American journal of ophthalmology · 2021
Classification Criteria for Birdshot Chorioretinitis.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Standardization of Uveitis Nomenclature (SUN) Working Group
Аннотация
PURPOSE: To determine classification criteria for birdshot chorioretinitis.
DESIGN: Machine learning of cases with birdshot chorioretinitis and 8 other posterior uveitides.
METHODS: Cases of posterior uveitides 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 uveitides / panuveitides. The resulting criteria were evaluated on the validation set.
RESULTS: One thousand sixty-eight cases of posterior uveitides, including 207 cases of birdshot chorioretinitis, were evaluated by machine learning. Key criteria for birdshot chorioretinitis included a multifocal choroiditis with (1) the characteristic appearance of a bilateral multifocal choroiditis with cream-colored or yellow-orange, oval or round choroidal spots ("birdshot" spots); (2) absent to mild anterior chamber inflammation; and (3) absent to moderate vitreous inflammation; or multifocal choroiditis with positive HLA-A29 testing and either classic "birdshot spots" or characteristic imaging on indocyanine green angiography. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for birdshot chorioretinitis were 10% in the training set and 0% in the validation set.
CONCLUSIONS: The criteria for birdshot chorioretinitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.
DESIGN: Machine learning of cases with birdshot chorioretinitis and 8 other posterior uveitides.
METHODS: Cases of posterior uveitides 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 uveitides / panuveitides. The resulting criteria were evaluated on the validation set.
RESULTS: One thousand sixty-eight cases of posterior uveitides, including 207 cases of birdshot chorioretinitis, were evaluated by machine learning. Key criteria for birdshot chorioretinitis included a multifocal choroiditis with (1) the characteristic appearance of a bilateral multifocal choroiditis with cream-colored or yellow-orange, oval or round choroidal spots ("birdshot" spots); (2) absent to mild anterior chamber inflammation; and (3) absent to moderate vitreous inflammation; or multifocal choroiditis with positive HLA-A29 testing and either classic "birdshot spots" or characteristic imaging on indocyanine green angiography. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for birdshot chorioretinitis were 10% in the training set and 0% in the validation set.
CONCLUSIONS: The criteria for birdshot chorioretinitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.