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使用LASSO回归分析开发一种风险因子名谱预测模型,用于患有急性冠状动脉综合征并发症的高血压患者
Jumin Xie1, Li Song2, Zixuan Yang2
1Hubei Key Laboratory of Renal Disease Occurrence and Intervention, Medical School, Hubei Polytechnic University, Huangshi, Hubei, 435003, China. xiejumin@hbpu.edu.cn.
BMC cardiovascular disorders
|December 13, 2025
概括
这项研究开发了一个名图,用于预测高血压患者急性冠状动脉综合征 (ACS) 风险. 该模型有助于临床医生在早期诊断,个性化治疗和心血管疾病管理的预后.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 全球急性冠状动脉综合征 (ACS) 发病率正在上升.
- 高血压是ACS的一个重要风险因素.
研究的目的:
- 在高血压患者中开发ACS风险的预测性诺莫格拉姆模型.
- 为早期诊断和个性化治疗策略提供一个工具.
- 为了提高高血压ACS患者的预后评估.
主要方法:
- 从980名ACS患者 (2018-2023) 进行了回顾性数据收集.
- 包括患者的人口统计,病史和临床数据.
- 使用LASSO和后勤回归分析开发一个名图.
主要成果:
- 这项研究包括682名高血压ACS患者 (69.59%).
- 在各种临床和实验室参数中,在高血压和非高血压组之间观察到显著的差异.
- 构建并验证了一种高质量的名录模型.
结论:
- 为预测高血压患者的ACS风险,开发了一种经过验证的诺莫格拉姆模型.
- 该模型显示了高的预测准确性和临床实用性.
- 该工具支持改善对高血压患者ACS管理的临床决策.
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