ARE THERE TWO FORMS OF MULTIPLE EVANESCENT WHITE DOT SYNDROME?

PURPOSE: To analyze the nature of multiple evanescent white dot syndrome (MEWDS) and differentiate an idiopathic or primary form of MEWDS from a secondary form that is seen in association with other clinical conditions affecting the posterior segment of the eye. METHODS: Clinical and multimodal imaging findings including color fundus photography, fundus autofluorescence, fluorescein angiography, indocyanine green angiography, spectral-domain optical coherence tomography, and optical coherence tomography angiography of patients with secondary MEWDS are presented. RESULTS: Twenty consecutive patients with secondary MEWDS were evaluated. Fifteen patients were female. Most were young adults aged between 20 to 40 years with myopia (less than -6 diopters). Pathologic conditions associated with the secondary MEWDS reaction were high myopia (greater than -6 diopters) in two eyes, previous vitreoretinal surgery for rhegmatogenous retinal detachment in 2 eyes, and manifestations of multifocal choroiditis in 18 eyes. In all eyes, the MEWDS lesions followed a course of progression and resolution independent from the underlying condition. CONCLUSION: Secondary MEWDS seems to be an epiphenomenon ("EpiMEWDS") that may be seen in association with clinical manifestations disruptive to the choriocapillaris-Bruch membrane-retinal pigment epithelium complex.

Classification Criteria for Serpiginous Choroiditis.

PURPOSE: To determine classification criteria for serpiginous choroiditis. DESIGN: Machine learning of cases with serpiginous choroiditis 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 122 cases of serpiginous choroiditis, were evaluated by machine learning. Key criteria for serpiginous choroiditis included (1) choroiditis with an ameboid or serpentine shape; (2) characteristic imaging on fluorescein angiography or fundus autofluorescence; (3) absent to mild anterior chamber and vitreous inflammation; and (4) the exclusion of tuberculosis. 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 serpiginous choroiditis were 0% in both the training set and the validation set. CONCLUSIONS: The criteria for serpiginous choroiditis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.

Classification Criteria for Acute Posterior Multifocal Placoid Pigment Epitheliopathy.

PURPOSE: To determine classification criteria for acute posterior multifocal placoid pigment epitheliopathy (APMPPE). DESIGN: Machine learning of cases with APMPPE 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 posterior uveitides. The resulting criteria were evaluated on the validation set. RESULTS: One thousand sixty-eight cases of posterior uveitides, including 82 cases of APMPPE, were evaluated by machine learning. Key criteria for APMPPE included (1) choroidal lesions with a plaque-like or placoid appearance and (2) characteristic imaging on fluorescein angiography (lesions "block early and stain late diffusely"). Overall accuracy for posterior uveitides was 92.7% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for APMPPE were 5% in the training set and 0% in the validation set. CONCLUSIONS: The criteria for APMPPE had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.

Classification Criteria For Multiple Evanescent White Dot Syndrome.

PURPOSE: The purpose of this study was to determine classification criteria for multiple evanescent white dot syndrome (MEWDS). DESIGN: Machine learning of cases with MEWDS 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 in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior, or panuveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 1,068 cases of posterior uveitides, including 51 cases of MEWDS, were evaluated by machine learning. Key criteria for MEWDS included: 1) multifocal gray-white chorioretinal spots with foveal granularity; 2) characteristic imaging on fluorescein angiography ("wreath-like" hyperfluorescent lesions) and/or optical coherence tomography (hyper-reflective lesions extending from retinal pigment epithelium through ellipsoid zone into the retinal outer nuclear layer); and 3) absent to mild anterior chamber and vitreous inflammation. 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. Misclassification rates for MEWDS were 7% in the training set and 0% in the validation set. CONCLUSIONS: The criteria for MEWDS had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.