在社区高血压患者中,机器学习衍生的回声心脏学表型的预后影响
Anping Cai1, Zhou Dan1, Yingling Zhou1
1Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Science, Southern Medical University, Guangzhou, China.
概括
在高血压 (HTN) 中识别心声学表型至关重要. 在高血压患者中,严重的心脏改造显著增加了心血管事件和全因死亡的风险.
科学领域:
- 心脏病学 心脏病学
- 高血压研究 高血压研究
- 诊断成像 诊断成像 诊断成像
背景情况:
- 心声图是评估高血压 (HTN) 中心脏重塑的标准工具.
- 有限的研究存在于特定的心声回声表现型及其在HTN患者的预后价值.
研究的目的:
- 研究基于社区的高血压人口中的心声回声学表型的预后影响.
- 根据心声回声学特征识别不同的患者群.
主要方法:
- 分析了1881名没有严重心血管或脏疾病的高血压患者.
- 两步集群分析使用四个常规回声心脏学变量确定了两个不同的回声心脏学表型.
- 患者的随访时间平均为4.2年.
主要成果:
- 集群1 ("轻度重塑"HTN;n=1492) 显示左心房扩大和左心室缩的患病率较低,腹功能更好.
- 集群2 ("重重改造"HTN; n=389) 呈现较高的左心房扩大和左心室缩率,与扩张功能受损.
- 与集群1相比,集群2的患者年龄较大,更有可能是女性,并具有较高的并发症负担,并经历了与集群1相比显著更高的心血管死亡率和全因死亡率.
结论:
- 基于常规回声心脏学变量的算法可以识别心血管和全因死亡风险较高的无症状高血压患者.
- 需要进一步的研究来开发和验证表型特异性高血压的预防和干预策略.
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