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开发和初步验证IgA病进展的预测模型

Jinjin Wang¹, Tianmu Chen², Yan Fu³

¹Department of Nephrology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310003, Zhejiang, People's Republic of China.

Scientific reports

|December 14, 2025

概括

这项研究开发了一种实际模型,用于预测IgA脏病 (IgAN) 患者的5年脏存活率. 该工具有助于早期风险分层,特别是在初级保健中,以实现个性化的Igan管理.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
医学统计医学统计
临床预测建模临床预测建模

背景情况:

在全球范围内,IgA脏病 (IgAN) 是最常见的原发性淋巴细胞疾病.
在诊断时预测IGAN进展是具有挑战性的,特别是在初级保健机构.
对于早期风险分层和个性化管理,需要准确的预测工具.

研究的目的:

开发和验证一个可靠的预后模型,用于估计Igan患者的5年存期.
支持Igan的早期风险分层和个性化管理策略.
为初级保健机构创建一个临床上适用的工具.

主要方法:

从杭州医院 (培训/内部验证) 招募了1135名Igan患者.
外部验证使用来自三个独立中心的352名患者.
采用LASSO-Cox回归和XGBoost生存模型来识别预后变量并构建一个名ogram.

主要成果:

开发的名图表显示出高预测准确度,AUC为0.951 (训练),0.927 (内部验证) 和0.913 (外部验证).
低布里尔分数 (0.029内部,0.045外部) 表示校准良好.
决策曲线分析证实了名图的有利临床效用.

结论:

开发了一种临床实用的预后模型,以估计Igan患者的5年存期.
该模型利用了常规的临床和病理特征,使其适合于初级保健.
这种工具有助于早期识别高风险个体,使个性化长期管理成为可能,即使在资源有限的环境中.

关键词:

后续护理后续护理在IgA脏病发作中,IgA脏病发作最初的诊断初始的诊断.机器学习是机器学习.

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