Classification Criteria for Acute Posterior Multifocal Placoid Pigment Epitheliopathy

Acute posterior multifocal placoid pigment epitheliopathy: clinical presentation and risk of stroke and transient ischaemic attack

BACKGROUND/AIMS

Acute posterior multifocal placoid pigment epitheliopathy is a rare but important disease that can be associated with life-threatening complications due to cerebral vasculitis. The primary objective was to determine the incidence of neurological complications and risk factors for stroke and transient ischaemic attack (TIA) associated with acute posterior multifocal placoid pigment epitheliopathy. Secondary objectives included the clinical presentation, visual outcomes and recurrence rates.

METHODS

This was a multicentre retrospective case series including 111 eyes from 60 subjects presenting from January 2009 to June 2020.

RESULTS

Median age at presentation was 29 years (IQR 24.7-35.1) and 36 subjects (60.0%) were male. 20 subjects (33.3%) reported a viral prodrome. Stroke and TIA were observed in seven subjects (11.7%). Older age was the only significant risk factor for stroke/TIA (p=0.042). Vision loss occurred in seven eyes, with four eyes (3.6%) having final visual acuity 6/15-6/60 and three eyes (2.7%) having visual acuity of 6/60 or worse. Recurrence occurred in 10 subjects (16.7%).

CONCLUSIONS

The presence of headache cannot reliably predict those at risk of stroke/TIA. Individuals presenting with acute posterior multifocal pigment epitheliopathy should therefore undergo a clinical neurological review and work-up for cerebral vasculitis as deemed appropriate by the treating ophthalmologist and collaborating neurologist.

Fundus autofluorescence in uveitis: from pathogenesis to imaging interpretation

PURPOSE

This review aims to summarize the current fundus autofluorescence (FAF) ailment for diagnosis and follow-up of uveitis.

METHODS

A thorough literature search was performed in the PubMed database.

RESULTS

FAF maps the retinal pigment epithelium's (RPE) health. Therefore, several posterior infectious and non. This fast, easy-to-perform, noninvasive technique can detect and manage infectious uveitis.

CONCLUSIONS

FAF serves to understand pathophysiologic mechanisms of uveitis and is a valuable prognostic indicator of themselves.

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.

[Acute posterior multifocal placoid pigment epitheliopathy, serpiginous choroiditis and related diseases]

Acute Posterior Multifocal Placoid Pigment Epitheliopathy (APMPPE) and serpiginous choroiditis are two diseases classified as "white spot syndromes." Both are inflammatory/autoimmune diseases with suspected primary involvement of the choriocapillaris. The former usually has an excellent prognosis, while the latter can rapidly induce legal blindness. Whereas these diseases are well defined and well known, other entities (such as persistent placoid maculopathy or ampiginous choroiditis) with features of both APMPPE and serpiginous choroiditis have been described more recently. This review aims to describe demographic characteristics and multimodal imaging features to help differentiate between these four diseases.

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.

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.

Acute posterior multifocal placoid pigment epitheliopathy associated with serous retinal detachment: A case report

We report the occurrence of serous retinal detachment (SRD) in acute posterior multifocal placoid pigment epitheliopathy (APMPPE).

A 22-year-old man with no general or ophthalmological pathological history presented with an acute and bilateral decreased visual acuity. There was no notion of recent flu or recent vaccination. There were anterior chamber cells and vitreous cells. Fundus revealed white-yellowish lesions, scattered on posterior pole and periphery, associated with SRD in both eyes. Fluorescein Angiography showed early hypofluorescence followed by late hyperfuorescence. Optical coherence tomography (OCT) showed hyperreflective bands of the outer nuclear layer and interruption of the ellipsoid zone associated with bilateral SRD. Repeat OCT revealed a spontaneous and complete regression of SRD in both eyes, and improvement of visual acuity after one week of evolution without any treatment.

Serous retinal detachment is an uncommon manifestation of APMPPE. It is more suggestive of Vogt-Koyanagi-Harada disease (VKH), although angiographic features are typically observed in APMPPE. Moreover, spontaneous decrease of SRD within a few days is more in favor of APMPPE.

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Report the occurrence of serous retinal detachment in acute posterior multifocal placoid pigment epitheliopathy.

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Enhance the difference between Vogt-Koyanagi-Harada disease and acute posterior multifocal placoid pigment epitheliopathy complicated by serous retinal detachment.

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Report the spontaneous regression of the serous retinal detachment several days after the beginning of the symptomatology without any treatment.

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.