Open Reimplementation of the BIS Algorithms for Depth of Anesthesia.
Open Reimplementation of the BIS Algorithms for Depth of Anesthesia.
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DOI：
10.1213/ane.0000000000006119
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发表时间：
2022-10-01
影响因子：
5.7
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中科院分区：
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BIS 评分已融入临床麻醉护理和研究。然而，此类监视器使用的算法仍然是专有的。我们实际上并不知道我们在测量什么。如果我们知道，我们就可以更好地理解评分偏差的临床预后意义，并在闭环控制或避免术后认知功能障碍或青少年神经损伤方面取得更大的研究进展。在之前的工作中，对 A-2000 BIS 监视器进行了取证拆卸，并将其算法（BIS 引擎）作为机器代码检索。模拟器的开发首次允许根据任意脑电图数据计算 BIS 分数。我们现在解决这些算法如何运作以及它们在生理学上代表什么的基本问题。脑电图数据是在 12 名接受骨科、普通、血管和神经外科手术常规麻醉管理的患者的诱导、维持和苏醒期间获得的。这些数据用于触发 BIS 引擎各个部分的严密监控执行，使其能够以高级语言重新实现为名为 ibis 的算法。然后为了简洁和生理清晰度而重写 Ibis，以产生一种新颖的完全透明的麻醉深度算法，名为 openibis。 ibis 算法的输出在功能上与本地 BIS A-2000 没有区别，r = 0.9970 (0.9970-0.9971)，在无单位 0-100 麻醉深度量表上，方法之间的 Bland-Altman 平均差异为 -0.25 ± 2.6。此精度超过了任何早期重新实现 BIS 算法功能的尝试的性能。 openibis 算法还与本机算法的输出非常接近（r = 0.9395 (0.9390-0.9400)，Bland-Altman 2.62 ± 12.0），仅 64 行可读代码，其功能可以与 EEG 信号中的可观察特征明确相关。 openibis 算法的操作以直观的图形形式描述。 openibis 算法最终提供了有关 BIS 的明确答案：最重要的信号分量对低伽马波段的依赖，以及这些分量如何相互加权。逆向工程可以以其他方式无法获得的清晰和精确的方式得出这些结论。这些结果与之前被认为具有权威性的评论文章相矛盾：BIS 评分似乎根本不依赖于脑电双频指数。这些结果通过阐明其生理学基础，为使用麻醉深度评分的临床麻醉研究奠定了坚实的基础，并能够与其他动物模型进行比较以进行机制研究。
BIS scores have become interwoven into clinical anesthesia care and research. Yet, the algorithms used by such monitors remain proprietary. We do not actually know what we are measuring. If we knew, we could better understand the clinical prognostic significance of deviations in the score and make greater research advances in closed-loop control or avoiding post-operative cognitive dysfunction or juvenile neurological injury. In previous work, an A-2000 BIS monitor was forensically disassembled and its algorithms (the BIS Engine) retrieved as machine code. Development of an emulator allowed BIS scores to be calculated from arbitrary EEG data for the first time. We now address the fundamental questions of how these algorithms function and what they represent physiologically. EEG data were obtained during induction, maintenance and emergence from twelve patients receiving customary anesthetic management for orthopedic, general, vascular and neurosurgical procedures. These data were used to trigger the closely-monitored execution of the various parts of the BIS Engine, allowing it to be reimplemented in a high-level language as an algorithm entitled ibis. Ibis was then rewritten for concision and physiological clarity to produce a novel completely clear-box depth-of-anesthesia algorithm entitled openibis. The output of the ibis algorithm is functionally indistinguishable from the native BIS A-2000, with r = 0.9970 (0.9970-0.9971) and Bland-Altman mean difference between methods of −0.25 ± 2.6 on a unitless 0-100 depth-of-anesthesia scale. This precision exceeds the performance of any earlier attempt to reimplement the function of the BIS algorithms. The openibis algorithm also matches the output of the native algorithm very closely (r = 0.9395 (0.9390-0.9400), Bland-Altman 2.62 ± 12.0) in only 64 lines of readable code whose function can be unambiguously related to observable features in the EEG signal. The operation of the openibis algorithm is described in an intuitive, graphical form. The openibis algorithm finally provides definitive answers about the BIS: the reliance of the most important signal components on the low-gamma waveband, and how these components are weighted against each other. Reverse engineering allows these conclusions to be reached with a clarity and precision that cannot be obtained by other means. These results contradict previous review articles that were believed to be authoritative: the BIS score does not appear to depend on a bispectral index at all. These results put clinical anesthesia research using depth-of-anesthesia scores on a firm footing by elucidating their physiological basis, and enable comparison to other animal models for mechanistic research.