基于机器学习的预后模型的开发和验证,用于IgA脏病与慢性脏病阶段3或4的IgA脏病
Zixian Yu1, Xiaoxuan Ning2, Yunlong Qin1,3
1Department of Nephrology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.
Kidney diseases (Basel, Switzerland)
|December 12, 2024
概括
这项研究开发了一种随机生存森林 (RSF) 模型,用于预测免疫球蛋白A脏病 (IgAN) 患者的末期脏病 (ESKD). 该RSF模型在识别病进展高风险患者方面表现出卓越的性能.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 内部医学 内部医学
- 生物统计学 生物统计学
背景情况:
- 免疫球蛋白A脏病 (IgAN) 患者的估计膜过率 (eGFR) 降低和蛋白尿升高,面临末期病 (ESKD) 的风险更高.
- 患有慢性病 (CKD) 3-4期和蛋白尿 ≥1.0 g/d的Igan患者的预后尚不清楚,需要准确的预测模型.
研究的目的:
- 开发和验证预后模型来预测ESKD和Igan患者的死亡率,这些患者患有CKD第3-4期和显著的蛋白尿.
- 确定影响该患者群体疾病进展的关键临床和病理因素.
主要方法:
- 利用了来自西京医院 (2008年12月至2020年1月) 263名Igan患者 (CKD3-4期,蛋白尿≥1.0g/d) 的数据.
- 开发并比较了包括随机生存森林 (RSF),生存支持矢量机器 (SSVM),极端梯度增强 (XGBoost) 和考克斯回归在内的预后模型.
- 使用一致性指数 (C-指数),集成的Brier分数 (IBS),净重新分类指数 (NRI) 和集成的歧视改进 (IDI) 评估模型性能.
主要成果:
- RSF模型实现了最高的预测准确性,在训练队列中C指数为0.871,在测试队列中为0.810.
- 确定了IGAN进展的七个显著风险因素:年龄,血液尿素,血清尿酸,血清,血清,淋巴结核硬化比率,血红蛋白和管状缩/间歇性纤维化.
- 与SSVM,XGBoost和Cox回归模型相比,RSF模型显示出优越的歧视和风险分类,正如NRI和IDI所指出的那样.
结论:
- 开发的RSF模型有效地预测了Igan患者的预后,这些患者患有CKD阶段3-4和蛋白尿≥1.0g/d.
- 该模型结合了七个关键的风险因素,为临床决策和患者管理提供了宝贵的见解.
- 该RSF模型显示了优越的性能比传统的方法,如Cox回归预测Igan进展.
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