Li Hui, Guo Qin, Zhang Tiantian, Zhou Shufen, Guo Chengshan
From the Department of Rheumatology and Immunology, The People's Hospital of Baoan Shenzhen, The Second Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.
J Clin Rheumatol. 2025 Aug 1;31(5):e42-e48. doi: 10.1097/RHU.0000000000002225. Epub 2025 Apr 25.
Axial spondyloarthritis (axSpA) is a chronic inflammatory disease primarily affecting the spine and sacroiliac joints, with anterior uveitis (AU) as a common extra-articular manifestation. Predicting AU onset in axSpA patients is challenging, as traditional statistical methods often fail to capture the disease's complexity.
This study aimed to develop an interpretable machine learning (ML) model to predict AU onset in axSpA patients through a historical cohort analysis of 1508 patients from a tertiary medical center. Clinical data involving 54 variables were preprocessed through imputation, factorization, oversampling, outlier capping, and standardization. Recursive feature elimination identified 12 key predictors. Subsequently, 10 ML algorithms were assessed using performance metrics and visualization techniques.
The gradient boosting machine model incorporating 12 key factors showed high accuracy in predicting AU risk. Shapley additive explanations analysis revealed that hip involvement, nonsteroidal anti-inflammatory drug use, and smoking were the most influential predictors. The model's interpretability provided clear insights into the contribution of each feature to AU risk, supporting early diagnosis and personalized treatment.
The gradient boosting machine model predicts AU risk in axSpA patients, helping identify high-risk cases for early intervention and personalized treatment to prevent complications such as vision loss.
轴性脊柱关节炎（axSpA）是一种主要影响脊柱和骶髂关节的慢性炎症性疾病，前葡萄膜炎（AU）是常见的关节外表现。预测axSpA患者的AU发病具有挑战性，因为传统统计方法往往无法捕捉该疾病的复杂性。
本研究旨在通过对一家三级医疗中心的1508例患者进行历史队列分析，开发一种可解释的机器学习（ML）模型来预测axSpA患者的AU发病。涉及54个变量的临床数据通过插补、因子分解、过采样、异常值截断和标准化进行预处理。递归特征消除确定了12个关键预测因子。随后，使用性能指标和可视化技术评估了10种ML算法。
纳入12个关键因素的梯度提升机模型在预测AU风险方面显示出高准确性。Shapley相加解释分析表明，髋关节受累、使用非甾体抗炎药和吸烟是最具影响力的预测因子。该模型的可解释性为每个特征对AU风险的贡献提供了清晰的见解，有助于早期诊断和个性化治疗。
梯度提升机模型可预测axSpA患者的AU风险，有助于识别高危病例以便早期干预和个性化治疗，从而预防视力丧失等并发症。
