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Xiwen Ji, Zicheng Zhang, Dan Lin, Mali Dai, Xia Zhao, Xingneng Guo, Jie Du, Meng Zhou, Yuqin Wang; A Novel Clinical Five-Risk Factor Panel for Individualized Recurrence Risk Assessment of Patients With Acute Anterior Uveitis. Trans. Vis. Sci. Tech. 2021;10(13):29. doi: https://doi.org/10.1167/tvst.10.13.29.
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Detecting and managing relapses of acute anterior uveitis (AAU) is necessary for improving follow-up planning to minimize recurrences and further complications. However, reliable clinical and laboratory risk factors are lacking, as is a predictive model for use in clinical practice that is capable of identifying patients at high risk for recurrence after remission.
We analyzed 38 laboratory parameters and clinical data from a large longitudinal retrospective cohort of 233 patients with AAU. Association of laboratory parameters with recurrence-free survival (RFS) was evaluated using univariate Cox proportional hazards regression. A clinically applicable predictive model was developed using a logistic regression model.
Of the 38 laboratory parameters studied, we identified 5 parameters (HDL, ankylosing spondylitis, HLA-B27, MO, and LDL) to be associated with RFS. We developed a clinical five-risk factor panel (5RF-panel), which was capable of effectively distinguishing recurrent patients from nonrelapsed patients (area under the curve [AUC] = 0.837), as well as between patients with high and low risks of AAU recurrence (hazard ratio [HR] = 45.874, 95% confidence interval [CI] = 5.232-402.2, P < 0.001). The robust performance of the 5RF-panel was further validated in the testing cohort (AUC = 0.725, and HR = 51.982, 95% CI = 4.438-608.9, P = 0.024). Furthermore, the 5RF-panel demonstrated superior performance in stratifying recurrence risk based on known risk factors.
We identified and validated a novel clinical 5RF-panel to predict individualized risk of AAU recurrence and improved patient classification for clinical management.
The present study identified and validated a 5RF-panel that is a promising individualized predictive tool to monitor recurrence risk and guide personalized management of patients with AAU.
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