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在基于社区验光的糖尿病眼部筛查项目中,数码摄影的分级者一致性、敏感性和特异性。

Grader agreement, and sensitivity and specificity of digital photography in a community optometry-based diabetic eye screening program.

作者信息

Sellahewa Luckni, Simpson Craig, Maharajan Prema, Duffy John, Idris Iskandar

机构信息

Diabetic Medicine Department, Nottingham University Hospitals, Nottingham, UK ; North Nottinghamshire Eye Screening Service, Sherwood Forest Hospitals Foundation Trust, University of Nottingham, Nottingham, UK.

North Nottinghamshire Eye Screening Service, Sherwood Forest Hospitals Foundation Trust, University of Nottingham, Nottingham, UK.

出版信息

Clin Ophthalmol. 2014 Jul 17;8:1345-9. doi: 10.2147/OPTH.S61483. eCollection 2014.

Abstract

BACKGROUND

Digital retinal photography with mydriasis is the preferred modality for diabetes eye screening. The purpose of this study was to evaluate agreement in grading levels between primary and secondary graders and to calculate their sensitivity and specificity for identifying sight-threatening disease in an optometry-based retinopathy screening program.

METHODS

This was a retrospective study using data from 8,977 patients registered in the North Nottinghamshire retinal screening program. In all cases, the ophthalmology diagnosis was used as the arbitrator and considered to be the gold standard. Kappa statistics were used to evaluate the level of agreement between graders.

RESULTS

Agreement between primary and secondary graders was 51.4% and 79.7% for detecting no retinopathy (R0) and background retinopathy (R1), respectively. For preproliferative (R2) and proliferative retinopathy (R3) at primary grading, agreement between the primary and secondary grader was 100%. Where there was disagreement between the primary and secondary grader for R1, only 2.6% (n=41) were upgraded by an ophthalmologist. The sensitivity and specificity for detecting R3 was 78.2% and 98.1%, respectively. None of the patients upgraded from any level of retinopathy to R3 required photocoagulation therapy. The observed kappa between the primary and secondary grader was 0.3223 (95% confidence interval 0.2937-0.3509), ie, fair agreement, and between the primary grader and ophthalmology for R3 was 0.5667 (95% confidence interval 0.4557-0.6123), ie, moderate agreement.

CONCLUSION

These data provide information on the safety of a community optometry-based retinal screening program for screening as a primary and as a secondary grader. The level of agreement between the primary and secondary grader at a higher level of retinopathy (R2 and R3) was 100%. Sensitivity and specificity for R3 were 78.2% and 98.1%, respectively. None of the false-negative results required photocoagulation therapy.

摘要

背景

散瞳数字视网膜摄影是糖尿病眼部筛查的首选方式。本研究的目的是评估初级和次级分级者之间分级水平的一致性,并计算他们在基于验光的视网膜病变筛查项目中识别威胁视力疾病的敏感性和特异性。

方法

这是一项回顾性研究,使用了北诺丁汉郡视网膜筛查项目中登记的8977例患者的数据。在所有病例中,眼科诊断被用作仲裁标准,并被视为金标准。kappa统计用于评估分级者之间的一致性水平。

结果

对于检测无视网膜病变(R0)和背景性视网膜病变(R1),初级和次级分级者之间的一致性分别为51.4%和79.7%。对于初级分级中的增殖前期(R2)和增殖性视网膜病变(R3),初级和次级分级者之间的一致性为100%。在R1分级中初级和次级分级者存在分歧的情况下,只有2.6%(n = 41)被眼科医生升级。检测R3的敏感性和特异性分别为78.2%和98.1%。从任何视网膜病变级别升级到R3的患者均无需光凝治疗。初级和次级分级者之间观察到的kappa值为0.3223(95%置信区间为

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本文引用的文献

1
A simple risk stratification for time to development of sight-threatening diabetic retinopathy.
Diabetes Care. 2013 Mar;36(3):580-5. doi: 10.2337/dc12-0625. Epub 2012 Nov 12.
2
External quality assurance for image grading in the Scottish Diabetic Retinopathy Screening Programme.
Diabet Med. 2012 Jun;29(6):776-83. doi: 10.1111/j.1464-5491.2011.03504.x.
5
Grading and disease management in national screening for diabetic retinopathy in England and Wales.
Diabet Med. 2003 Dec;20(12):965-71. doi: 10.1111/j.1464-5491.2003.01077.x.
7
The evaluation of screening policies for diabetic retinopathy using simulation.
Diabet Med. 2002 Sep;19(9):762-70. doi: 10.1046/j.1464-5491.2002.00773.x.
8
Preservation of sight in diabetes: developing a national risk reduction programme.
Diabet Med. 2000 Sep;17(9):627-34. doi: 10.1046/j.1464-5491.2000.00353.x.
9
Screening and prevention of diabetic blindness.
Acta Ophthalmol Scand. 2000 Aug;78(4):374-85. doi: 10.1034/j.1600-0420.2000.078004374.x.
10
Effectiveness of screening and monitoring tests for diabetic retinopathy--a systematic review.
Diabet Med. 2000 Jul;17(7):495-506. doi: 10.1046/j.1464-5491.2000.00250.x.

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