Development of Classification Criteria for the Uveitides.
Development of Classification Criteria for the Uveitides.
复制标题
开发葡萄威的分类标准。
DOI：
10.1016/j.ajo.2021.03.061
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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25种最常见的葡萄膜炎制定分类标准。机器学习使用5766例25葡萄膜炎。病例收集在信息学设计的初步数据库中。使用正式的共识技术，最终的数据库是由4046例达到绝大多数的诊断协议。在葡萄膜炎类别内分析病例，并将其分成训练集和验证集。机器学习在训练集上使用多项逻辑回归和套索正则化，以确定每种疾病的一组简约标准，并最大限度地减少错误分类率。在验证集中评价所得标准。在10%随机样本的情况下，由一个蒙面观察者评估为表达机器学习标准而开发的规则的准确性。在验证集中，按葡萄膜炎分类的总体准确性估计值为：前葡萄膜炎96.7%（95%置信区间[CI] 92.4，98.6）;中间葡萄膜炎99.3%（95% CI 96.1，99.9）;后葡萄膜炎98.0%（95% CI 94.3，99.3）;全葡萄膜炎94.0%（95% CI 89.0，96.8）;感染性后/全葡萄膜炎93.3%（95% CI 89.1，96.3）。对“规则”进行掩蔽评估的准确性为：前葡萄膜96.5%（95% CI 91.4，98.6）中间葡萄膜炎98.4%（91.5，99.7），后葡萄膜99.2%（95% CI 95.4，99.9），全葡萄膜炎98.9%（95% CI 94.3，99.8），感染性后部/全葡萄膜炎98.8%（95% CI 93.4，99.9）。这25种葡萄膜炎的分类标准具有较高的总体准确性（即低误分类率），并且似乎表现良好，足以用于临床和转化研究。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25种最常见葡萄膜炎的分类标准。所得到的标准在训练集和验证集中具有>90%的总体葡萄膜分类准确度，这表明在临床和转化研究中的潜在有用性。
To develop classification criteria for 25 of the most common uveitides. Machine learning using 5766 cases of 25 uveitides. Cases were collected in an informatics-designed preliminary database. Using formal consensus techniques, a final database was constructed of 4046 cases achieving supermajority agreement on the diagnosis. Cases were analyzed within uveitic class and were split into a training set and a validation set. Machine learning used multinomial logistic regression with lasso regularization on the training set to determine a parsimonious set of criteria for each disease and to minimize misclassification rates. The resulting criteria were evaluated in the validation set. Accuracy of the rules developed to express the machine learning criteria was evaluated by a masked observer in a 10% random sample of cases. Overall accuracy estimates by uveitic class in the validation set were: anterior uveitides 96.7% (95% confidence interval [CI] 92.4, 98.6); intermediate uveitides 99.3% (95% CI 96.1, 99.9); posterior uveitides 98.0% (95% CI 94.3, 99.3); panuveitides 94.0% (95% CI 89.0, 96.8); and infectious posterior/panuveitides 93.3% (95% CI 89.1, 96.3). Accuracies of the masked evaluation of the “rules” were: anterior uveitides 96.5% (95% CI 91.4, 98.6) intermediate uveitides 98.4% (91.5, 99.7), posterior uveitides 99.2% (95% CI 95.4, 99.9), panuveitides 98.9% (95% CI 94.3, 99.8), and infectious posterior/panuveitides 98.8% (95% CI 93.4, 99.9). The classification criteria for these 25 uveitides had high overall accuracy (i.e. low misclassification rates) 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 25 of the most common uveitides were developed. The resulting criteria had overall uveitic class accuracies >90% in both the training and validation sets, suggesting potential usefulness in clinical and translational research.
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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：
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：
--
作者：
Petri, Michelle;Orbai, Ana-Maria;Alarcon, Graciela S.;Gordon, Caroline;Merrill, Joan T.;Fortin, Paul R.;Bruce, Ian N.;Isenberg, David;Wallace, Daniel J.;Nived, Ola;Sturfelt, Gunnar;Ramsey-Goldman, Rosalind;Bae, Sang-Cheol;Hanly, John G.;Sanchez-Guerrero, Jorge;Clarke, Ann;Aranow, Cynthia;Manzi, Susan;Urowitz, Murray;Gladman, Dafna;Kalunian, Kenneth;Costner, Melissa;Werth, Victoria P.;Zoma, Asad;Bernatsky, Sasha;Ruiz-Irastorza, Guillermo;Khamashta, Munther A.;Jacobsen, Soren;Buyon, Jill P.;Maddison, Peter;Dooley, Mary Anne;van vollenhoven, Ronald F.;Ginzler, Ellen;Stoll, Thomas;Peschken, Christine;Jorizzo, Joseph L.;Callen, Jeffrey P.;Lim, S. Sam;Fessler, Barri J.;Inanc, Murat;Kamen, Diane L.;Rahman, Anisur;Steinsson, Kristjan;Franks, Andrew G., Jr.;Sigler, Lisa;Hameed, Suhail;Fang, Hong;Ngoc Pham;Brey, Robin;Weisman, Michael H.;McGwin, Gerald, Jr.;Magder, Laurence S.
通讯作者：
Magder, Laurence S.
影响因子：
5.8
作者：
Kursa, Miron B.;Rudnicki, Witold R.
通讯作者：
Rudnicki, Witold R.
影响因子：
13.3
作者：
Aringer, Martin;Costenbader, Karen;Johnson, Sindhu R.
通讯作者：
Johnson, Sindhu R.