Development of Classification Criteria for the Uveitides

Jabs, Douglas A.; McCluskey, Peter; Oden, Neal; Palestine, Alan G.; Peterson, Jerry A.; Saleem, Sophia; Thorne, Jennifer E.; Trusko, Brett

doi:10.1016/j.ajo.2021.03.061

Cited by 60 publications

(24 citation statements)

References 32 publications

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“…This retrospective study included patients from uveitis and other outpatient clinics at the University Hospital Bonn, Department of Ophthalmology, Germany, with posterior uveitis in agreement with the diagnostic criteria of the SUN working group 13 , 14 . It enrolled acute posterior multifocal placoid pigment epitheliopathy (APMPPE), ocular sarcoidosis mimicking birdshot chorioretinopathy in terms of non-pigmented bright lesions, punctate inner choroidopathy (PIC), birdshot chorioretinopathy, toxoplasmosis chorioretinits, multifocal choroiditis and panuveitis (MCP), presumed ocular histoplasmosis syndrome (POHS), or serpiginous choroiditis.…”

Section: Methodsmentioning

confidence: 99%

Spectrally resolved autofluorescence imaging in posterior uveitis

Wintergerst

Merten

Berger

et al. 2022

Sci Rep

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Clinical discrimination of posterior uveitis entities remains a challenge. This exploratory, cross-sectional study investigated the green (GEFC) and red emission fluorescent components (REFC) of retinal and choroidal lesions in posterior uveitis to facilitate discrimination of the different entities. Eyes were imaged by color fundus photography, spectrally resolved fundus autofluorescence (Color-FAF) and optical coherence tomography. Retinal/choroidal lesions’ intensities of GEFC (500–560 nm) and REFC (560–700 nm) were determined, and intensity-normalized Color-FAF images were compared for birdshot chorioretinopathy, ocular sarcoidosis, acute posterior multifocal placoid pigment epitheliopathy (APMPPE), and punctate inner choroidopathy (PIC). Multivariable regression analyses were performed to reveal possible confounders. 76 eyes of 45 patients were included with a total of 845 lesions. Mean GEFC/REFC ratios were 0.82 ± 0.10, 0.92 ± 0.11, 0.86 ± 0.10, and 1.09 ± 0.19 for birdshot chorioretinopathy, sarcoidosis, APMPPE, and PIC lesions, respectively, and were significantly different in repeated measures ANOVA (p < 0.0001). Non-pigmented retinal/choroidal lesions, macular neovascularizations, and fundus areas of choroidal thinning featured predominantly GEFC, and pigmented retinal lesions predominantly REFC. Color-FAF imaging revealed involvement of both, short- and long-wavelength emission fluorophores in posterior uveitis. The GEFC/REFC ratio of retinal and choroidal lesions was significantly different between distinct subgroups. Hence, this novel imaging biomarker could aid diagnosis and differentiation of posterior uveitis entities.

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Section: Methodsmentioning

confidence: 99%

Spectrally resolved autofluorescence imaging in posterior uveitis

Wintergerst

Merten

Berger

et al. 2022

Sci Rep

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“…For all patients, the uveitis diagnosis was achieved after an ophthalmological examination. Depending on the anatomical classification [ 13 , 26 ], all uveitis patients underwent a standardized screening protocol, which has been reported previously [ 19 ]. Patients with an etiological diagnosis in progress were not included.…”

Section: Methodsmentioning

confidence: 99%

“…Diagnosis of the different conditions causing uveitis is based on the presence of different features, such as genetic (e.g., HLA-B27, HLA-A29) and ethnic factors, epidemiologic characteristics (i.e., age, gender, ancestry), anatomical localization (i.e., anterior, intermediate, posterior, or panuveitis), the laterality (i.e., uni-or bilateral), and the chronicity (i.e., acute, recurrent, or chronic) [ 12 , 13 ]. Other ophthalmologic characteristics may also orient the diagnosis, such as the granulomatous character, the existence of ocular hypertonia, synechia, or retinal vasculitis (venous and/or arterial, occlusive), and the occurrence of single or multiple retinochoroidal lesions [ 6 , 13 ]. The differential diagnosis of uveitis is often complex for junior, general ophthalmologists or physicians.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Bayesian Network for Supporting the Etiological Diagnosis of Uveitis

Jamilloux

Romain-Scelle

Rabilloud

et al. 2021

JCM

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The etiological diagnosis of uveitis is complex. We aimed to implement and validate a Bayesian belief network algorithm for the differential diagnosis of the most relevant causes of uveitis. The training dataset (n = 897) and the test dataset (n = 154) were composed of all incident cases of uveitis admitted to two internal medicine departments, in two independent French centers (Lyon, 2003–2016 and Dijon, 2015–2017). The etiologies of uveitis were classified into eight groups. The algorithm was based on simple epidemiological characteristics (age, gender, and ethnicity) and anatomoclinical features of uveitis. The cross-validated estimate obtained in the training dataset concluded that the etiology of uveitis determined by the experts corresponded to one of the two most probable diagnoses in at least 77% of the cases. In the test dataset, this probability reached at least 83%. For the training and test datasets, when the most likely diagnosis was considered, the highest sensitivity was obtained for spondyloarthritis and HLA-B27-related uveitis (76% and 63%, respectively). The respective specificities were 93% and 54%. This algorithm could help junior and general ophthalmologists in the differential diagnosis of uveitis. It could guide the diagnostic work-up and help in the selection of further diagnostic investigations.

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“…Both the retrospective and the prospective phases require the compilation of specific data drawn from the ophthalmic examinations, such as the Lens Opacities Classification System III (LOCSIII), best corrected visual acuity (BCVA), the classifications provided by the Standardization of Uveitis Nomenclature (SUN) Working Group, the grading scheme for vitreous haze according with Nussenblatt et al, the National Eye Institute Grading System for Vitreous Cells (adopted by SUN Working Group), the ASU-WOG fluorescein angiography scoring system, and the ASUWOG indocyanine green angiographic scoring system [8][9][10][11][12][13][14].…”

Section: Registry Developmentmentioning

confidence: 99%

Development and Implementation of the AIDA International Registry for Patients with Non-Infectious Uveitis

et al. 2022

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Introduction:The aim of this paper is to point out the design, development and deployment of the AutoInflammatory Disease Alliance (AIDA) International Registry for paediatric and adult patients with non-infectious uveitis (NIU). Methods: This is a physician-driven, population-and electronic-based registry implemented for both retrospective and prospective collection of real-world demographics, clinical, laboratory, instrumental and socioeconomic data of patients with uveitis and other non-infectious inflammatory ocular diseases recruited through the AIDA Network. Data recruitment, based on Luca Cantarini and Claudia Fabiani contributed equally to this manuscript.

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Development of Classification Criteria for the Uveitides

Cited by 60 publications

References 32 publications

Spectrally resolved autofluorescence imaging in posterior uveitis

Spectrally resolved autofluorescence imaging in posterior uveitis

Development and Validation of a Bayesian Network for Supporting the Etiological Diagnosis of Uveitis

Development and Implementation of the AIDA International Registry for Patients with Non-Infectious Uveitis

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