Epidemiology, clinical features, and classification of 3,404 patients with uveitis: Colombian Uveitis Multicenter Study (COL-UVEA).

PURPOSE: To describe the epidemiology, clinical features, and classification of uveitis in a large cohort of Colombian patients. METHODS: Data were collected from seven ophthalmological referral centers in the four main cities in Colombia. The study included patients with a confirmed diagnosis of uveitis from January 2010 to December 2022. Information on demographics, ophthalmic examination findings, uveitis classification, and etiology was recorded. RESULTS: The study reviewed 3,404 clinical records of patients with uveitis. The mean age at diagnosis was 41.1 (SD 19.0) years, and 54.2% of the patients were female. Overall, 1,341(39.4%) were infectious, 626 (18.4%) non-infectious, and four masquerade syndromes (0.1%). The most common types of uveitis were unilateral (66.7%), acute (48.3%), and non-granulomatous (83%). Anterior uveitis was the most common anatomical localization (49.5%), followed by posterior uveitis (22.9%), panuveitis (22.3%), and intermediate uveitis (5.2%). A diagnosis was established in 3,252 (95.5%) cases; idiopathic was the most common cause (27.7%), followed by toxoplasmosis (25.3%) and virus-associated uveitis (6.4%). The age group between 30 and 50 exhibited the highest frequency of uveitis. CONCLUSION: This multicenter study comprehensively describes uveitis characteristics in Colombian patients, providing valuable insights into its demographic and clinical features. The study findings emphasize the need to continue updating the changing patterns of uveitis to improve diagnosis and treatment strategies for diseases associated with intraocular inflammation.

AUTOMATED DETECTION OF VITRITIS USING ULTRAWIDE-FIELD FUNDUS PHOTOGRAPHS AND DEEP LEARNING.

BACKGROUND/PURPOSE: Evaluate the performance of a deep learning algorithm for the automated detection and grading of vitritis on ultrawide-field imaging. METHODS: Cross-sectional noninterventional study. Ultrawide-field fundus retinophotographs of uveitis patients were used. Vitreous haze was defined according to the six steps of the Standardization of Uveitis Nomenclature classification. The deep learning framework TensorFlow and the DenseNet121 convolutional neural network were used to perform the classification task. The best fitted model was tested in a validation study. RESULTS: One thousand one hundred eighty-one images were included. The performance of the model for the detection of vitritis was good with a sensitivity of 91%, a specificity of 89%, an accuracy of 0.90, and an area under the receiver operating characteristics curve of 0.97. When used on an external set of images, the accuracy for the detection of vitritis was 0.78. The accuracy to classify vitritis in one of the six Standardization of Uveitis Nomenclature grades was limited (0.61) but improved to 0.75 when the grades were grouped into three categories. When accepting an error of one grade, the accuracy for the six-class classification increased to 0.90, suggesting the need for a larger sample to improve the model performances. CONCLUSION: A new deep learning model based on ultrawide-field fundus imaging that produces an efficient tool for the detection of vitritis was described. The performance of the model for the grading into three categories of increasing vitritis severity was acceptable. The performance for the six-class grading of vitritis was limited but can probably be improved with a larger set of images.

Uveítis: estudio de 109 casos / Uveitis: Study of 109 cases

Objetivo Analizar y describir la clasificación anatómica y etiológica, las características clínicas y epidemiológicas y los síntomas más frecuentes de los casos de uveítis en nuestra población. Pacientes y método Estudio observacional descriptivo, retrospectivo de los casos de uveítis atendidos en el Servicio de Oftalmología del Hospital Universitario Virgen del Rocío en el año 2021. Se estudiaron las características demográficas y clínicas. Resultados Se estudiaron 109 casos de uveítis, en 46 hombres y 63 mujeres, con una media de edad de 45,43±16,11 años. Los síntomas más frecuentes fueron dolor (74,31%), hiperemia (73,39%) y visión borrosa (65,14%). La clasificación anatómica más frecuente fue anterior (55,96%), seguida de panuveítis (18,35%), uveítis posterior (12,84%) e intermedia (7,34%). En cuanto a la etiología, la mayoría de las uveítis fueron idiopáticas (42,99%), seguidas de las no infecciosas (38,32%) y las infecciosas (18,69%). Conclusiones En el sur de España, las características de la uveítis son similares a las de otros estudios en países occidentales (AU)

Purpose Analyse and describe the anatomical and etiological classification, clinical and epidemiological characteristics and most frequent symptoms of uveitis cases in our population. Patients and method Descriptive, retrospective observational study of uveitis cases treated in the Ophthalmology Department of Virgen del Rocío University Hospital in 2021. The demographic and clinical characteristics were studied. Results A total of 109cases of uveitis were studied, 46 men and 63 women, with a mean age of 45.43±16.11 years. The most frequent symptoms were pain (74.31%), hyperemia (73.39%) and blurred vision (65.14%). The most frequent anatomical classification was anterior (55.96%), followed by panuveitis (18.35%), posterior (12.84%) and intermediate uveitis (7.34%). Regarding etiology, most uveitis were idiopathic (42.99%), followed by non-infectious (38.32%) and finally infectious (18.69%). Conclusions In southern España, the characteristics of uveitis are similar to other studies in Western countries (AU)

Classification of Seasonal Hyperacute Panuveitis (SHAPU).

INTRODUCTION: Classification are essential part of scientific methodology and has important role in medical reporting system. Even after having 46 years long history, Seasonal Hyperacute Panuveitis (SHAPU), the blinding diseases reported mainly from Nepal lacks a standard classification system. Thus, we aim to contribute in the ophthalmic nosology by purposing a classification system for SHAPU. METHODOLOGY: The classification is suggested on the background of prolonged experience of this entity by the group of investigators who have dedicated years of research on this topic. CONCLUSION: We are optimistic that the proposed classification system will help in effective planning and evaluation of this ocular emergency condition and deliver the appropriate and reliable information for timely management and prevention of complications.

Development of Classification Criteria for the Uveitides.

PURPOSE: To develop classification criteria for 25 of the most common uveitides. DESIGN: Machine learning using 5,766 cases of 25 uveitides. METHODS: Cases were collected in an informatics-designed preliminary database. Using formal consensus techniques, a final database was constructed of 4,046 cases achieving supermajority agreement on the diagnosis. Cases were analyzed within uveitic class and were split into a training set and a validation set. Machine learning used multinomial logistic regression with lasso regularization on the training set to determine a parsimonious set of criteria for each disease and to minimize misclassification rates. The resulting criteria were evaluated in the validation set. Accuracy of the rules developed to express the machine learning criteria was evaluated by a masked observer in a 10% random sample of cases. RESULTS: Overall accuracy estimates by uveitic class in the validation set were as follows: anterior uveitides 96.7% (95% confidence interval [CI] 92.4, 98.6); intermediate uveitides 99.3% (95% CI 96.1, 99.9); posterior uveitides 98.0% (95% CI 94.3, 99.3); panuveitides 94.0% (95% CI 89.0, 96.8); and infectious posterior uveitides / panuveitides 93.3% (95% CI 89.1, 96.3). Accuracies of the masked evaluation of the "rules" were anterior uveitides 96.5% (95% CI 91.4, 98.6) intermediate uveitides 98.4% (91.5, 99.7), posterior uveitides 99.2% (95% CI 95.4, 99.9), panuveitides 98.9% (95% CI 94.3, 99.8), and infectious posterior uveitides / panuveitides 98.8% (95% CI 93.4, 99.9). CONCLUSIONS: The classification criteria for these 25 uveitides had high overall accuracy (ie, low misclassification rates) and seemed to perform well enough for use in clinical and translational research.

Classification Criteria for Sarcoidosis-Associated Uveitis.

PURPOSE: The purpose of this study was to determine classification criteria for sarcoidosis-associated uveitis. DESIGN: Machine learning of cases with sarcoid uveitis and 15 other uveitides. METHODS: Cases of anterior, intermediate, and panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed including cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used in the training sets to determine a parsimonious set of criteria that minimized the misclassification rate among the uveitides. The resulting criteria were evaluated in the validation sets. RESULTS: A total of 1,083 cases of anterior uveitides, 589 cases of intermediate uveitides, and 1,012 cases of panuveitides, including 278 cases of sarcoidosis-associated uveitis, were evaluated by machine learning. Key criteria for sarcoidosis-associated uveitis included a compatible uveitic syndrome of any anatomic class and evidence of sarcoidosis, either 1) tissue biopsy results demonstrating non-caseating granulomata or 2) bilateral hilar adenopathy on chest imaging. The overall accuracy of the diagnosis of sarcoidosis-associated uveitis in the validation set was 99.7% (95% confidence interval: 98.8-99.9). The misclassification rates for sarcoidosis-associated uveitis in the training sets were 3.2% in anterior uveitis, 2.6% in intermediate uveitis, and 1.2% in panuveitis; in the validation sets, the misclassification rates were 0% in anterior uveitis, 0% in intermediate uveitis, and 0% in panuveitis. CONCLUSIONS: The criteria for sarcoidosis-associated uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Classification Criteria for Fuchs Uveitis Syndrome.

PURPOSE: To determine classification criteria for Fuchs' uveitis syndrome. DESIGN: Machine learning of cases with Fuchs' uveitis syndrome and 8 other anterior uveitides. METHODS: Cases of anterior 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 anterior uveitides. The resulting criteria were evaluated on the validation set. RESULTS: One thousand eighty-three cases of anterior uveitides, including 146 cases of Fuchs' uveitis syndrome, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for Fuchs' uveitis syndrome included unilateral anterior uveitis with or without vitritis and either: 1) heterochromia or 2) unilateral diffuse iris atrophy and stellate keratic precipitates. The misclassification rates for Fuchs' uveitis syndrome were 4.7% in the training set and 5.5% in the validation set, respectively. CONCLUSIONS: The criteria for Fuchs' uveitis syndrome had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.

Classification Criteria for Behçet Disease Uveitis.

PURPOSE: To determine classification criteria for Behçet disease uveitis. DESIGN: Machine learning of cases with Behçet disease and 5 other panuveitides. METHODS: Cases of panuveitides 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: One thousand twelve cases of panuveitides, including 194 cases of Behçet disease with uveitis, were evaluated by machine learning. The overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval 89.0, 96.8). Key criteria for Behçet disease uveitis were a diagnosis of Behçet disease using the International Study Group for Behçet Disease criteria and a compatible uveitis, including (1) anterior uveitis; (2) anterior chamber and vitreous inflammation; (3) posterior uveitis with retinal vasculitis and/or focal infiltrates; or (4) panuveitis with retinal vasculitis and/or focal infiltrates. The misclassification rates for Behçet disease uveitis were 0.6% in the training set and 0% in the validation set, respectively. CONCLUSIONS: The criteria for Behçet disease uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.

Classification Criteria for Tubercular Uveitis.

PURPOSE: To determine classification criteria for tubercular uveitis. DESIGN: Machine learning of cases with tubercular uveitis and 14 other uveitides. METHODS: Cases of noninfectious posterior uveitis or panuveitis, and of infectious posterior uveitis or panuveitis, 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 analyzed by anatomic class, and each class was 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 sets. RESULTS: Two hundred seventy-seven cases of tubercular uveitis were evaluated by machine learning against other uveitides. Key criteria for tubercular uveitis were a compatible uveitic syndrome, including (1) anterior uveitis with iris nodules, (2) serpiginous-like tubercular choroiditis, (3) choroidal nodule (tuberculoma), (4) occlusive retinal vasculitis, and (5) in hosts with evidence of active systemic tuberculosis, multifocal choroiditis; and evidence of tuberculosis, including histologically or microbiologically confirmed infection, positive interferon-γ release assay test, or positive tuberculin skin test. The overall accuracy of the diagnosis of tubercular uveitis vs other uveitides in the validation set was 98.2% (95% confidence interval 96.5, 99.1). The misclassification rates for tubercular uveitis were training set, 3.4%; and validation set, 3.6%. CONCLUSIONS: The criteria for tubercular uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.

Classification Criteria for Syphilitic Uveitis.

PURPOSE: To determine classification criteria for syphilitic uveitis. DESIGN: Machine learning of cases with syphilitic uveitis and 24 other uveitides. METHODS: Cases of anterior, intermediate, posterior, and panuveitides 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 analyzed by anatomic class, and each class was 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 different uveitic classes. The resulting criteria were evaluated on the validation set. RESULTS: Two hundred twenty-two cases of syphilitic uveitis were evaluated by machine learning, with cases evaluated against other uveitides in the relevant uveitic class. Key criteria for syphilitic uveitis included a compatible uveitic presentation (anterior uveitis; intermediate uveitis; or posterior or panuveitis with retinal, retinal pigment epithelial, or retinal vascular inflammation) and evidence of syphilis infection with a positive treponemal test. The Centers for Disease Control and Prevention reverse screening algorithm for syphilis testing is recommended. The misclassification rates for syphilitic uveitis in the training sets were as follows: anterior uveitides 0%, intermediate uveitides 6.0%, posterior uveitides 0%, panuveitides 0%, and infectious posterior/panuveitides 8.6%. The overall accuracy of the diagnosis of syphilitic uveitis in the validation set was 100% (99% confidence interval 99.5, 100)-that is, the validation set's misclassification rates were 0% for each uveitic class. CONCLUSIONS: The criteria for syphilitic uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.

Classification Criteria for Tubulointerstitial Nephritis With Uveitis Syndrome.

PURPOSE: To determine classification criteria for tubulointerstitial nephritis with uveitis (TINU). DESIGN: Machine learning of cases with TINU and 8 other anterior uveitides. METHODS: Cases of anterior 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 anterior uveitides. The resulting criteria were evaluated on the validation set. RESULTS: One thousand eighty-three cases of anterior uveitides, including 94 cases of TINU, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for TINU included anterior chamber inflammation and evidence of tubulointerstitial nephritis with either (1) a positive renal biopsy or (2) evidence of nephritis (elevated serum creatinine and/or abnormal urine analysis) and an elevated urine ß-2 microglobulin. The misclassification rates for TINU were 1.2% in the training set and 0% in the validation set. CONCLUSIONS: The criteria for TINU had a low misclassification rate and seemed to perform well enough for use in clinical and translational research.

Uveitis for the non-ophthalmologist.

The uveitides are a heterogeneous group of diseases characterized by inflammation inside the eye. The uveitides are classified as infectious or non-infectious. The non-infectious uveitides, which are presumed to be immune mediated, can be further divided into those that are associated with a known systemic disease and those that are eye limited,-ie, not associated with a systemic disease. The ophthalmologist identifies the specific uveitic entity by medical history, clinical examination, and ocular imaging, as well as supplemental laboratory testing, if indicated. Treatment of the infectious uveitides is tailored to the particular infectious organism and may include regional and/or systemic medication. First line treatment for non-infectious uveitides is corticosteroids that can be administered topically, as regional injections or surgical implants, or systemically. Systemic immunosuppressive therapy is used in patients with severe disease who cannot tolerate corticosteroids, require chronic corticosteroids at >7.5 mg/day prednisone, or in whom the disease is known to respond better to immunosuppression. Management of many of these diseases is optimized by coordination between the ophthalmologist and rheumatologist or internist.

Pediatric Uveitis and Scleritis in a Multi-Ethnic Asian Population.

Purpose: To describe the demographics and clinical characteristics of pediatric uveitis and scleritis at a tertiary eye care center.Methods: Clinical records of children with either uveitis or scleritis that presented between January 1989 and January 2016 were reviewed retrospectively.Results: Seventy-three patients were identified. Fifty-four had uveitis and 19 had scleritis. Posterior uveitis was the most common (27.8%), followed by intermediate uveitis (25.9%), panuveitis (25.9%) and anterior uveitis. Majority were noninfectious uveitis (37.0%), and 29.6% were idiopathic. Common associations were sarcoidosis (14.8%), HLA-B27 haplotype (9.3%) and toxoplasmosis (7.4%). Posterior scleritis was more common (94.7%) than anterior scleritis and majority were idiopathic (68.4%). Delayed patient presentation and presenting visual acuity worse than 0.3 LogMAR were associated with poor visual outcome (p = .03; OR = 0.17; 95% CI, 0.03-0.84 and p = .007; OR = 0.09; 95% CI, 0.02-0.52 respectively).Conclusion: Majority had noninfectious etiologies. Uveitis associated with juvenile idiopathic arthritis was rare.

Standardisation of optical coherence tomography angiography nomenclature in uveitis: first survey results.

AIM: To standardise the nomenclature for reporting optical coherence angiography (OCT-A) findings in the field of uveitis. METHODS: Members of the International Uveitis Study Group, of the American Uveitis Society and of the Sociedad Panamericana de Infermedades Oculares that choose to participate responded to an online questionnaire about their preferred terminology when reporting on OCT-A findings in uveitis. The response of individuals with several publications on OCT-A (experts) was compared with uveitis specialists (users) who have less than five publications on the field of uveitis and OCT-A. RESULTS: A total of 108 uveitis specialists who participated in the survey were included in the analysis. Of those, 23 were considered OCT-A 'experts'. There was an agreement in both groups for the definition of wide-field (WF)-OCT-A, and definition of neovascularisation in uveitis. Moreover, there was a difference in the responses in other areas, such as quantification of ischaemia, definition of 'large' areas of ischaemia or terms to describe decreased OCT-A signal from different causes. There was an unanimous need of 'users' and 'experts' to distinguish size of decreased OCT-A signal in uveitis, to implement a quantitative measurement of decreased flow specifically for WF-OCT-A and to use different terms for different causes of decreased OCT-A signal. CONCLUSIONS: While there was considerable agreement in the terminology used by all uveitis experts, significant differences in terminology were noted between 'users' and 'experts'. These differences indicate the need for standardisation of nomenclature among all uveitis specialists both for the purpose of reporting and in clinical use.

Clinical and laboratory characteristics of ocular syphilis and neurosyphilis among individuals with and without HIV infection.

BACKGROUND/AIMS: In the era of increasing incidence of syphilis globally, ocular syphilis is re-emerging as an important cause of uveitis. The aim of this study was to determine the clinical and laboratory characteristics of ocular- and neurosyphilis among individuals with and without HIV infection. METHODS: Retrospective analysis of patients diagnosed with ocular syphilis presenting to Tygerberg Hospital, South Africa, over a 5-year period ending December 2018. RESULTS: Two-hundred and fifteen eyes of 146 patients were included. HIV coinfection was present in 52.1% of the patients, with 23.7% of these patients being newly diagnosed on presentation. The median age was 36.5±9.8 years. Bilateral involvement occurred in 47.3%, with 68.1% of these patients being HIV positive. The most frequent form of intraocular inflammation was posterior uveitis (40.9%), followed by panuveitis (38.1%), both of which were more predominant in HIV-positive eyes. Seventy-four per cent of all eyes had a visual acuity ≤20/50 and 40% <20/200 at presentation. A lumbar puncture was performed in 113 patients (77.4%). Sixteen patients had confirmed neurosyphilis and 27 probable neurosyphilis according to the UpToDate algorithms. CONCLUSION: This study included the largest number of ocular syphilis cases with the largest proportion of HIV infection to date. Forty-three of 146 patients (37.0%) had neurosyphilis. HIV status must be determined in all patients with ocular syphilis since almost » of patients were newly diagnosed with HIV infection by doing so.

Findings and Graduation of Sarcoidosis-Related Uveitis: A Single-Center Study.

Ocular involvement is present in up to 79% of sarcoid patients. Uveitis is the main ocular manifestation and presents as a chronic intraocular inflammatory condition with potentially detrimental effects on visual acuity and quality of life. This retrospective study was conducted to explore the incidence and characteristics of ocular sarcoidosis in a single tertiary ophthalmology center. Medical records of 84 patients presenting between June 2007 and March 2021 were analyzed. Based on the "International Workshop on Ocular Sarcoidosis" (IWOS) criteria, ocular sarcoidosis was determined as: definite (n = 24; 28.6%), presumed (n = 33; 39.3%), probable (n = 10; 11.9%), and indefinite (n = 17; 20.2%) in our study population. In 43.9% of the definite and presumed cases, the eye was primarily affected. In addition to specific ocular findings, the diagnosis was supported by biopsy (28.6%) and chest x-ray or computer tomography (66.7%). Moreover, an increased soluble interleukin-2 receptor (sIL-2R) expression (76.2%), elevated angiotensin-converting enzyme (ACE) levels (34.8%), and lymphocytopenia (35.1%) were valuable laboratory findings. Co-affected organs were lungs (60.7%), skin (15.5%), and central nervous system (8.3%). Our findings support the prominent role of the eye in the early detection of sarcoidosis. In addition to the IWOS criteria, sIL-2R, in particular, was shown to be relevant in establishing the diagnosis.

Logistic Regression Classification of Primary Vitreoretinal Lymphoma versus Uveitis by Interleukin 6 and Interleukin 10 Levels.

PURPOSE: To assess the diagnostic performance and generalizability of logistic regression in classifying primary vitreoretinal lymphoma (PVRL) versus uveitis from intraocular cytokine levels in a single-center retrospective cohort, comparing a logistic regression model and previously published Interleukin Score for Intraocular Lymphoma Diagnosis (ISOLD) scores against the interleukin 10 (IL-10)-to-interleukin 6 (IL-6) ratio. DESIGN: Retrospective cohort study. PARTICIPANTS: Patient histories, pathology reports, and intraocular cytokine levels from 2339 patient entries in the National Eye Institute Histopathology Core database. METHODS: Patient diagnoses of PVRL versus uveitis and associated aqueous or vitreous IL-6 and IL-10 levels were collected retrospectively. From these data, cytokine levels were compared between diagnoses with the Mann-Whitney U test. A logistic regression model was trained to classify PVRL versus uveitis from aqueous and vitreous IL-6 and IL-10 samples and compared with ISOLD scores and IL-10-to-IL-6 ratios. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUC) for each classifier and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) at the optimal cutoff (maximal Youden index) for each classifier. RESULTS: Seventy-seven lymphoma patients (10 aqueous samples, 67 vitreous samples) and 84 uveitis patients (19 aqueous samples, 65 vitreous samples) treated between October 5, 1999, and September 16, 2015, were included. Interleukin 6 levels were higher and IL-10 levels were lower in uveitis patients compared with lymphoma patients (P < 0.01). For vitreous samples, the logistic regression model, ISOLD score, and IL-10-to-IL-6 ratio achieved AUCs of 98.3%, 97.7%, and 96.3%, respectively. Sensitivity, specificity, PPV, and NPV at the optimal cutoffs for each classifier were 94.2%, 96.9%, 97%, and 94% for the logistic regression model; 92.7%, 100%, 100%, and 92.9% for the ISOLD score; and 94.2%, 95.3%, 95.6%, and 93.9% for the IL-10-to-IL-6 ratio. All models achieved complete separation between uveitis and lymphoma in the aqueous data set. CONCLUSIONS: The accuracy of the logistic regression model and generalizability of the ISOLD score to an independent patient cohort suggest that intraocular cytokine analysis by logistic regression may be a promising adjunct to cytopathologic analysis, the gold standard, for the early diagnosis of primary vitreoretinal lymphoma. Further validation studies are merited.

Epidemiology of uveitis in urban Australia.

IMPORTANCE: Few prior studies have described the epidemiology of uveitis in the Australian population. BACKGROUND: To report the incidence and period prevalence of active uveitis in Melbourne and detail their subtypes and aetiologies. DESIGN: Cross-sectional study using retrospective medical record review in a tertiary hospital. PARTICIPANTS: Patients with a coded diagnosis of uveitis who attended the emergency department or specialist ocular immunology clinic at the Royal Victorian Eye and Ear Hospital between November 2014 through October 2015 (N = 1752). METHODS: Medical records were reviewed to confirm the date of diagnosis and subtype of uveitis. Incidence and prevalence rates were calculated utilizing estimates of the adult population residing in areas of greater Melbourne with more than 30 ocular-related presentations to the emergency department annually. MAIN OUTCOMES AND MEASURES: Presence and date of onset, anatomical distribution and aetiology of uveitis. RESULTS: During the study period, 734 new cases of uveitis and 502 cases of pre-existing uveitis requiring active treatment were confirmed. These figures yielded an incidence of 21.54 (CI 20.03, 23.15) per 100 000 person-years and a period prevalence of 36.27 (CI 34.30, 38.35) per 100 000 persons. The distribution of prevalent uveitis cases was anterior (75%), intermediate (6%), posterior (15%) and panuveitis (4%). An infectious aetiology accounted for 13.4% of cases, a systemic associated disease for 26.4% of cases, and no cause was identified in 60.2% of cases. CONCLUSION AND RELEVANCE: The incidence and prevalence rates of uveitis in urban Australia were lower than recent studies from the United States and Europe.

Changing biological disease modifying treatment for paediatric uveitis in the real world.

IMPORTANCE: Paediatric uveitis is a severe sight-threatening uveitis due to disease progression and treatment failure. Biological agents are a promising new treatment. This study provides real-world data on their use from Sydney, Australia. BACKGROUND: Traditionally corticosteroids and non-biological immunosuppressive agents were used to treat paediatric uveitis, often with poor outcomes. DESIGN: Retrospective, chart review over an 8-year period at a tertiary referral eye hospital. PARTICIPANTS: A total of 27 paediatric uveitis patients treated with biological agents. METHODS: Chart review of demographic data and treatment outcomes. MAIN OUTCOME MEASURES: Treatment efficacy (corticosteroid-sparing effect, topical steroid cessation/reduction, reduction in systemic-steroid sparing agents, change in intraocular inflammation, visual acuity and central macular thickness); treatment failure; and adverse events. Data were collected at biological initiation, 6 weeks, 6 months and 12 months. RESULTS: Biological therapy over 1 year was effective with prednisolone dose reduced to <5 mg/day in five of six patients (83%), number of systemic steroid-sparing agents was reduced to ≤1 in two of four patients (50%) and cessation of topical steroid achieved in 12/41 of eyes (29%). Improvement of anterior chamber cells by two grades occurred in 20/25 eyes (80%), improvement of logMAR to ≤0.3 occurred in 12/18 eyes (67%) and macular oedema decreased in 4/5 eyes (80%). Treatment failure occurred in six eyes (13.01%) and five patients (18.5%) developed an adverse reaction. CONCLUSIONS AND RELEVANCE: Biological therapy was effective in paediatric patients with uveitis. Intraocular inflammation improved with maintained visual acuity, systemic corticosteroid dose decreased and there was a low frequency of adverse events.