血压变化 (BPV) 作为多系统风险和诊断洞察力的新型数字生物标志物:测量,机制和新兴的人工智能方法
Lakshmi Sree Pugalenthi1, Sidhartha Gautam Senapati2, Jay Gohri3
1Department of Internal Medicine, Mercy Catholic Medical Center, Darby, PA 19023, USA.
Biomedicines
|February 27, 2026
概括
血压变化 (BPV),而不仅仅是平均值,显著增加心血管事件和器官损伤的风险. 使用人工智能驱动设备监测BPV可能会改善患者风险评估和管理.
科学领域:
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 神经学 神经学
背景情况:
- 高血压传统上是通过平均血压来评估的.
- 新出现的证据强调了血压变化 (BPV) 的预后价值.
- 升高的BPV意味着血液动力学压力,导致血管损伤和末端器官损伤.
研究的目的:
- 审查目前关于BPV预后影响的证据.
- 探索BPV的病理生理学,测量技术和临床影响.
- 检查人工智能 (AI) 在BPV监测和风险分层中的作用.
主要方法:
- 在主要数据库 (PubMed,Cochrane Library,Scopus,Web of Science) 中进行了全面的文献搜索.
- 包括的研究类型包括观察性研究,队列,随机试验和元分析.
- 该审查综合了BPV与死亡率,心血管事件和末端器官损伤相关的证据.
主要成果:
- 高血压与心血管死亡率,中风,心力衰竭和慢性病进展的风险增加有关.
- 血压高血压有助于终端器官损伤,包括认知能力下降和孕前.
- 病理生理机制包括自主失调,动脉硬,内皮功能障碍和炎症.
结论:
- 血压是多系统风险的关键,动态标志物,独立于平均血压.
- 抗高血压药物可能会影响BPV,尽管具体试验有限.
- 人工智能驱动的设备为持续的BPV监测和个性化的风险分层提供了潜在的潜力.
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