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心率变异性分析预测丙泊酚-瑞芬太尼全身麻醉前唤醒:一项可行性研究。

Heart rate variability analysis for the prediction of pre-arousal during propofol-remifentanil general anaesthesia: A feasibility study.

机构信息

CIC IT 1403, CHU Lille, Lille, France.

ULR 2694-METRICS, Univ. Lille, Lille, France.

出版信息

PLoS One. 2024 Oct 31;19(10):e0310627. doi: 10.1371/journal.pone.0310627. eCollection 2024.

Abstract

Accidental awareness during general anaesthesia is a major complication. Despite the routine use of continuous electroencephalographic monitoring, accidental awareness during general anaesthesia remains relatively frequent and constitutes a significant additional cost. The prediction of patients' arousal during general anaesthesia could help preventing accidental awareness and some researchers have suggested that heart rate variability (HRV) analysis contains valuable information about the patient arousal during general anaesthesia. We conducted pilot study to investigate HRV ability to detect patient arousal. RR series and the Bispectral IndexTM (BISTM) were recorded during general anaesthesia. The pre-arousal period T0 was defined as the time at which the BISTM exceeded 60 at the end of surgery. HRV parameters were computed over several time periods before and after T0 and classified as "BISTM<60" or "BISTM≥60". A multivariate logistic regression model and a classification and regression tree algorithm were used to evaluate the HRV variables' ability to detect "BISTM≥60". All the models gave high specificity but poor sensitivity. Excluding T0 from the classification increased the sensitivity for all the models and gave AUCROC>0.7. In conclusion, we found that HRV analysis provided encouraging results to predict arousal at the end of general anaesthesia.

摘要

全身麻醉期间出现意外意识是一种主要并发症。尽管常规使用连续脑电图监测,但全身麻醉期间仍会出现意外意识,这仍然是一个相对频繁的问题,并构成了显著的额外成本。预测全身麻醉期间患者的觉醒可以帮助预防意外意识,一些研究人员认为心率变异性(HRV)分析包含有关全身麻醉期间患者觉醒的有价值信息。我们进行了一项初步研究,以调查 HRV 检测患者觉醒的能力。RR 系列和双频谱指数(BISTM)在全身麻醉期间被记录下来。预觉醒期 T0 定义为手术结束时 BISTM 超过 60 的时间。在 T0 前后的多个时间段计算 HRV 参数,并分类为“BISTM<60”或“BISTM≥60”。使用多元逻辑回归模型和分类回归树算法来评估 HRV 变量检测“BISTM≥60”的能力。所有模型的特异性都很高,但敏感性较差。从分类中排除 T0 增加了所有模型的敏感性,并给出 AUCROC>0.7。总之,我们发现 HRV 分析提供了令人鼓舞的结果,可以预测全身麻醉结束时的觉醒。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/41e5/11527244/b69bf9dc9ee3/pone.0310627.g001.jpg

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