推进高血压脏病的精确医学:一种包含病理指标的新型预后模型
Yunlong Qin1,2, Jin Zhao3, Yan Xing3,4
1Department of Nephrology, Xijing Hospital, Fourth Military Medical University, Xi'an, China, 379925000@qq.com.
Kidney & blood pressure research
|March 30, 2025
概括
随机生存森林 (RSF) 算法准确地预测高血压脏病 (HN) 患者的长期脏病进展. 这种先进的模型优于传统方法,为管理HN提供了更好的临床见解.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 高血压脏病 (HN) 构成长期损伤的重大风险.
- 准确预测HN患者的预后对于有效管理至关重要.
- 脏活检是诊断HN和评估其严重程度的关键.
研究的目的:
- 评估高血压脏病 (HN) 患者的长期脏预后.
- 利用随机生存森林 (RSF) 算法来预测HN患者的不良结果.
- 将RSF模型的性能与传统的考克斯回归进行比较.
主要方法:
- 活检证明HN的患者被招募并分为训练 (70%) 和测试 (30%) 组.
- 一个复合的终点包括估计膜过率 (eGFR),末期脏疾病或死亡下降≥50%.
- 使用RSF识别的因素开发了RSF和Cox回归模型;使用C指数,Brier分数,NRI和IDI评估了模型性能.
主要成果:
- 分析了225名HN患者;32%的患者在299个月的中位随访期间经历了不良事件.
- 在RSF模型中确定了6个关键变量 (病态慢性,eGFR,HDL胆固醇,血红素,单细胞,中风量).
- 与考克斯模型相比,RSF模型在训练和测试组中表现出更高的预测准确性 (更高的C指数,更好的NRI/IDI).
结论:
- 该RSF算法有效地识别HN预后的风险因素.
- 开发了一种新的临床预后RSF模型,用于预测HN患者的不良结果.
- 该RSF模型在考克斯回归上提供了优越的性能,为临床实践提供了有价值的见解.
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