Classification Criteria for Syphilitic Uveitis.
Classification Criteria for Syphilitic Uveitis.
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DOI：
10.1016/j.ajo.2021.03.039
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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
确定梅毒葡萄膜炎和其他24例葡萄膜炎的机器学习分类标准。在信息学设计的初步数据库中收集了前部、中间部、后部和全葡萄膜炎的病例，并使用正式的共识技术构建了最终数据库，这些病例在诊断上取得了绝大多数的一致。病例按解剖分类进行分析，每个分类分为训练集和验证集。在训练集上使用使用多项Logistic回归的机器学习来确定一组简约的标准，以最小化不同葡萄膜类之间的错误分类率。在验证集上对结果标准进行评估。用机器学习方法对222例梅毒葡萄膜炎患者进行评估，并与葡萄膜炎相关类别中的其他葡萄膜炎患者进行比较。梅毒葡萄膜炎的关键标准包括：(1)前葡萄膜炎，(2)中间葡萄膜炎，或(3)伴有视网膜、视网膜色素上皮或视网膜血管炎症的后葡萄膜炎或全葡萄膜炎，以及梅毒螺旋体试验阳性的梅毒感染证据。推荐美国疾病预防控制中心梅毒检测反向筛查算法。梅毒葡萄膜炎的误分率前葡萄膜炎为0%，中间葡萄膜炎为6.0%，后葡萄膜炎为0%，全葡萄膜炎为0%，感染性后葡萄膜炎为8.6%。验证集对梅毒葡萄膜炎诊断的总体准确率为100%(99%可信区间99.5,100)，即验证集对每类葡萄膜炎的错误分类率为0%。梅毒葡萄膜炎的标准误分率很低，而且表现得足够好，可用于临床和翻译研究。采用形式化的方法制定分类标准，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，制定了梅毒葡萄膜炎的分类标准。关键标准包括相容的葡萄膜炎综合征和梅毒螺旋体试验阳性的证据。由此产生的分类标准具有较低的误分率。
To determine classification criteria for syphilitic uveitis Machine learning of cases with syphilitic uveitis and 24 other uveitides. Cases of anterior, intermediate, posterior, 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 set to determine a parsimonious set of criteria that minimized the misclassification rate among the different uveitic classes. The resulting criteria were evaluated on the validation set. Two hundred twenty-two cases of syphilitic uveitis were evaluated by machine learning with cases evaluated against other uveitides in the relevant uveitic class. Key criteria for syphilitic uveitis included a compatible uveitic presentation, (1) anterior uveitis, 2) intermediate uveitis, or 3) posterior or panuveitis with retinal, retinal pigment epithelial, or retinal vascular inflammation) and evidence of syphilis infection with a positive treponemal test. The Centers for Disease Control and Prevention reverse screening algorithm for syphilis testing is recommended. The misclassification rates for syphilitic uveitis in the training sets were: anterior uveitides 0%, intermediate uveitides 6.0%, posterior uveitides 0%, panuveitides 0%, and infectious posterior/panuveitides 8.6%. The overall accuracy of the diagnosis of syphilitic uveitis in the validation set was 100% (99% CI 99.5, 100) – i.e. the validation sets misclassification rates were 0% for each uveitic class. The criteria for syphilitic 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 syphilitic uveitis were developed. Key criteria included a compatible uveitic syndrome and evidence of syphilis with a positive treponemal test. The resulting classification criteria had a low misclassification rate.
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影响因子：
4.2
作者：
Standardization of Uveitis Nomenclature (SUN) Working Group
通讯作者：
Standardization of Uveitis Nomenclature (SUN) Working Group
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：
33.9
作者：
Oliver, Sara E.;Aubin, Mark;Markowitz, Lauri
通讯作者：
Markowitz, Lauri
影响因子：
4
作者：
Rose-Nussbaumer, Jennifer;Goldstein, Debra A.;Smith, Justine R.
通讯作者：
Smith, Justine R.
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.