021-05-28       Impact factor: 14.919

3.  Spatial distribution of early red lesions is a risk factor for development of vision-threatening diabetic retinopathy.

Authors:  Giovanni Ometto; Phil Assheton; Francesco Calivá; Piotr Chudzik; Bashir Al-Diri; Andrew Hunter; Toke Bek
Journal:  Diabetologia       Date:  2017-09-07       Impact factor: 10.122

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.