Literature DB >> 10647713

A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy.

G H Bresnick1, D B Mukamel, J C Dickinson, D R Cole.   

Abstract

OBJECTIVE: To provide scientifically based screening rules for the primary care setting designed to identify, through evaluation of a prescribed and limited portion of the posterior fundus, those patients with diabetes who have retinopathy severe enough to need referral to eye care specialists.
DESIGN: Retrospective analysis of the Early Treatment Diabetic Retinopathy Study (ETDRS) photographic data base. PARTICIPANTS: The fundus photographic grading data from 3711 patients with diabetes enrolled in the ETDRS.
METHODS: Multivariate regression techniques were used to identify retinopathy lesions in photographic fields 1, 2, 3, or a combination thereof that predict proliferative diabetic retinopathy (PDR) or clinically significant macular edema (CSME) within the seven standard fields. These were used to construct a family of screening rules with optimal combined sensitivity and specificity on which to base referrals to eye care specialists. MAIN OUTCOME MEASURES: Presence of moderate to severe nonproliferative diabetic retinopathy (NPDR), PDR, or CSME in graded fundus photographs.
RESULTS: Hemorrhages and microaneurysms (h/ma) temporal to the macula (photographic field 3), as severe as or more severe than ETDRS standard photograph 1 (h/ma 3 > or = 3), identified 87% to 89% of eyes with PDR and 92% to 93% of eyes with moderately severe to severe NPDR, which are at high risk for developing PDR. Extrapolating the results using retinopathy prevalence data from epidemiologic studies for the general older onset diabetic population, the calculated sensitivity for detecting PDR on a single examination is 87%, the specificity 80%; for moderate NPDR or worse, the sensitivity is 81 %, specificity 93%. Applying the presence of h/ma 3 > or = 3 as a screening rule to the older onset population, 26.5% of patients would be referred and 73.5% would not be referred. Any hard exudate within one disc diameter of the macular center detects CSME with sensitivity 94%, specificity 54%. Hard exudate of moderate or worse severity anywhere in the macular region (field 2) predicts CSME with sensitivity 89%, specificity 58%.
CONCLUSIONS: Screening protocols based on assessing retinopathy lesion severity in the posterior fundus have the potential to identify most diabetic patients with vision-threatening retinopathy. If the protocols can be implemented effectively in a primary care setting, patients requiring referral for specialty care could be reliably identified, and the total number of patients needing specialty referral could be substantially reduced from current guidelines.

Entities:  

Mesh:

Year:  2000        PMID: 10647713     DOI: 10.1016/s0161-6420(99)00010-x

Source DB:  PubMed          Journal:  Ophthalmology        ISSN: 0161-6420            Impact factor:   12.079


  26 in total

Review 1.  Retinal imaging and image analysis.

Authors:  Michael D Abràmoff; Mona K Garvin; Milan Sonka
Journal:  IEEE Rev Biomed Eng       Date:  2010

2.  Utility of hard exudates for the screening of macular edema.

Authors:  Taras V Litvin; Glen Y Ozawa; George H Bresnick; Jorge A Cuadros; Matthew S Muller; Ann E Elsner; Thomas J Gast
Journal:  Optom Vis Sci       Date:  2014-04       Impact factor: 1.973

3.  Telemedicine and Diabetic Retinopathy: Review of Published Screening Programs.

Authors:  Kevin Tozer; Maria A Woodward; Paula A Newman-Casey
Journal:  J Endocrinol Diabetes       Date:  2015-11-11

4.  Automated detection of diabetic retinopathy: barriers to translation into clinical practice.

Authors:  Michael D Abramoff; Meindert Niemeijer; Stephen R Russell
Journal:  Expert Rev Med Devices       Date:  2010-03       Impact factor: 3.166

Review 5.  Clinical Components of Telemedicine Programs for Diabetic Retinopathy.

Authors:  Mark B Horton; Paolo S Silva; Jerry D Cavallerano; Lloyd Paul Aiello
Journal:  Curr Diab Rep       Date:  2016-12       Impact factor: 4.810

6.  Segmentation of retinal blood vessels based on feature-oriented dictionary learning and sparse coding using ensemble classification approach.

Authors:  Navdeep Singh; Lakhwinder Kaur; Kuldeep Singh
Journal:  J Med Imaging (Bellingham)       Date:  2019-11-22

7.  Automated early detection of diabetic retinopathy.

Authors:  Michael D Abràmoff; Joseph M Reinhardt; Stephen R Russell; James C Folk; Vinit B Mahajan; Meindert Niemeijer; Gwénolé Quellec
Journal:  Ophthalmology       Date:  2010-06       Impact factor: 12.079

8.  EyePACS: an adaptable telemedicine system for diabetic retinopathy screening.

Authors:  Jorge Cuadros; George Bresnick
Journal:  J Diabetes Sci Technol       Date:  2009-05-01

9.  Nonmydriatic fundus photography for teleophthalmology diabetic retinopathy screening in rural and urban clinics.

Authors:  Eric K Chin; Bruna V Ventura; Kai-Yin See; Joann Seibles; Susanna S Park
Journal:  Telemed J E Health       Date:  2013-11-12       Impact factor: 3.536

10.  Accuracy of screening for diabetic retinopathy by family physicians.

Authors:  James M Gill; David M Cole; Harry M Lebowitz; James J Diamond
Journal:  Ann Fam Med       Date:  2004 May-Jun       Impact factor: 5.166

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