Classification Criteria For Multiple Evanescent White Dot Syndrome

doi:10.1016/j.ajo.2021.03.050

American Journal of Ophthalmology

Volume 228, August 2021, Pages 198-204

https://doi.org/10.1016/j.ajo.2021.03.050 Get rights and content

Purpose

The purpose of this study was to determine classification criteria for multiple evanescent white dot syndrome (MEWDS).

Design

Machine learning of cases with MEWDS and 8 other posterior uveitides.

Methods

Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on 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 infectious posterior, or panuveitides. The resulting criteria were evaluated in the validation set.

Results

A total of 1,068 cases of posterior uveitides, including 51 cases of MEWDS, were evaluated by machine learning. Key criteria for MEWDS included: 1) multifocal gray-white chorioretinal spots with foveal granularity; 2) characteristic imaging on fluorescein angiography (“wreath-like” hyperfluorescent lesions) and/or optical coherence tomography (hyper-reflective lesions extending from retinal pigment epithelium through ellipsoid zone into the retinal outer nuclear layer); and 3) absent to mild anterior chamber and vitreous inflammation. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval: 94.3-99.3) in the validation set. Misclassification rates for MEWDS were 7% in the training set and 0% in the validation set.

Conclusions

The criteria for MEWDS had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Section snippets

METHODS

The SUN Developing Classification Criteria for the Uveitides project proceeded in 4 phases as previously described: 1) informatics, 2) case collection, 3) case selection, and 4) machine learning.15, 16, 17, 18

RESULTS

Ninety-five cases of MEWDS were collected, and 51 (54%) achieved supermajority agreement on the diagnosis during the “selection” phase and were used in the machine learning phase These cases of MEWDS were compared to cases of posterior uveitides, including 82 cases of APMPPE; 207 cases of BSCR; 122 cases of serpiginous choroiditis; 138 cases of MFCPU; 144 cases of PIC; 12 cases of sarcoid posterior uveitis; 35 cases of syphilitic posterior uveitis; and 277 cases of tubercular

DISCUSSION

The classification criteria developed by the SUN Working Group for MEWDS have an acceptable misclassification rate, indicating good discriminatory performance relative to other non-infectious posterior and panuveitides. Because the goal of the SUN criteria was classification at presentation and because MEWDS spontaneously resolves, these criteria were most appropriate for the early, active stage of the disease.²⁰

Unlike other diseases in this class, the primary lesion appears to be at the level

CRediT roles

Douglas A. Jabs, MD, MBA: Conceptualization, Methodology, Validation, Investigation, Data curation, Writing–Review and editing, Visualization, Supervision, Project administration, Funding acquisition. Antoine P. Brezin, MD: Investigation, Writing–Review and editing. Andrew D. Dick, MBBS, MD, FRCP, FRCS, FRCOphth: Investigation, Writing–Review and editing. Ralph D. Levinson, MD: Investigation, Writing–Review and editing. Lyndell L. Lim, MD: Investigation, Writing–Review and editing. Peter

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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.
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.
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.
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: Supplemental Material available at AJO.com.

¹: Members of the SUN Working Group are listed online at AJO.com.

²: Writing Committee: Douglas A. Jabs, Antoine P. Brezin, Andrew D. Dick, Ralph D. Levinson, Lyndell L. Lim, Peter McCluskey, Neal Oden, Alan G. Palestine, Jennifer E. Thorne, Brett E. Trusko, Albert Vitale, and Susan E. Wittenberg.

³: Writing Committee Affiliations: Members of the SUN Working Group are listed online at AJO.com. Department of Epidemiology (D.A.J., J.E.T.), the Johns Hopkins University Bloomberg School of Public Health, and the Wilmer Eye Institute, Department of Ophthalmology (D.A.J., J.E.T.), Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Department of Ophthalmology (A.P.B.), University of Paris V-Hôpital Cochin, Paris, France; Academic Unit of Ophthalmology (A.D.D.), Bristol Medical School, University of Bristol, Bristol, UK; National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital (A.D.D.), London, UK; University College London Institute of Ophthalmology (A.D.D.), London UK; Stein Eye Institute and Department of Ophthalmology (R.D.L.), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA; Center for Eye Research Australia (L.L.L.), Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia; Save Sight Institute (P.M.), Department of Ophthalmology, University of Sydney School of Medicine, Sydney, New South Wales, Australia; Emmes Co, LLC, (N.O.) Rockville, Maryland, USA; Department of Ophthalmology (A.G.P.), University of Colorado School of Medicine, Aurora, Colorado, USA; Department of Medicine (B.E.T.), Texas A&M University, College Station, Texas, USA; Department of Ophthalmology (A.V.), University of Utah School of Medicine, Salt Lake City, Utah, USA; and the Houston Eye Associates (S.E.W.), Houston, Texas, USA.

⁴: Financial Disclosures: Douglas A. Jabs: none. Antoine Brezin: none. Andrew Dick: none. Ralph Levinson: none. Lyndell L. Lim: none. Peter McCluskey: none. Neal Oden: none. Alan G. Palestine: none. Jennifer E. Thorne: Dr. Thorne engaged in a portion of this research as a consultant and was compensated for the consulting service; Brett E. Trusko: none. Albert Vitale: none. Susan E. Wittenberg: none.

^⁎: Inquiries to: Douglas A. Jabs, Department of Epidemiology, the Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 20215 USA. E-mail: [email protected].

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Original articleClassification Criteria For Multiple Evanescent White Dot Syndrome

Purpose

Design

Methods

Results

Conclusions

Section snippets

METHODS

RESULTS

DISCUSSION

CRediT roles

Am J Ophthalmol

J Curr Ophthalmol

Am J Ophthalmol

Am J Ophthalmol

Multiple evanescent white dot syndrome. I. Clinical findings

Arch Ophthalmol

Multiple evanescent white dot syndrome

Arch Ophthalmol

Features and function of multiple evanescent white dot syndrome

Arch Ophthalmol

Multiple evanescent white dot syndrome (MEWDS)

Intern Ophthalmol Clin

High-resolution multimodal imaging of multiple evanescent white dot syndrome

Ophthalmic Surg Lasers Imaging Retina

Expanded clinical spectrum of multiple evanescent white doe syndrome with multimodal imaging

Retina

White dot syndromes: a 20-year study of incidence, clinical features, and outcomes

Ocular Immunol Inflamm

Microstructural abnormalities in MEWDS demonstrated by ultrahigh resolution optical coherence tomography

Retina

Fundus autofluorescence and multiple evanescent white dot syndrome

Retina

Original article
Classification Criteria For Multiple Evanescent White Dot Syndrome