Ten-year follow-up of infliximab treatment for uveitis in Behçet disease patients: A multicenter retrospective study

PurposeTo evaluate 10-year outcome of infliximab (IFX) treatment for uveitis in Behçet disease (BD) patients using a standardized follow-up protocol.DesignRetrospective longitudinal cohort study.Participants140 BD uveitis patients treated with IFX enrolled in our previous study.MethodsMedical records were reviewed for demographic information, duration of IFX treatment, number of ocular attacks before IFX initiation, best corrected visual acuity (VA) at baseline and 1, 2, 3, 4, 5, and 10 years after IFX initiation, uveitis recurrence after IFX initiation and main anatomical site, concomitant therapies, and adverse events (AEs).Main outcome measures10-year IFX continuation rate and change in LogMAR VA.ResultsOf 140 BD patients, 106 (75.7%) continued IFX treatment for 10 years. LogMAR VA improved gradually after initiation of IFX, and the improvement reached statistical significance from 2 years of treatment. Thereafter, significant improvement compared with baseline was maintained until 10 years, despite a slight deterioration of logMAR VA from 5 years. However, eyes with worse baseline decimal VA < 0.1 showed no significant improvement from baseline to 10 years. Uveitis recurred after IFX initiation in 50 patients (recurrence group) and did not recur in 56 (non-recurrence group). Ocular attacks/year before IFX initiation was significantly higher in the recurrence group (2.82 ± 3.81) than in the non-recurrence group (1.84 ± 1.78). In the recurrence group, uveitis recurred within 1 year in 58% and within 2 years in 74%. Seventeen patients (34%) had recurrent anterior uveitis, 17 (34%) had posterior uveitis, and 16 (32%) had panuveitis, with no significant difference in VA outcome. In addition, logMAR VA at 10 years did not differ between the recurrence and non-recurrence groups. AEs occurred among 43 patients (30.7%), and 24 (17.1%) resulted in IFX discontinuation before 10 years.ConclusionsAmong BD patients with uveitis who initiated IFX, approximately 75% continued treatment for 10 years, and their VA improved significantly and was maintained for 10 years. Uveitis recurred in one-half of the patients, but visual acuity did not differ significantly from the patients without recurrence

Quantitative Analysis of Retinal Vascular Leakage in Retinal Vasculitis Using Machine Learning

Retinal vascular leakage is known to be an important biomarker to monitor the disease activity of uveitis. Although fluorescein angiography (FA) is a gold standard for the diagnosis and assessment of the disease activity of uveitis, the evaluation of FA findings, especially retinal vascular leakage, remains subjective and descriptive. In the current study, we developed an automatic segmentation model using a deep learning system, U-Net, and subtraction of the retinal vessel area between early-phase and late-phase FA images for the detection of the retinal vascular leakage area in ultrawide field (UWF) FA images in three patients with Behçet’s Disease and three patients with idiopathic uveitis with retinal vasculitis. This study demonstrated that the automated model for segmentation of the retinal vascular leakage area through the UWF FA images reached 0.434 (precision), 0.529 (recall), and 0.467 (Dice coefficient) without using UWF FA images for training. There was a significant positive correlation between the automated segmented area (pixels) of retinal vascular leakage and the FA vascular leakage score. The mean pixels of automatic segmented vascular leakage in UWF FA images with treatment was significantly reduced compared with before treatment. The automated segmentation of retinal vascular leakage in UWF FA images may be useful for objective and quantitative assessment of disease activity in posterior segment uveitis. Further studies at a larger scale are warranted to improve the performance of this automatic segmentation model to detect retinal vascular leakage

Evaluation of disease activity in uveoretinitis associated with Behçet’s disease

Behçet’s disease is a multi-organ inflammatory disorder with systemic vasculitis of unknown etiology. Ocular lesions occur in about 70% of patients with Behçet’s disease, and it is more frequent and severe in men. The frequency of ocular inflammatory attacks has been used as a main outcome measure to assess the efficacy of therapy on uveoretinitis in patients with Behçet’s disease. The ocular Behçet’s disease research group of Japan have recently proposed a new scoring system, Behçet’s disease ocular attack score 24 (BOS24), to assess the disease activity of ocular Behçet’s disease. This review highlights the efficacy and application of the BOS24 scoring system in clinical practice for patients with ocular Behçet’s disease. In addition, a new semi-quantitative scoring system to evaluate the degree of retinal vascular leakage on fluorescein angiography reported by our group is described

Immune Privilege and Eye-Derived T-Regulatory Cells

Certain cellular components of the eye, such as neural retina, are unable to regenerate and replicate after destructive inflammation. Ocular immune privilege provides the eye with immune protection against intraocular inflammation in order to minimize the risk to vision integrity. The eye and immune system use strategies to maintain the ocular immune privilege by regulating the innate and adaptive immune response, which includes immunological ignorance, peripheral tolerance to eye-derived antigens, and intraocular immunosuppressive microenvironment. In this review, we summarize current knowledge regarding the molecular mechanism responsible for the development and maintenance of ocular immune privilege via regulatory T cells (Tregs), which are generated by the anterior chamber-associated immune deviation (ACAID), and ocular resident cells including corneal endothelial (CE) cells, ocular pigment epithelial (PE) cells, and aqueous humor. Furthermore, we examined the therapeutic potential of Tregs generated by RPE cells that express transforming growth factor beta (TGF-β), cytotoxic T lymphocyte-associated antigen-2 alpha (CTLA-2α), and retinoic acid for autoimmune uveoretinitis and evaluated a new strategy using human RPE-induced Tregs for clinical application in inflammatory ocular disease. We believe that a better understanding of the ocular immune privilege associated with Tregs might offer a new approach with regard to therapeutic interventions for ocular autoimmunity

Behçet's disease: global epidemiology of an Old Silk Road disease

Although much is yet to be discovered about Behçet's disease, genetic, environmental and infectious factors are thought to play a role in its developmen

Quantitative Analysis of Retinal Vascular Leakage in Retinal Vasculitis Using Machine Learning

Retinal vascular leakage is known to be an important biomarker to monitor the disease activity of uveitis. Although fluorescein angiography (FA) is a gold standard for the diagnosis and assessment of the disease activity of uveitis, the evaluation of FA findings, especially retinal vascular leakage, remains subjective and descriptive. In the current study, we developed an automatic segmentation model using a deep learning system, U-Net, and subtraction of the retinal vessel area between early-phase and late-phase FA images for the detection of the retinal vascular leakage area in ultrawide field (UWF) FA images in three patients with Behçet’s Disease and three patients with idiopathic uveitis with retinal vasculitis. This study demonstrated that the automated model for segmentation of the retinal vascular leakage area through the UWF FA images reached 0.434 (precision), 0.529 (recall), and 0.467 (Dice coefficient) without using UWF FA images for training. There was a significant positive correlation between the automated segmented area (pixels) of retinal vascular leakage and the FA vascular leakage score. The mean pixels of automatic segmented vascular leakage in UWF FA images with treatment was significantly reduced compared with before treatment. The automated segmentation of retinal vascular leakage in UWF FA images may be useful for objective and quantitative assessment of disease activity in posterior segment uveitis. Further studies at a larger scale are warranted to improve the performance of this automatic segmentation model to detect retinal vascular leakage