基于过氧化酶的综合性血液学分析,用于预测1年内心肌梗塞和死亡
Marie-Luise Brennan1, Anupama Reddy, W H Wilson Tang
1Department of Cell Biology, Center for Cardiovascular Diagnostics and Prevention, Cleveland Clinic, Ohio, USA.
Circulation
|June 23, 2010
概括
在血液检测中识别生物模式可以改善心血管风险预测,对于心脏状况稳定的患者. 使用血液学数据的新模型比传统因素更好地预测心肌梗塞 (MI) 和死亡.
科学领域:
- 心脏病学 心脏病学
- 血液学 血液学 血液学
- 生物统计学 生物统计学
背景情况:
- 生物模式识别显示出有可能识别心肌梗塞 (MI) 和死亡风险的患者.
- 血液学表型数据,包括白细胞过氧化酶,红细胞和血小板参数,可能与传统风险因素相比,在稳定的心脏病患者中提供更好的心血管风险预测.
研究的目的:
- 开发和验证一个预测模型 (PEROX),用于稳定心脏病患者的1年死亡和心脏病发作风险.
- 评估综合性血液学模式识别模型对传统风险因素的增量预后价值.
主要方法:
- 分析了7369名接受选择性心脏评估的稳定患者队列.
- 一个预测模型 (PEROX) 通过使用标准临床数据和高通量过氧化酶基血液学分析仪数据从具有差异的完整血液样本中获得.
- 该模型是在导出队列 (n=5895) 中开发的,并在独立队列 (n=1474) 中验证的.
主要成果:
- 确定了23个高风险和24个低风险的血液学模式.
- 红细胞和白细胞 (氧化酶) 衍生的参数是死亡风险的关键预测因素.
- 心脏病发作风险模式包括传统的心脏病风险因素和来自所有血细胞系的元素.
- 佩洛克斯模型在1年死亡或心脏病发作风险方面实现了78%的预后准确性,超过了传统的风险因素 (67%).
- 佩洛克斯模型将23.5%的患者重新分类到不同的风险类别,当添加到传统风险因素 (P<0.001) 时.
结论:
- 临床,生化和血液学参数的全面模式识别提供了增量预后价值.
- 这种方法提高了在接受选择性心脏导管治疗的稳定患者的1年死亡和心脏病发作风险的预测.
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