An Algorithm for the Diagnosis of Behçet Disease Uveitis in Adults

Ilknur Tugal-Tutkun, Sumru Onal, Miles Stanford, Mehmet Akman, Jos W R Twisk, Maarten Boers, Merih Oray, Pinar Çakar Özdal, Sibel Kadayifcilar, Radgonde Amer, Sivakumar R Rathinam, Rajesh Vedhanayaki, Moncef Khairallah, Yonca Akova, Fatime Nilüfer Yalcindag, Esra Kardes, Berna Basarir, Çigdem Altan, Yilmaz Özyazgan, Ahmet Gül

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Purpose: To develop an algorithm for the diagnosis of Behçet's disease (BD) uveitis based on ocular findings.Methods: Following an initial survey among uveitis experts, we collected multi-center retrospective data on 211 patients with BD uveitis and 207 patients with other uveitides, and identified ocular findings with a high diagnostic odds ratio (DOR). Subsequently, we collected multi-center prospective data on 127 patients with BD uveitis and 322 controls and developed a diagnostic algorithm using Classification and Regression Tree (CART) analysis and expert opinion.Results: We identified 10 items with DOR >5. The items that provided the highest accuracy in CART analysis included superficial retinal infiltrate, signs of occlusive retinal vasculitis, and diffuse retinal capillary leakage as well as the absence of granulomatous anterior uveitis or choroiditis in patients with vitritis.Conclusion: This study provides a diagnostic tree for BD uveitis that needs to be validated in future studies.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalOcular Immunology and Inflammation
Early online date14 Apr 2020
DOIs
Publication statusE-pub ahead of print - 14 Apr 2020

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