Identification of red flags for IgG4-related disease: an international European Reference Network for Rare Connective Tissue Diseases framework.

IgG4-related disease is a rare fibroinflammatory condition. Prompt recognition is fundamental to initiate treatment and to prevent organ damage. Diagnostic and classification criteria are primarily intended for use by clinicians with established expertise in IgG4-related disease. Absence of disease awareness among primary care physicians and specialists without expertise in IgG4-related disease remains the main cause of diagnostic delay. We aimed to identify red flags that might increase the suspicion of IgG4-related disease in primary and secondary care settings. A task force of experts in IgG4-related disease from the European Reference Network for Rare Connective Tissue Diseases (ERN-ReCONNET), patient representatives, and primary care physicians derived potential red flags for IgG4-related disease through a systematic literature search and a level of agreement exercise. Five red flags reached 100% agreement among experts: swelling in one or more organ system; pancreatic and biliary tree involvement; increased serum IgG4; IgG4 ⁺ plasma cell tissue infiltration; and obliterative phlebitis. Red flags for IgG4-related disease are intended for use in primary and secondary care to improve referral to centres of expertise and prompt early diagnosis.
Competing Interests: Declaration of interests GPG received funding from The European Commission within the contract SANTE/2018/B3/030-SI2·813822 to support collaboration with European Reference Network (ERN) ReCONNET as a methodologist in the systematic review on red flags for IgG4-related disease. FF received honoraria from GSK for lectures and plenary presentations at educational events. TA received travel grants from AbbVie and Neovil; declares study support from Janssen; and received honoraria from AbbVie, Amgen, AstraZeneca, GSK, and Neovil for lectures and plenary presentations at educational events. All other authors declare no competing interests.
(Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)