Classification Criteria for Sarcoidosis-Associated Uveitis.
Classification Criteria for Sarcoidosis-Associated Uveitis.
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DOI：
10.1016/j.ajo.2021.03.047
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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
为确定结节病相关葡萄膜炎的分类标准，对结节病葡萄膜炎和其他15种葡萄膜炎病例进行机器学习。前，中间，和panuveitides的情况下收集在一个信息设计的初步数据库，并最终数据库的情况下，实现绝大多数的诊断协议，使用正式的共识技术。病例按解剖类别进行分析，每个类别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最小化中间葡萄膜炎之间的错误分类率的一组简约标准。在验证集上评价所得标准。通过机器学习对1083例前葡萄膜炎、589例中间葡萄膜炎和1012例全葡萄膜炎（包括278例结节病相关葡萄膜炎）进行了评估。结节病相关性葡萄膜炎的关键标准包括任何解剖学分类的相容性葡萄膜炎综合征和结节病证据，1）组织活检显示非干酪化肉芽肿或2）胸部成像显示双侧肺门腺病。在验证集中诊断结节病相关性葡萄膜炎的总体准确率为99.7%在训练集中结节病相关葡萄膜炎的误分类率为：前葡萄膜炎3.2%，中间葡萄膜炎2.6%，和全葡萄膜炎1.2%;在验证集中，误分类率为：前葡萄膜炎0%，中间葡萄膜炎0%，全葡萄膜炎0%。结节病相关性葡萄膜炎的标准具有较低的错误分类率，并且似乎在临床和转化研究中表现足够好。使用正式的方法来开发分类标准，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，开发了结节病相关葡萄膜炎的分类标准。关键标准包括相容性葡萄膜炎综合征和结节病证据，组织活检显示非干酪化肉芽肿或胸部影像学显示双侧肺门腺病。由此产生的分类标准有一个低的错误分类率。
To determine classification criteria for sarcoidosis-associated uveitis Machine learning of cases with sarcoid uveitis and 15 other uveitides. Cases of anterior, intermediate, and panuveitides 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 analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training sets to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation sets. One thousand eighty-three anterior uveitides, 589 intermediate uveitides, and 1012 panuveitides, including 278 cases of sarcoidosis-associated uveitis, were evaluated by machine learning. Key criteria for sarcoidosis-associated uveitis included a compatible uveitic syndrome of any anatomic class and evidence of sarcoidosis, either 1) a tissue biopsy demonstrating non-caseating granulomata or 2) bilateral hilar adenopathy on chest imaging. The overall accuracy of the diagnosis of sarcoidosis-associated uveitis in the validation set was 99.7% (95% confidence interval 98.8, 99.9).The misclassification rates for sarcoidosis-associated uveitis in the training sets were: anterior uveitis 3.2%, intermediate uveitis 2.6%, and panuveitis 1.2%; in the validation sets the misclassification rates were: anterior uveitis 0%, intermediate uveitis 0%, and panuveitis 0%, respectively. The criteria for sarcoidosis-associated uveitis had a low misclassification rate and appeared to perform sufficiently well 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 sarcoidosis-associated uveitis were developed. Key criteria included a compatible uveitic syndrome and evidence of sarcoidosis with either a tissue biopsy demonstrating non-caseating granulomata or chest imaging demonstrating bilateral hilar adenopathy. The resulting classification 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
影响因子：
4.2
作者：
Nora, Rina La Distia;van Velthoven, Miriam E. J.;Rothova, Aniki
通讯作者：
Rothova, Aniki
影响因子：
3.3
作者：
Ungprasert P;Tooley AA;Crowson CS;Matteson EL;Smith WM
通讯作者：
Smith WM
影响因子：
3.3
作者：
Herbort, Carl P.;Rao, Narsing A.;Mochizuki, Manabu
通讯作者：
Mochizuki, Manabu
影响因子：
4.2
作者：
Kosmorsky, GS;Meisler, DM;Lowder, CY
通讯作者：
Lowder, CY