Am J Ophthalmol. 2021 Aug;228:245-254. doi: 10.1016/j.ajo.2021.03.051. Epub 2021 May 11.
The purpose of this study was to determine classification criteria for cytomegalovirus (CMV) retinitis.
Machine learning of cases with CMV retinitis 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 in 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 in the validation set.
A total of 803 cases of infectious posterior/panuveitides, including 211 cases of CMV retinitis, were evaluated by machine learning. Key criteria for CMV retinitis included: 1) necrotizing retinitis with indistinct borders due to numerous small satellites; 2) evidence of immune compromise; and either 3) a characteristic clinical appearance, or 4) positive polymerase chain reaction assay results for CMV from an intraocular specimen. Characteristic appearances for CMV retinitis included: 1) wedge-shaped area of retinitis; 2) hemorrhagic retinitis; or 3) granular retinitis. 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 CMV retinitis were 6.9% in the training set and 6.3% in the validation set.
The criteria for CMV retinitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
本研究旨在确定巨细胞病毒（CMV）视网膜炎的分类标准。
CMV 视网膜炎及其他 4 种感染性后葡萄膜炎/全葡萄膜炎病例的机器学习分析。
通过信息学设计的初步数据库收集感染性后葡萄膜炎/全葡萄膜炎病例，使用正式共识技术对诊断达成多数共识的病例构建最终数据库。病例分为训练集和验证集。在训练集中使用多项逻辑回归的机器学习来确定一组简洁的标准，这些标准可以最大限度地降低感染性后葡萄膜炎/全葡萄膜炎的分类错误率。将得出的标准在验证集中进行评估。
通过机器学习评估了共 803 例感染性后葡萄膜炎/全葡萄膜炎病例，包括 211 例 CMV 视网膜炎。CMV 视网膜炎的关键标准包括：1）边界不清的坏死性视网膜炎，由许多小卫星组成；2）免疫功能受损的证据；并且符合以下标准之一：3）具有特征性临床表现，或 4）从眼内标本中检测到 CMV 的聚合酶链反应（PCR）检测结果为阳性。CMV 视网膜炎的特征表现包括：1）视网膜炎呈楔形区域；2）出血性视网膜炎；或 3）颗粒状视网膜炎。训练集中感染性后葡萄膜炎/全葡萄膜炎的总体准确率为 92.1%，验证集中为 93.3%（95%置信区间：88.2-96.3）。训练集中 CMV 视网膜炎的分类错误率为 6.9%，验证集中为 6.3%。
CMV 视网膜炎的分类标准错误率较低，似乎足以用于临床和转化研究。
