Classification Criteria for Fuchs Uveitis Syndrome.
Classification Criteria for Fuchs Uveitis Syndrome.
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DOI：
10.1016/j.ajo.2021.03.052
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发表时间：
2021-08
影响因子：
4.2
通讯作者：
Standardization of Uveitis Nomenclature (SUN) Working Group
中科院分区：
文献类型：
--
作者：
Standardization of Uveitis Nomenclature (SUN) Working Group
探讨Fuchs葡萄膜炎综合征的分类标准。Fuchs葡萄膜炎综合征和其他8种前葡萄膜炎病例的机器学习。前葡萄膜炎病例收集在信息学设计的初步数据库中，最终数据库的构建采用正式的共识技术，在诊断上取得绝大多数一致的病例。将病例分为训练集和验证集。在训练集上使用使用多项逻辑回归的机器学习来确定一组最小化前葡萄膜错误分类率的简约标准。在验证集上评价所得标准。1083例前葡萄膜炎患者，包括146例Fuchs葡萄膜炎综合征患者，通过机器学习进行了评估。前葡萄膜炎的总体准确度在训练集中为97.5%，在验证集中为96.7%（95%置信区间92.4，98.6）。Fuchs葡萄膜炎综合征的关键标准包括单侧前葡萄膜炎伴或不伴玻璃体炎以及：1）异色或2）单侧弥漫性虹膜萎缩和星状角膜沉淀。FUS的错误分类率在训练集中为4.7%，在验证集中为5.5%。Fuchs葡萄膜炎综合征的标准具有较低的错误分类率，并且表现良好，足以用于临床和转化研究。使用正式的方法来制定分类标准，包括基于信息学的病例收集，基于共识技术的病例选择和机器学习，制定了Fuchs葡萄膜炎综合征的分类标准。关键标准包括单侧前葡萄膜炎，伴有：1）异色或2）单侧弥漫性虹膜萎缩和星状角膜沉淀。由此产生的标准有一个低的错误分类率。
To determine classification criteria for Fuchs uveitis syndrome. Machine learning of cases with Fuchs uveitis syndrome and 8 other anterior uveitides. Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set. One thousand eighty-three cases of anterior uveitides, including 146 cases of Fuchs uveitis syndrome, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for Fuchs uveitis syndrome included unilateral anterior uveitis with or without vitritis and either: 1) heterochromia or 2) unilateral diffuse iris atrophy and stellate keratic precipitates. The misclassification rates for FUS were 4.7% in the training set and 5.5% in the validation set, respectively. The criteria for Fuchs uveitis syndrome had a low misclassification rate and appeared to perform well enough for use in clinical and translational research. Using a formalized approach to developing classification criteria, including informatics-based case collection, consensus-technique-based case selection, and machine learning, classification criteria for Fuchs uveitis syndrome were developed. Key criteria included unilateral anterior uveitis with either: 1) heterochromia or 2) unilateral diffuse iris atrophy and stellate keratic precipitates. The resulting criteria had a low misclassification rate.
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影响因子：
1.7
作者：
Trusko B;Thorne J;Jabs D;Belfort R;Dick A;Gangaputra S;Nussenblatt R;Okada A;Rosenbaum J;Standardization of Uveitis Nomenclature (SUN) Project
通讯作者：
Standardization of Uveitis Nomenclature (SUN) Project
DOI：
10.1007/s00417-009-1239-7
发表时间：
2010-04-01
影响因子：
2.7
作者：
Ruokonen, Peter C.;Metzner, Sylvia;Pleyer, Uwe
通讯作者：
Pleyer, Uwe
影响因子：
4.1
作者：
Chee, S-P;Jap, A.
通讯作者：
Jap, A.
影响因子：
4.7
作者：
Aggarwal, Rohit;Ringold, Sarah;Khanna, Dinesh;Neogi, Tuhina;Johnson, Sindhu R.;Miller, Amy;Brunner, Hermine I.;Ogawa, Rikke;Felson, David;Ogdie, Alexis;Aletaha, Daniel;Feldman, Brian M.
通讯作者：
Feldman, Brian M.
影响因子：
8.1
作者：
Gonzales, John A.;Hinterwirth, Armin;Thuy Doan
通讯作者：
Thuy Doan