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Purpose: To determine classification criteria for varicella zoster virus (VZV) anterior uveitis., Design: Machine learning of cases with VZV anterior uveitis 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 123 cases of VZV anterior uveitis, 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 VZV anterior uveitis included unilateral anterior uveitis with either (1) positive aqueous humor polymerase chain reaction assay for VZV; (2) sectoral iris atrophy in a patient ≥60 years of age; or (3) concurrent or recent dermatomal herpes zoster. The misclassification rates for VZV anterior uveitis were 0.9% in the training set and 0% in the validation set, respectively., Conclusions: The criteria for VZV anterior uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: The purpose of this study was to determine classification criteria for herpes simplex virus (HSV) anterior uveitis DESIGN: Machine learning of cases with HSV anterior uveitis 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 in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated in the validation set., Results: A total of 1,083 cases of anterior uveitides, including 101 cases of HSV anterior uveitis, 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 HSV anterior uveitis included unilateral anterior uveitis with either 1) positive aqueous humor polymerase chain reaction assay for HSV; 2) sectoral iris atrophy in a patient ≤50 years old; or 3) HSV keratitis. The misclassification rates for HSV anterior uveitis were 8.3% in the training set and 17% in the validation set., Conclusions: The criteria for HSV anterior uveitis had a reasonably low misclassification rate and appeared to perform well enough for use in clinical and translational research., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: The purpose of this study was to determine classification criteria for spondyloarthritis/HLA-B27-associated anterior uveitis DESIGN: Machine learning of cases with spondyloarthritis/HLA-B27-associated anterior uveitis 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 in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated in the validation set., Results: A total of 1,083 cases of anterior uveitides, including 184 cases of spondyloarthritis/HLA-B27-associated anterior uveitis, 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% CI: 92.4-98.6). Key criteria for spondyloarthritis/HLA-B27-associated anterior uveitis included 1) acute or recurrent acute unilateral or unilateral alternating anterior uveitis with either spondyloarthritis or a positive test result for HLA-B27; or 2) chronic anterior uveitis with a history of the classic course and either spondyloarthritis or HLA-B27; or 3) anterior uveitis with both spondyloarthritis and HLA-B27. The misclassification rates for spondyloarthritis/HLA-B27-associated anterior uveitis were 0% in the training set and 3.6% in the validation set., Conclusions: The criteria for spondyloarthritis/HLA-B27-associated anterior uveitis had a low misclassification rate and appeared to perform well enough for use in clinical and translational research., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: To determine classification criteria for cytomegalovirus (CMV) anterior uveitis., Design: Machine learning of cases with CMV anterior uveitis 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 89 cases of CMV anterior uveitis, 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 CMV anterior uveitis included unilateral anterior uveitis with a positive aqueous humor polymerase chain reaction assay for CMV. No clinical features reliably diagnosed CMV anterior uveitis. The misclassification rates for CMV anterior uveitis were 1.3% in the training set and 0% in the validation set., Conclusions: The criteria for CMV anterior uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: To determine classification criteria for juvenile idiopathic arthritis (JIA)-associated chronic anterior uveitis (CAU)., Design: Machine learning of cases with JIA CAU 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 202 cases of JIA CAU, 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 JIA CAU included (1) chronic anterior uveitis (or, if newly diagnosed, insidious onset) and (2) JIA, except for the systemic, rheumatoid factor-positive polyarthritis, and enthesitis-related arthritis variants. The misclassification rates for JIA CAU were 2.4% in the training set and 0% in the validation set., Conclusions: The criteria for JIA CAU had a low misclassification rate and seemed to perform well enough for use in clinical and translational research., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Anterior uveitis is the most common form of uveitis. There are several known and many possible etiologies for anterior uveitis. After examining the posterior segment and ruling out masquerade syndromes, the main step of etiologic diagnosis is clinical characterization. It is essential to establish unilateral versus bilateral involvement and presence or absence of granulomatous features. Subsequently, a work-up may be obtained which then helps to confirm diagnostic hypotheses based on the detailed history and clinical examination. The priority is to rule out an infection, although less frequent, before starting steroid therapy, adapted to the severity of the clinical picture. Finally, biologics have greatly changed the management and prevention of some forms of anterior uveitis, in particular uveitis associated with HLA-B27 and juvenile idiopathic arthritis-associated anterior uveitis., (Copyright © 2017 Elsevier Masson SAS. All rights reserved.)

Anterior Uveitis is the most common form of uveitis. There are several known and many possible etiologies for anterior uveitis. After examining the posterior segment and ruling out masquerade syndromes, the main step of etiologic diagnosis is clinical characterization. It is essential to establish the presence or absence of unilateral versus bilateral and granulomatous features. Subsequently, a directed work-up may be obtained which then helps to confirm diagnostic hypotheses based on the detailed history and clinical examination. The priority is to rule out an infection. Treatments are adapted according to etiology and disease severity. Finally, biologics have greatly changed the management and prevention of some forms of anterior uveitis, in particular uveitis associated with HLA-B27 and juvenile idiopathic arthritis-associated anterior uveitis., (Copyright © 2017 Elsevier Masson SAS. All rights reserved.)

Purpose: To propose a classification system to grade semi-automated flare readings and assess its correlation with clinical flare grading and also to explore the utility of an additional step in clinical flare assessment between grades 0 and 1., Methods: Semi-automated flare readings from 103 eyes with uveitis were taken using the Kowa FM 700 laser flare meter and classified into two models (LFCM and LFCM&#95;1), and introduction of a 0.5 step in flare grading was explored., Results: Good correlation was present between the conventional SUN clinical flare and the proposed clinical classification for flare (weighted kappa (WK) = 89.64%, p < 0.001). Semi-automated flare grading (LFCM and LFCM&#95;1) had WK agreement of 82.52% and 79.85% (p < 0.001) with conventional SUN clinical flare grading., Conclusions: The proposed classification system for semi-automated laser flare readings (LFCM) allows stratification of measurements into grades analogous to clinical flare grades and correlates well with conventional clinical flare grading., (© 2015 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.)

Purpose: This study was designed to determine the feasibility of anterior segment optical coherence tomography (AS-OCT) to objectively image and quantify the degree of AC inflammation., Design: Prospective evaluation of a diagnostic test., Participants: Patients with anterior segment involving uveitis., Methods: Observational case series of patients with uveitis. Single-line and 3-dimensional (3D) volume AS-OCT scans were manually graded to evaluate for the presence or absence of cells in the AC. Clinical grading scores were correlated to the number of cells seen in each line scan. An automated algorithm was developed to measure the number of cells seen in the 3D volume scan and compared with manual measurements and clinical grading scores., Main Outcome Measures: Degree of anterior segment inflammation., Results: A total of 114 eyes from 76 patients were imaged, 83 eyes with line scans and 31 eyes with volume scans. The average number of cells on line scans was 0.13 for grade 0, 1.2 for grade 1/2+, 2.6 for grade 1+, 5.7 for grade 2+, 15.5 for grade 3+, and 41.2 for grade 4+. Spearman correlation coefficient comparing clinical grade with the individual AS-OCT line scans was 0.967 (P < 0.0001). The range of cells in the automated cell count of 3D volume scans was 13.60 to 1222; the range for manual cell counts was from 9.2 to 2245. The Spearman correlation coefficients were r = 0.7765 (P < 0.0001) and r = 0.7484 (P < 0.0001) comparing the manual and automated cell counts with the clinical grade, respectively. Spearman correlation coefficient comparing the automatic cell counts with manual cell count in the 3D volume scan was 0.997 (P < 0.0001)., Conclusions: Anterior segment OCT can be used to image and grade the degree of AC inflammation. Clinical grading strongly correlates with the number of cells on AS-OCT line scans and volume scans. The automated algorithm to measure cell count had a high correlation to manual measurement of cells in the 3D volume scans. This modality could be used to objectively grade response to treatment., (Copyright © 2015 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Purpose: To describe ocular manifestations in primary varicella infection and their relationship to systemic severity and the associated eyelid rash., Methods: One hundred consecutive children with primary varicella were examined prospectively. The cases were classified as mild, moderate, and severe according to the severity of clinical presentation. Excluding the presence of eyelid rash, children with ocular findings were assigned to group 1 (G1), and those without ocular findings were assigned to group 2 (G2). Patients in G1 were also evaluated according to the nature of ocular manifestations and the course of uveitis., Results: Twenty-one percent of patients had ocular involvement (G1) and 79% had no ocular involvement (G2). While chickenpox had a mild course in 85.7% of patients in G1 and 88.6% of patients in G2, all others had a moderate course. None of the children had a severe course. A varicella eyelid rash was present in 28.6% of patients in G1 and 13.9% in G2. Among ocular findings, 38.1% of patients had conjunctivitis, 57.1% had anterior uveitis, and 4.8% had disciform keratouveitis. There was no significant association between severity of chickenpox and severity of ocular involvement (p=0.712). There was also no relationship between eyelid rash and ocular involvement (p=0.787)., Conclusions: There is neither an association between the severity of chickenpox and the severity of ocular involvement nor an association between the presence of a varicella eyelid rash and the development of uveitis. As the prognosis regarding sequelae of ocular involvement in varicella infection is good, only those patients with ocular signs and symptoms need be referred by pediatricians for an ophthalmologic examination.

Purpose: To determine how a biomicroscope illumination system affects the grading of anterior chamber (AC) inflammation., Design: Laboratory investigation., Methods: An artificial AC was designed to replicate optically a human AC and was filled with 5-mum polystyrene beads suspended in ethanol. A high-definition video eyepiece camera recorded the moving beads. Using image processing software, the main outcomes measures determined were the average number of beads in a 1 x 1-mm field at varying widths of the slit-beam., Results: The volume of light and number of beads observed increased significantly as the slit-beam widened. Additionally, 3 separate biomicroscopes of identical make and model were found to produce different levels of luminance at the same aperture dial settings, influencing the number of beads observed, with the brighter biomicroscope yielding higher bead counts., Conclusions: Ability to count beads and perhaps the ability to count inflammatory cells in an inflamed eye depend on a number of factors, including the level of illumination and width of the slit-beam. This study demonstrated that the brighter the illumination and the wider the beam, the more beads were observed. This illustrates the importance of standardizing biomicroscopy, particularly where consecutive observations are used to make clinical decisions and in cases of multicenter clinical trials where clinical data are evaluated across different facilities.

Background: We investigated the characteristics and causes of various uveitis subtypes in patients presenting to the Regional Eye Centre at the Royal Alexandra Hospital, University of Alberta, Edmonton, Alta., and estimated the incidence of anterior uveitis in northern Alberta., Methods: A retrospective study was conducted of all patients presenting with uveitis to a single, full-time ophthalmologist at the Regional Eye Centre from September 2004 to June 2005. Uveitis was classified according to onset, severity, anatomical subtype, etiology, recurrence rate, and response to treatment. Statistical analysis was used to compare patients referred by ophthalmologists with those referred by non-ophthalmologists., Results: Two hundred and nine eyes of 171 patients were included in the study. Ophthalmologist referrals consisted of 67.4% anterior, 14.0% intermediate, and 18.6% panuveitis, and non-ophthalmological referrals were 92.8% anterior, 5.4% intermediate, and 1.8% panuveitis. Referrals from ophthalmologists were significantly more likely to be chronic, recurrent, and (or) less responsive to treatment than referrals from other sources., Interpretation: Referral bias strongly affects the proportions of uveitis subtypes seen. Human leukocyte antigen-B27-associated diseases (especially ankylosing spondylitis), sarcoidosis, and herpes infections should be considered among the most likely causes of uveitis to be diagnosed in this patient population.

Purpose: To extensively characterize the complex network of cytokines present in uveitis aqueous humor (AqH), and the relationships between cytokines and the cellular infiltrate., Methods: AqH from noninflammatory control subjects and patients with idiopathic, Fuchs' heterochromic cyclitis (FHC), and herpes-viral or Behçet's uveitis were analyzed for IL-1beta, -2, -4, -5, -7, -8, -10, -12, -13, -15, TNFalpha, IFNgamma, CCL2 (MCP-1), CCL5 (RANTES), CCL11 (Eotaxin), TGFbeta2, and CXCL12 (SDF-1), using multiplex bead immunoassays. The cellular infiltrate was also determined for each sample., Results: Idiopathic uveitis AqH, compared with noninflammatory controls, was characterized by high levels of IL-6, IL-8, CCL2 and IFNgamma, the levels of which correlated with each other. For IL-6 and IL-8 these levels were proportional to the number of neutrophils present. By contrast, the levels of both TGFbeta2 and CXCL12 decreased in idiopathic uveitis AqH with increasing inflammation. Cluster analysis showed a degree of segregation between noninflammatory and idiopathic uveitis AqH. Further examination using random forest analysis yielded a complete distinction between these two groups. The minimum cytokines required for this classification were IL-6, IL-8, CCL2, IL-13, TNFalpha, and IL-2., Conclusions: Application of multiplex bead immunoassays has allowed us to identify distinct patterns of cytokines that relate to both clinical disease and the cellular infiltrates present. Bioinformatics analysis allowed identification of cytokines that differentiate idiopathic uveitis from noninflammatory control AqH and are likely to be important for the pathogenesis of uveitis.

Purpose: To review the classification of herpes simplex virus (HSV) keratitis and anterior uveitis using the available clinical, pathologic, and laboratory models of disease., Methods: The literature was reviewed to establish prior classifications of herpes simplex keratitis and uveitis. The author introduces a classification attempting to encompass all patients with this disease process, taking into account the newer proposed mechanisms of disease., Results: A classification of HSV keratitis and uveitis is introduced based on the literature and the experience of the author in treating patients with this condition., Conclusions: There are multiple mechanisms of disease after ocular HSV infection, including damage from live virus, from immune and inflammatory mechanisms, and from structural damage in the aftermath of HSV infection (metaherpetic disease). Clinical, pathologic, and etiologic descriptions of these disease manifestations are offered. This may allow better communication between clinicians and authors of research protocols by defining the different aspects of this complex disease process.