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Ocular manifestations in ANCA-associated vasculitis: a comprehensive analysis from Chinese medical centers

  • Shulin Liu,
  • Mei Xu,
  • Xinyu Zhao,
  • Jingyuan Yang,
  • Wenfei Zhang,
  • Youxin Chen

摘要

Introduction

This study aimed to explore ocular manifestations in ANCA-associated vasculitis (AAV), focusing on granulomatosis with polyangiitis (GPA), eosinophilic granulomatosis with polyangiitis (EGPA), and microscopic polyangiitis (MPA) and to examine the associations with laboratory parameters and other systemic manifestations.

Methods

This retrospective study reviewed data from 533 AAV patients across two major Chinese medical centers from January 2016 to November 2023. Data including diagnosis, cranial manifestations of disease, ocular complications, and laboratory parameters were analyzed. Univariate and multivariable logistic regression analyses assessed associations across disease manifestations. Machine learning models were also utilized to predict the risk of retinal/eye involvement in AAV patients.

Results

Among 533 patients (210 GPA, 217 MPA, 99 EGPA, and 7 unclassified AAV), ocular complications were observed in 20.64% of them, with a distribution of 36.67% in GPA, 7.37% in MPA, and 18.18% in EGPA. The most common ocular manifestations included scleritis and retro-orbital mass/dacryocystitis, which were notably prevalent in GPA patients. Retinal involvement was observed in 9.09% of EGPA cases. The machine learning models yielded that eosinophil percentage (EOS%), high-sensitivity C-reactive protein (hsCRP), and CD4 + T cell/CD8 + T cell ratio (T4/T8) can predict retinal involvement. Furthermore, the white blood cell, EOS%, APTT, IgA, hsCRP, PR3-ANCA, and T4/T8 can predict eye involvement.

Conclusion

Ocular manifestations are a prevalent complication across all forms of AAV. Predictive models developed through machine learning offer promising tools for early intervention and tailored patient care. This necessitates a multidisciplinary approach, integrating rheumatology and ophthalmology expertise for optimal patient outcomes.