随机生存森林机器学习用于预测心血管事件的患者与测量脂蛋白 (a) 水平:一个模型开发研究
Jay B Lusk1,2,3, Emily C O'Brien1,2,4, Bradley G Hammill1,4
1Department of Population Health Sciences (J.B.L., E.C.O., B.G.H.), Duke University, Durham, NC.
Circulation. Genomic and precision medicine
|January 23, 2025
概括
一个新的随机生存森林模型更好地预测测量脂蛋白水平的患者的心血管事件. 这种先进的模型超越了传统的风险因素评估,可以更好地预测心血管风险.
科学领域:
- 心脏病学 心脏病学
- 生物统计学 生物统计学
- 基因组学就是基因组学.
背景情况:
- 已建立的心血管风险模型可能无法准确评估患有高脂蛋白[Lp[a]水平的患者.
- 脂蛋白是动脉样硬化心血管疾病的因果风险因素.
研究的目的:
- 开发和评估一种新的心血管事件风险预测模型.
- 为了比较随机生存森林模型与测量Lp(a) 水平的患者的传统风险因子模型的性能.
主要方法:
- 使用纳什维尔生物科学LPp (a) 数据集的数据进行模型开发研究.
- 包括在1989-2022年间测量Lp (a) 的患者,以及至少一年的先前EHR数据.
- 将随机生存森林模型与使用传统风险因素的考克斯比例危险模型进行比较,用哈雷尔的C指数评估歧视.
主要成果:
- 该研究包括4369名患者 (49.5%为女性,平均年龄51岁,平均Lp (a) 33.6 mg/dL).
- 与初级预防 (0.69) 和二级预防 (0.80) 模型相比,随机生存森林模型实现了更高的C指数 (0.82).
- 这些发现在初级预防群体和竞争性风险分析中一致.
结论:
- 一个随机生存森林模型在预测Lp (a) 水平的个体中预测心血管事件方面表现优异.
- 这表明将Lp(a) 纳入先进模型可以增强心血管风险分层.
更多相关视频
06:16Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies
Published on: July 28, 2023
2.4K
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.2K
相关概念视频
01:19Survival Tree
57
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
57
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
26
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
26