NAT COMMUN:优于临床医生,机器学习助力急性冠脉综合征预测 

2020-08-11 MedSci原创 MedSci原创

目前,迅速识别急性冠脉综合征仍然是临床实践中的一个重要挑战。12导联心电图(ECG)在患者初始评估时很容易获得,但基于当前规则的解释方法缺乏足够的准确性。

目前,迅速识别急性冠脉综合征仍然是临床实践中的一个重要挑战。12导联心电图(ECG)在患者初始评估时很容易获得,但基于当前规则的解释方法缺乏足够的准确性。

最近,在Nature Communication上面发表了一篇报道,其用基于机器学习的方法,预测胸痛患者潜在的急性心肌缺血风险。

利用12导联心电图的554个时空特征,研究人员在两个独立的前瞻性患者队列(n = 1244)上训练和测试多个分类器。

研究人群由正在进行的EMPIRE研究(用于及时识别冠心病事件的ECG方法)中的两个患者队列组成。第一个队列(2013-2014年)包括745名患者,第二个队列(2014-2015年)包括499名患者,其具有可解释心电图(即没有过多的噪声或伪影,不存在室性心动过速或颤动)。

医护人员在两个队列中连续招募患者,并在进行任何医疗救治措施之前在现场获取了12导联心电图。医护人员将心电图传输到医疗指挥中心,并将其存储在那里进行离线分析。

研究人员收集了自索引日起30天的临床数据。用于训练分类器和测试性能的主要研究结果被定义为任何ACS事件。

在随后的敏感性分析中,研究人员排除了院前心电图确诊的STEMI患者,这些患者被紧急送往导管实验室。

最后,为了优化算法的临床效用,研究人员在分析中纳入了所有的ECG,包括那些有继发性复极化变化的ECG(即起搏、束支阻滞和左心室肥厚)。

在保持较高的负预测值的同时,研究人员最终的融合模型与商业软件相比,实现了52%的灵敏度增益,与有经验的临床医生相比,实现了37%的灵敏度增益。

这样一个超早期的、基于心电图的临床决策支持工具,当与训练有素的急诊人员的判断相结合时,将有助于改善胸痛患者的临床结果,减少不必要的费用。

 

原始出处:

Salah Al-Zaiti et al. Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram, Nature Communications (2020). 

 

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    2020-12-04 liye789132251
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    2020-08-18 liuli5079
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    2020-08-13 120def22m49(暂无昵称)

    厉害

    0

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    2020-08-11 zhaojing6889

    👍

    0

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