Classification Criteria for Acute Retinal Necrosis Syndrome.
Classification Criteria for Acute Retinal Necrosis Syndrome.
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DOI：
10.1016/j.ajo.2021.03.057
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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
确定急性视网膜坏死（ARN）的分类标准。ARN和其他4种感染性后部/全葡萄膜炎病例的机器学习。感染性后部/全葡萄膜炎病例收集在信息学设计的初步数据库中，并使用正式共识技术构建了诊断达成绝大多数一致的病例的最终数据库。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感染性后/全葡萄膜炎的错误分类率的简约标准。在验证集上评价所得标准。803例感染性后部/全葡萄膜炎，包括186例ARN，通过机器学习进行了评估。ARN的关键标准包括：1）周围坏死性视网膜炎;和2）单纯疱疹病毒或水痘带状疱疹病毒阳性的眼内液样本的聚合酶链反应测定;或3）具有环形或融合性视网膜炎、视网膜血管鞘和/或阻塞以及超过最小玻璃体炎的特征性临床表现。感染性后部/全葡萄膜炎的总体准确度在训练集中为92.1%，在验证集中为93.3%（95%置信区间88.2，96.3）。ARN的错误分类率在训练集中为15%，在验证集中为11.5%。ARN的标准具有相当低的错误分类率，并且似乎在临床和转化研究中表现得足够好。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，开发了急性视网膜坏死的分类标准。关键标准包括外周坏死性视网膜炎和单纯疱疹或水痘带状疱疹病毒眼内感染的PCR证据，或具有环形或融合性视网膜炎、视网膜血管鞘和/或阻塞和玻璃体炎的特征性临床表现。由此产生的分类标准有一个合理的低错误分类率。
To determine classification criteria for acute retinal necrosis (ARN). Machine learning of cases with ARN and 4 other infectious posterior/ panuveitides. Cases of infectious posterior/panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on 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 infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set. Eight hundred three cases of infectious posterior/panuveitides, including 186 cases of ARN, were evaluated by machine learning. Key criteria for ARN included: 1) peripheral necrotizing retinitis; and either 2) polymerase chain reaction assay of an intraocular fluid specimen positive for either herpes simplex virus or varicella zoster virus; or 3) a characteristic clinical appearance with circumferential or confluent retinitis, retinal vascular sheathing and/or occlusion, and more than minimal vitritis. Overall accuracy for infectious posterior/panuveitides was 92.1% in the training set and 93.3% (95% confidence interval 88.2, 96.3) in the validation set. The misclassification rates for ARN were 15% in the training set and 11.5% in the validation set. The criteria for ARN had a reasonably 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 the acute retinal necrosis were developed. Key criteria included peripheral necrotizing retinitis and either PCR evidence of intraocular infection with herpes simplex or varicella zoster virus or characteristic clinical picture with circumferential or confluent retinitis, retinal vascular sheathing and/or occlusion, and vitritis. The resulting classification criteria had a reasonably low misclassification rate.
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影响因子：
4.2
作者：
Baltinas, Julijana;Lightman, Sue;Tomkins-Netzer, Oren
通讯作者：
Tomkins-Netzer, Oren
DOI：
10.1073/pnas.1521651112
发表时间：
2015-12-22
影响因子：
11.1
作者：
Casanova, Jean-Laurent
通讯作者：
Casanova, Jean-Laurent
影响因子：
4.1
作者：
BROWN, RM;MENDIS, U
通讯作者：
MENDIS, U
影响因子：
4.2
作者：
Gangaputra, Sapna;Drye, Lea;Lyon, Alice T.
通讯作者：
Lyon, Alice T.
影响因子：
4.2
作者：
FORSTER, DJ;DUGEL, PU;RAO, NA
通讯作者：
RAO, NA