基于心电图的自动检测左心室缩及其在血液透析患者的预测价值
Theresa Letz1,2, Carina Hörandtner1,2, Matthias C Braunisch3
1AIT Austrian Institute of Technology GmbH, Center for Health and Bioresources, Medical Signal Analysis, Giefinggasse 4, A-1210 Vienna, Austria.
Physiological measurement
|June 19, 2023
概括
在末期病 (ESKD) 患者中,自动化心电图 (ECG) 分析左心室缩 (LVH) 对于预测死亡率与手动测量一样可靠. 这种自动化方法为评估ESKD患者的LVH风险提供了更有效的方法.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 心脏病学 心脏病学
- 医疗技术 医疗技术 医学技术
背景情况:
- 左心室缩 (LVH) 是末期病 (ESKD) 患者死亡的重要危险因素.
- 突然心脏病死亡约占ESKD患者死亡的25%.
- 电心电图 (ECG) 是一个成本高效的工具,用于评估LVH,但手动分析是耗时的.
研究的目的:
- 为了将基于心电图的自动化LVH参数测量与手动注释进行比较.
- 评估ESKD患者心血管和全因死亡率的自动化LVH参数的预测值.
主要方法:
- 利用了301名接受血液透析的ESKD患者的1224小时心电图数据.
- 使用自动和手动方法计算了Peguero-Lo Presti,Cornell和Sokolow-Lyon电压参数.
- 雇员Bland-Altman图表用于协议分析和Cox回归用于死亡率预测.
主要成果:
- 自动和手动LVH测量显示了最小的平均差异 (-0.027 mV,0.027 mV, -0.025 mV).
- 分类性LVH检测方法之间的差异只有13个案例.
- 自动和手动LVH参数显示死亡率的预测值相似.
结论:
- 自动化算法在ESKD患者中提供可靠的LVH参数评估.
- 在预测死亡率方面,自动LVH评估与手动测量一样有效.
- 自动化心电图分析为LVH在ESKD中的风险分层提供了一个有效的替代方案.
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