Classification Criteria for Intermediate Uveitis, Non–Pars Planitis Type

Purpose: To determine classification criteria for intermediate uveitis, non-pars planitis type (IU- NPP, also known as undifferentiated intermediate uveitis) / Design: Machine learning of cases with IU-NPP and 4 other intermediate uveitides. / Methods: Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the 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 intermediate uveitides. The resulting criteria were evaluated on the validation set. / Results: Five hundred eighty-nine of cases of intermediate uveitides, including 114 cases of IU-NPP, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1, 99.9). Key criteria for IU-NPP included unilateral or bilateral intermediate uveitis with neither 1) snowballs in the vitreous nor 2) snowbanks on the pars plana. Other key exclusions included: 1) multiple sclerosis, 2) sarcoidosis, and 3) syphilis. The misclassification rates for pars planitis were 0 % in the training set and 0% in the validation set, respectively. / Conclusions: The criteria for IU-NPP had a low misclassification rate and appeared to perform well enough for use in clinical and translational research

Classification Criteria for Multiple Sclerosis-Associated Intermediate Uveitis

PURPOSE: The purpose of this study was to determine classification criteria for multiple sclerosis-associated intermediate uveitis. DESIGN: Machine learning of cases with multiple sclerosis-associated intermediate uveitis and 4 other intermediate uveitides. METHODS: Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the 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 intermediate uveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 589 cases of intermediate uveitides, including 112 cases of multiple sclerosis-associated intermediate uveitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval: 96.1-99.9). Key criteria for multiple sclerosis-associated intermediate uveitis included unilateral or bilateral intermediate uveitis and multiple sclerosis diagnosed by the McDonald criteria. Key exclusions included syphilis and sarcoidosis. The misclassification rates for multiple sclerosis-associated intermediate uveitis were 0 % in the training set and 0% in the validation set. CONCLUSIONS: The criteria for multiple sclerosis-associated intermediate uveitis had a low misclassification rate and appeared to perform sufficiently well enough for use in clinical and translational research

Fundus Findings in Chronic Granulomatous Disease

Chronic granulomatous disease (CGD) is a rare primary immunodeficiency disease that has been reported to present with various chorioretinal findings, predominantly in men. We report a case of a 17-year-old girl with a known diagnosis of CGD referred to the ophthalmology clinic for evaluation of an inflamed pingueculum. Upon clinical examination and ophthalmic imaging, high-quality montage fundus photographs demonstrated a wide array of bilaterally asymmetric chorioretinal findings known to be characteristic of the ophthalmic manifestations of CGD, including chorioretinitis and focal subretinal granuloma. This report also adds to the body of evidence that the chorioretinal findings associated with this disease have the potential to worsen over time

Subretinal Fibrosis and Uveitis: A Spectral Domain OCT Study of Its Evolution and the Minimal Therapeutic Effect of the Off-label Treatment with Ranibizumab

Purpose: The subretinal fibrosis and uveitis (SFU) syndrome is a rare multifocal posterior uveitis characterized by progressive subretinal fibrosis and significant visual loss. Methods: Slit-lamp examination, dilated fundoscopy, fluorescein angiography, Spectral Domain-Optical Coherence Tomography (SD-OCT) and laboratory testing were employed. Results: A 52-year-old male presented with bilateral (best-corrected visual acuity: 2/10) visual loss. Clinical examination revealed bilateral anterior uveitis with posterior synechiae and posterior uveitis. Medical workup revealed no pathologic findings. Treatment included 1 gr intravenous prednisone followed by oral prednisone, immunosuppresive therapy and three ranibizumab injections in the right eye with no improvement. One year later, there was significant subretinal fibrosis. In the second year follow-up, the picture was slightly worse, with persisting bilateral macular edema and fibrosis. Conclusions: This is the first SFU syndrome report monitored with SD-OCT. This novel imaging modality can localize the lesion level, guide the therapeutic approach and may prove helpful in assessing disease prognosis