Ann Neurol-哈佛大学新发现:人工智能,助力癫痫诊疗

2021-07-15 Freeman MedSci原创

哈佛大学新发现:人工智能,助力癫痫诊疗

在接受脑电图(EEG)监测的危重病人中,多达一半的人出现了癫痫和类似癫痫的周期性和节律性脑活动模式("癫痫样活动")。在小范围的重点队列中,这些模式已被证明与神经系统残疾和死亡率的增加有关,不良后果的概率与癫痫样活动的负担成比例地上升。

然而,癫痫样活动(EA)负担的预后相关性还没有在横跨整个神经系统、内科和外科疾病的大型和异质性队列中得到量化。连续脑电图数据的分析和EA负担的大规模量化,不仅用于预后研究,也用于潜在的治疗试验,但由于审查和注释原始脑电图的耗时性而受到限制。

藉此,哈佛大学的Sahar F. Zafar等人,开发了一种新的自动方法,能够有效地注释来自急性住院病人的大量连续EEG记录中的所有癫痫形态。利用这些注释,他们开发了一个机器学习模型,来估计出院时持续暴露于癫痫样活动对神经系统残疾水平的独立贡献。

踏天对2011-2017年间接受>16小时连续脑电图的1967名神经科、内科和外科患者进行单中心回顾性分析。并开发了一种人工智能算法,对11.02 TB的EEG进行注释,并对记录72小时内的癫痫活动负担进行量化。

他们评估了:
1)记录的前24小时内的负担
2)负担最高的12小时纪元(峰值负担),
3)监测的前72小时内的累积。
最后,应用机器学习来估计癫痫病负担对结果的影响。结果的衡量标准是出院改良的Rankin量表,分为好(0-4)和差(5-6)两类。

他们发现,癫痫峰值负担与不良结局独立相关(P<0.0001)。其他独立的关联包括年龄、APACHE II、发病时的癫痫发作和缺氧缺血性脑病的诊断

根据最后一次脑电图测量(最多72小时的监测)和出院之间的时间间隔:最后一次测量和出院之间的时间间隔<5天。0.0941[CI 0.0706, 0.1191];最后一次测量和出院之间的5-10天。0.0946 [CI 0.0631, 0.1290];最后一次测量和出院之间的间隔时间大于10天。0.0998 [CI 0.0698, 0.1335]。调整协变量后,癫痫样活动峰值负担从0%增加到100%,不良结局的概率增加了35%。

这个研究的重要在于发现了,自动测量癫痫样活动峰值负担为未来的多中心随机试验提供了一个方便、一致和可量化的目标,以调查抑制癫痫样活动是否能改善结果。

原文出处:
Zafar SF, Rosenthal ES, Jing J, Ge W, Tabaeizadeh M, Aboul Nour H, Shoukat M, Sun H, Javed F, Kassa S, Edhi M, Bordbar E, Gallagher J, Moura V, Ghanta M, Shao YP, An S, Sun J, Cole AJ, Westover MB. Automated annotation of epileptiform burden and its association with outcomes. Ann Neurol. 2021 Jul 7. doi: 10.1002/ana.26161. Epub ahead of print. PMID: 34231244.

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    2022-04-05 yinhl1978
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    2021-07-17 1209e435m98(暂无昵称)

    学习了,谢谢分享

    0

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    2021-07-16 学医无涯

    智能代偿

    0

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    2021-07-16 吴牡丽

    学习了

    0

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