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多式联网数据的机器学习集成确定了在没有心血管疾病的人群中循环NT-proBNP的关键特征

Zhiyuan Ning¹, Xuanfei Jiang¹, Huan Huang¹

¹Department of Neurology, The Fifth School of Clinical Medicine of Zhejiang, Huzhou Central Hospital, Chinese Medical University, 1558 Third Ring North Road, Huzhou, 313000, Zhejiang, China.

Scientific reports

|April 8, 2025

概括

年龄,性别,种族,静脉血压和红细胞计数是未患有心血管疾病的个体中N-终端亲脑尿 (NT-proBNP) 水平的关键预测因素. 了解这些因素对于准确的NT-proBNP解释至关重要.

科学领域:

生物标志物研究的研究.
心血管健康的心血管健康
机器学习在医学中的应用.

背景情况:

N-终端亲脑尿 (NT-proBNP) 是诊断和预测各种疾病,特别是心力衰竭的重要生物标志物.
对影响心血管健康个体NT-proBNP水平的因素的全面评估是有限的.
了解这些影响因素对于准确的临床解释和个性化医学至关重要.

研究的目的:

在没有被诊断为心血管疾病的大量人群中,识别和全面评估与NT-proBNP水平相关的因素.
利用机器学习来识别NT-proBNP的关键预测因素.
通过使用先进的统计模型,验证已识别的预测因素和NT-proBNP水平之间的关系.

主要方法:

利用了来自1999-2004年国家健康和营养检查调查 (NHANES) 的12526名参与者的数据.
采用机器学习来分析66种人口,人体,生活方式,生化,血液,代谢和疾病特征.
应用单变量,多变量,加权最小方位回归 (WLS) 和通用添加模型 (GAM) 来进行特征选择和验证.

主要成果:

年龄是NT-proBNP水平的最强单变量预测因素 (R2=36.91%).
在多变量模型中确定的前六个预测因素是年龄,性别,红细胞计数,种族/种族,静脉血压和总蛋白质水平.
一般化添加模型 (GAM) 揭示了NT-proBNP与年龄,红细胞计数,缩血压和总蛋白质之间的显著非线性关联.

结论:

年龄,性别,种族/民族,静脉血压,红细胞计数和总蛋白质是一般人群中NT-proBNP的重要预测因素.
该研究强调了考虑多样化的人口子组和个体特征对于准确的NT-proBNP生物标志物解释的重要性.
研究结果支持开发个性化临床算法,用于NT-proBNP评估和队列研究设计.

关键词:

在流通中的NT-proBNP.机器学习是机器学习.国家健康和营养检查调查 (NHANES)没有心血管疾病的人.个性化医疗是个性化的医疗.公共卫生公共卫生

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