基于系统性炎症反应指数和痛风中心血管疾病风险的机器学习:一项回顾性研究和临床验证
Qiang Zhang1, Xuan-Hua Yu2, Wei-Zhen Zhang3
1Department of Rheumatology and Chinese Medicine, The 962nd Hospital of the PLA, Harbin, 150048, China.
Clinical rheumatology
|November 17, 2025
概括
系统性炎症反应指数 (SIRI) 与痛风患者心血管疾病 (CVD) 风险增加有线相关. 较高的SIRI水平表明风险更高,这表明SIRI是该人群中CVD预测的有价值工具.
科学领域:
- 类风湿病学 类风湿病学
- 心脏病学 心脏病学
- 生物统计学 生物统计学
背景情况:
- 痛风是一种慢性炎症状况,通常与心血管疾病 (CVD) 相关.
- 鉴定痛风患者心血管疾病风险的可靠标志物对于及时干预至关重要.
研究的目的:
- 研究系统性炎症反应指数 (SIRI) 与患有痛风的人患心血管疾病的风险之间的关联.
- 评估一个机器学习模型的预测性能,包括SIRI用于痛风患者的心血管疾病风险.
主要方法:
- 分析了六个周期的NHANES数据.
- 机器学习算法和SHAP解释用于共变量选和变量重要性.
- 后勤回归,限制立方脊柱 (RCS),ROC,DCA和校准曲线,以评估SIRI与心血管疾病风险和模型性能的关联.
- 将SIRI与弗雷明汉风险评分 (FRS) 模型集成,以量化预测的改进.
主要成果:
- 在痛风患者中,SIRI和心血管疾病风险之间发现了显著的正线性关联 (OR=1.297每单位增加).
- 与最低四分位数 (Q1) 相比,SIRI最高四分位数 (Q4) 的个人患心血管疾病的风险增加了2.060倍.
- 开发的模型显示强大的歧视 (AUC=0.755) 并提高了FRS模型的预测准确性.
结论:
- 在被诊断患有痛风的患者中,SIRI显示出与心血管疾病风险的正线性关系.
- 结合SIRI的基于机器学习的模型为痛风患者的心血管疾病风险预测提供了强大的方法.
- 作为FRS等现有风险评分的宝贵补充,SIRI有助于早期CVD识别和管理.
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