机器学习预测在牛津分类系统的IgA脏病的T-分数
Lin-Lin Xu1, Di Zhang2,3,4, Hao-Yi Weng2,3,4
1Renal Division, Peking University First Hospital, Kidney Genetics Center, Peking University Institute of Nephrology, Key Laboratory of Renal Disease, Ministry of Health of China, Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China.
Frontiers in immunology
|August 21, 2023
概括
一个新的模型使用临床数据预测免疫球蛋白A脏病 (IgAN) 患者的损伤严重程度,帮助那些没有活检的人的预后. 这种病理T分数预测 (Tpre) 模型为管理Igan和预防末期病 (ESKD) 提供了宝贵的见解.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 免疫学 免疫学 免疫学
- 病理学 病理学 病理学
背景情况:
- 免疫球蛋白A脏病 (IgAN) 是末期脏病 (ESKD) 的主要原因.
- 病理表现,特别是牛津分类T分数,对于预测Igan患者的结果至关重要.
- 在没有脏活检数据的情况下,对IGAN患者的预后评估可能具有挑战性.
研究的目的:
- 使用临床变量开发Igan患者病理T-score的预测模型.
- 评估这种非侵入性预测模型在预测5年ESKD风险方面的有效性.
- 评估预测的T-score (Tpre) 是否可以替代活检衍生的T-score (Tbio) 在预测Igan预后.
主要方法:
- 在690名Igan患者的训练数据集上使用堆叠算法开发了一种病理T分数预测 (Tpre) 模型.
- 使用临床变量,临床变量加Tbio,临床变量加Tpre.构建了五年ESKD预测模型.
- 模型的性能在一个独立的测试组和355名患者的外部验证组上进行了评估.
主要成果:
- 使用年龄,血压,蛋白尿,eGFR,血清IgA和尿酸等特征的Tpre模型在测试组中实现了0.82的AUC.
- 与基础临床模型 (AUC 0.86) 相比,纳入Tbio的5年ESKD预测模型显示AUC显著增加到0.92.
- 使用Tpre的模型表现出与Tbio可比的性能,AUC为0.90而不是0.92,在外部验证集中实现了0.93的AUC.
结论:
- 一种新的病理T分数预测 (Tpre) 模型有效地利用常规临床数据估计Igan患者的病理严重程度.
- Tpre模型有助于预测缺少脏活检得分的患者的Igan预后.
- 这种非侵入性方法支持IgAN的临床决策和管理策略.
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