Classification Criteria for Juvenile Idiopathic Arthritis-Associated Chronic Anterior Uveitis

Interferon-γ Release Assay in the Assessment of Cellular Immunity-A Single-Centre Experience with mRNA SARS-CoV-2 Vaccine in Patients with Juvenile Idiopathic Arthritis

Background: As the SARS-CoV-2 virus remains one of the main causes of severe respiratory system infections, the Food and Drug Administration strongly advises the continuation of current vaccination programs, including the distribution of updated boosters, especially in high-risk groups of patients. Therefore, there is an unceasing need for further research on the safety and, no less importantly, the clinical effectivity of the vaccines, with an extra focus on cohorts of patients with underlying health problems. This study aimed to assess the efficacy of the SARS-CoV-2 vaccine in possibly immunocompromised children with rheumatic disease while utilizing the interferon-gamma release assay (IGRA) as a marker for COVID-19 immunity in the study follow-up. Methods: This prospective study was performed in a group of 55 pediatric patients diagnosed with juvenile idiopathic arthritis. Eight participants were immunized with the Comirnaty mRNA vaccine before the research commenced, while the rest of the group (n = 47) had not been vaccinated against SARS-CoV-2. At the study baseline, the cellular response to the virus antigen was measured using a specific quantitative IGRA in whole blood; subsequently, the anti-SARS-CoV-2 test was performed, marking the antibodies' levels in serum. Around four months after the enrollment of the last patient in the study, a follow-up survey regarding the events of COVID-19 infection within the cohort was conducted. Results: The study confirmed that all the vaccinated children developed specific T-cell (p = 0.0016) and humoral (p = 0.001 for IgA antibodies, p = 0.008 for IgG antibodies) responses to the inoculation, including those receiving biological treatment and those on conventional disease-modifying anti-rheumatic drugs. The study also showed the different patterns of immunity elicited both after infection and post-vaccination, with higher levels of antibodies and T-cell response after inoculation than after natural exposure to the pathogen. According to the follow-up survey, six children developed PCR-confirmed SARS-CoV-2 infection, whereas the additional 10 patients admitted to having COVID-like symptoms with no laboratory verification. Conclusions: SARS-CoV-2 vaccinations elicit valid immune responses in pediatric rheumatic patients. Including the assessment of T-cell immunity in the evaluation of inoculation-induced immunization can enhance the accuracy of sole humoral response assays.

A Prospective Study of Clinical Features of Anterior Uveitis in Taiwan

In this study, we reported the patterns, epidemiology, and clinical features of anterior uveitis (AU) in Taiwan, an area of Eastern Asia. This prospective, cross-sectional case series study was performed to identify patients with AU at two tertiary medical centers (Kaohsiung Chang Gung Memorial Hospital and Kaohsiung Veterans General Hospital) located at the southern Taiwan between December 1, 2018, and March 31, 2020. The clinical diagnoses, ocular presentations, and laboratory data, including the results of the aqueous polymerase chain reaction tests, were investigated in these patients. A total of 112 patients, with a mean age of 48.9 years, were included. Most patients (87.5%) presented with unilateral eye disease, with 30 cases of ocular hypertension at the first presentation (27%). The most common clinical diagnoses were idiopathic AU (37.5%), human leukocyte antigen (HLA)-B27-associated acute AU (25.0%), and herpetic AU (18.8%). Among patients with herpetic AU, cytomegalovirus (CMV) was the most common pathogen (17/21, 81%). Compared to HLA-B27-associated acute AU, CMV-related AU was mostly observed in patients that were older in age, exhibited higher intraocular pressure, more keratic precipitates, greater iris atrophy, and more pseudophakia, but was least reported in those with posterior synechiae. This prospective study identified the pattern and clinical features of AU in southern Taiwan.

The role of a multicentre data repository in ocular inflammation: The Ocular Autoimmune Systemic Inflammatory Infectious Study (OASIS)

In the current literature, clinical registry cohorts related to ocular inflammation are few and far between, and there are none involving multi-continental international data. Many existing registries comprise administrative databases, data related to specific uveitic diseases, or are designed to address a particular clinical problem. The existing data, although useful and serving their intended purposes, are segmented and may not be sufficiently robust to design prognostication tools or draw epidemiological conclusions in the field of uveitis and ocular inflammation. To solve this, we have developed the Ocular Autoimmune Systemic Inflammatory Infectious Study (OASIS) Clinical Registry. OASIS collects prospective and retrospective data on patients with all types of ocular inflammatory conditions from centers all around the world. It is a primarily web-based platform with alternative offline modes of access. A comprehensive set of clinical data ranging from demographics, past medical history, clinical presentation, working diagnosis to visual outcomes are collected over a range of time points. Additionally, clinical images such as optical coherence tomography, fundus fluorescein angiography and indocyanine green angiography studies may be uploaded. Through the capturing of diverse, well-structured, and clinically meaningful data in a simplified and consistent fashion, OASIS will deliver a comprehensive and well organized data set ripe for data analysis. The applications of the registry are numerous, and include performing epidemiological analysis, monitoring drug side effects, and studying treatment safety efficacy. Furthermore, the data compiled in OASIS will be used to develop new classification and diagnostic systems, as well as treatment and prognostication guidelines for uveitis.

Clinical Features and Risk Factors of Uveitis in Korean Children with Juvenile Idiopathic Arthritis: A Retrospective Cohort Study

This study aimed to investigate the clinical features and risk factors of uveitis in Korean children with juvenile idiopathic arthritis (JIA). The medical records of JIA patients diagnosed between 2006 and 2019 and followed up for ≥1 year were retrospectively reviewed, and various factors including laboratory findings were analyzed for the risk of developing uveitis. JIA-associated uveitis (JIA-U) developed in 30 (9.8%) of 306 JIA patients. The mean age at the first uveitis development was 12.4 ± 5.7 years, which was 5.6 ± 3.7 years after the JIA diagnosis. The common JIA subtypes in the uveitis group were oligoarthritis-persistent (33.3%) and enthesitis-related arthritis (30.0%). The uveitis group had more baseline knee joint involvement (76.7% vs. 51.4%), which increased the risk of JIA-U during follow-up (p = 0.008). Patients with the oligoarthritis-persistent subtype developed JIA-U more frequently than those without it (20.0% vs. 7.8%; p = 0.016). The final visual acuity of JIA-U was tolerable (0.041 ± 0.103 logMAR). In Korean children with JIA, JIA-U may be associated with the oligoarthritis-persistent subtype and knee joint involvement.

Artificial intelligence in uveitis: A comprehensive review

Uveitis is a disease complex characterized by intraocular inflammation of the uvea that is an important cause of blindness and social morbidity. With the dawn of artificial intelligence (AI) and machine learning integration in healthcare, their application in uveitis creates an avenue to improve screening and diagnosis. Our review identified the use of artificial intelligence in studies of uveitis and classified them as diagnosis support, finding detection, screening, and standardization of uveitis nomenclature. The overall performance of models is poor, with limited datasets and a lack of validation studies and publicly available data and codes. We conclude that AI holds great promise to assist with the diagnosis and detection of ocular findings of uveitis, but further studies and large representative datasets are needed to guarantee generalizability and fairness.

Tumor necrosis factor (TNF) inhibitors for juvenile idiopathic arthritis-associated uveitis

BACKGROUND

Uveitis is the most common extra-articular manifestation of juvenile idiopathic arthritis (JIA) and a potentially sight-threatening condition characterized by intraocular inflammation. Current treatment for JIA-associated uveitis (JIA-U) is largely based on physician experience, observational evidence and consensus guidelines, resulting in considerable variations in practice.  OBJECTIVES: To evaluate the effectiveness and safety of tumor necrosis factor (TNF) inhibitors used for treatment of JIA-U.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL); Ovid MEDLINE; Embase.com; PubMed; Latin American and Caribbean Health Sciences Literature Database (LILACS); ClinicalTrials.gov, and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP). We last searched the electronic databases on 3 February 2022.

SELECTION CRITERIA

We included randomized controlled trials (RCTs) comparing TNF inhibitors with placebo in participants with a diagnosis of JIA and uveitis who were aged 2 to 18 years old.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methodology and graded the certainty of the body of evidence for seven outcomes using the GRADE classification.

MAIN RESULTS

We included three RCTs with 134 participants. One study conducted in the USA randomized participants to etanercept or placebo (N = 12). Two studies, one conducted in the UK (N = 90) and one in France (N = 32), randomized participants to adalimumab or placebo. All studies were at low risk of bias. Initial pooled estimates suggested that TNF-inhibitors may result in little to no difference on treatment success defined as 0 to trace cells on Standardization of Uveitis Nomenclature (SUN)-grading; or two-step decrease in activity based on SUN grading (estimated risk ratio (RR) 0.66; 95% confidence interval (CI) 0.21 to 2.10; 2 studies; 43 participants; low-certainty evidence) or treatment failure defined as a two-step increase in activity based on SUN grading (RR 0.31; 95% CI 0.01 to 7.15; 1 study; 31 participants; low-certainty evidence). Further analysis using the individual trial definitions of treatment response and failure suggested a positive treatment effect of TNF inhibitors; a RR of treatment success of 2.60 (95% CI 1.30 to 5.20; 3 studies; 124 participants; low-certainty evidence), and RR of treatment failure of 0.23 (95% CI 0.11 to 0.50; 3 studies; 133 participants). Almost all the evidence was on adalimumab and the evidence on etanercept was very limited.  For secondary outcomes, one study suggests that adalimumab may have little to no effect on risk of recurrence after induction of remission at three months (RR 2.50, 95% CI 0.31 to 20.45; 90 participants; very low-certainty evidence) and visual acuity, but the evidence is very uncertain; mean difference in longitudinal logMAR score change over six months was -0.01 (95% CI -0.06 to 0.03) and -0.02 (95% CI -0.07 to 0.03) using the best and worst logMAR measurement, respectively (low-certainty evidence). Low-certainty evidence from one study suggested that adalimumab treatment results in reduction of topical steroid doses at six months (hazard ratio 3.58; 95% CI 1.24 to 10.32; 74 participants who took one or more topical steroid per day at baseline). Adverse events, including injection site reactions and infections, were more common in the TNF inhibitor group. Serious adverse events were uncommon.

AUTHORS' CONCLUSIONS

Adalimumab appears to increase the likelihood of treatment success and decrease the likelihood of treatment failure when compared with placebo. The evidence was less conclusive about a positive treatment effect with etanercept. Adverse events from JIA-U trials are in keeping with the known side effect profile of TNF inhibitors. Standard validated JIA-U outcome measures are required to homogenize assessment and to allow for comparison and analysis of multiple datasets.

The standardisation of uveitis nomenclature (SUN) project

The uveitides are a collection of over 30 diseases characterised by intraocular inflammation. Previous work demonstrated that the agreement among uveitis experts on diagnosis was modest at best with some pairs of experts having chance alone agreement on selected diseases. The Standardisation of Uveitis Nomenclature (SUN) is a17-year collaboration among experts in uveitis, ocular image grading, informatics, and machine learning to improve clinical and translational uveitis research. The SUN "Developing Classification Criteria for the Uveitides" project used a rigorous, multi-phase approach to develop classification criteria for 25 of the most common uveitic diseases. The project's phases were: (1) informatics; (2) case collection; (3) case selection; (4) machine learning; and (5) consensus review and publication. The results were classification criteria with a high degree of accuracy (93.3%-99.3% depending on anatomic class of the uveitis), the goal of which is to form the basis for future uveitis research.

Evaluation of the SUN Classification Criteria for Uveitides in an Academic Uveitis Practice

PURPOSE

To evaluate the new Standardization of Uveitis Nomenclature (SUN) classification criteria for uveitides by applying them to patients in an academic uveitis practice.

DESIGN

Evaluation of classification criteria.

METHODS

The charts of all patients attending the uveitis service at the University of Colorado Hospital between January 1, 2013, and December 31, 2020, were reviewed. Patients with scleritis, ocular cicatricial pemphigoid, and peripheral ulcerative keratitis were excluded. We attempted to classify each patient's uveitis using the SUN classification criteria. Classification attempts were made within the relevant anatomical or infectious categories for their pathology but did not necessarily have to match their clinical diagnosis by a uveitis specialist. We recorded whether classification was possible as well as their clinical diagnosis by a uveitis specialist.

RESULTS

All patients attending the uveitis clinic at our academic institution between January 1, 2013, and December 31, 2020, were reviewed. Of the 1143 patients with uveitis, 572 (50.0%) had a disease that was not listed in the SUN classification system, and so no attempt to classify these patients was possible. Of the remaining 571 patients, 522 (91.4%) were able to be classified by SUN and in 492 (94.3%) of the 522 cases, their SUN classification matched their clinical diagnosis by a uveitis specialist.

CONCLUSIONS

Half of the patients at an academic uveitis practice had a disease for which no SUN classification criteria existed. In cases where classification by SUN could be attempted, the system performed well and generally agreed with their clinical diagnosis.

Artificial Intelligence and Imaging Processing in Optical Coherence Tomography and Digital Images in Uveitis

INTRODUCTION

Computer vision, understood as the area of science that trains computers to interpret digital images through both artificial intelligence (AI) and classical algorithms, has significantly advanced the analysis and interpretation of optical coherence tomography (OCT) in retina research. The aim of this review is to summarise the recent advances of computer vision in imaging processing in uveitis, with a particular focus in optical coherence tomography images.

MATERIAL AND METHODS

Literature review.

RESULTS

The development of computer vision to assist uveitis diagnosis and prognosis is still undergoing, but important efforts have been made in the field.

CONCLUSION

The automatising of image processing in uveitis could be fundamental to establish objective and standardised outcomes for future clinical trials. In addition, it could help to better understand the disease and its progression.

[Development of classification criteria for uveitis by the standardization of uveitis nomenclature (SUN) working group]

Hintergrund

Die Standardization of Uveitis Nomenclature (SUN) Working Group ist eine internationale Expertenkommission, die das Ziel verfolgt, eine standardisierte und international anerkannte Terminologie für das Gebiet der Uveitis zu erarbeiten. Dies erscheint angesichts der Forderung nach evidenzbasierter Medizin gerade bei relativ seltenen Erkrankungen wie der Uveitis wichtig.

Methoden

Unter Verwendung formaler Konsensustechniken wurde eine Datenbank von > 4000 Uveitispatienten erstellt, bei denen zuvor eine Mehrheitsübereinstimmung in der Diagnose erzielt wurde. Die Patientendaten wurden innerhalb der Uveitissubklasse analysiert und in einen Trainingssatz und einen Validierungssatz aufgeteilt. Mit maschinellem Lernen (ML) wurden multinomiale logistische Regressionen mit Lasso-Regularisierung auf dem Trainingssatz durchgeführt. Die Genauigkeit der Regeln, die entwickelt wurden, um die Kriterien des maschinellen Lernens auszudrücken, wurde von einem maskierten Beobachter in einer 10 %igen Zufallsstichprobe bewertet.

Ergebnisse

Die Schätzungen der Gesamtgenauigkeit nach Uveitisklassen im Validierungsset waren für alle Uveitisformen hoch: anteriore Uveitiden 96,7 % (95 %-Konfidenzintervall [CI] 92,4–98,6); intermediäre Uveitiden 99,3 % (95 %-CI 96,1–99,9); posteriore Uveitiden 98,0 % (95 %-CI 94,3–99,3); Panuveitiden 94,0 % (95 %-CI 89,0–96,8) und infektiöse posteriore Uveitiden/Panuveitiden 93,3 % (95 %-CI 89,1–96,3).

Schlussfolgerungen

Es werden Klassifikationskriterien präsentiert, die einen hohen Grad an Genauigkeit (geringe Fehlklassifikationsraten) aufweisen und sich daher gut für die künftige klinische und translationale Forschung eignen.