LAETA, INEGI, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.
Centro de Investigação Clínica em Anestesiologia, Serviço de Anestesiologia, Centro Hospitalar do Porto, Largo Professor Abel Salazar, 4099-001, Porto, Portugal.
Biomed Eng Online. 2020 Nov 14;19(1):84. doi: 10.1186/s12938-020-00828-6.
The amount of propofol needed to induce loss of responsiveness varied widely among patients, and they usually required less than the initial dose recommended by the drug package inserts. Identifying precisely the moment of loss of responsiveness will determine the amount of propofol each patient needs. Currently, methods to decide the exact moment of loss of responsiveness are based on subjective analysis, and the monitors that use objective methods fail in precision. Based on previous studies, we believe that the blink reflex can be useful to characterize, more objectively, the transition from responsiveness to unresponsiveness. The purpose of this study is to investigate the relation between the electrically evoked blink reflex and the level of sedation/anesthesia measured with an adapted version of the Richmond Agitation-Sedation Scale, during the induction phase of general anesthesia with propofol and remifentanil. Adding the blink reflex to other variables may allow a more objective assessment of the exact moment of loss of responsiveness and a more personalized approach to anesthesia induction.
The electromyographic-derived features proved to be good predictors to estimate the different levels of sedation/anesthesia. The results of the multinomial analysis showed a reasonable performance of the model, explaining almost 70% of the adapted Richmond Agitation-Sedation Scale variance. The overall predictive accuracy for the model was 73.6%, suggesting that it is useful to predict loss of responsiveness.
Our developed model was based on the information of the electromyographic-derived features from the blink reflex responses. It was able to predict the drug effect in patients undergoing general anesthesia, which can be helpful for the anesthesiologists to reduce the overwhelming variability observed between patients and avoid many cases of overdosing and associated risks. Despite this, future research is needed to account for variabilities in the clinical response of the patients and with the interactions between propofol and remifentanil. Nevertheless, a method that could allow for an automatic prediction/detection of loss of responsiveness is a step forward for personalized medicine.
在患者中，需要诱导失去反应性的异丙酚量差异很大，他们通常需要的初始剂量低于药物说明书中推荐的剂量。准确识别失去反应性的时刻将决定每个患者所需的异丙酚量。目前，决定失去反应性的确切时刻的方法是基于主观分析，而使用客观方法的监测器在精度上存在不足。基于之前的研究，我们认为眨眼反射可以更客观地描述从反应性到无反应性的转变。本研究的目的是在异丙酚和瑞芬太尼全身麻醉诱导期间，研究电诱发眨眼反射与使用改良版 Richmond 躁动-镇静量表测量的镇静/麻醉水平之间的关系。将眨眼反射添加到其他变量中可能允许更客观地评估失去反应性的确切时刻，并对麻醉诱导采取更个性化的方法。
肌电图衍生特征被证明是估计不同镇静/麻醉水平的良好预测因子。多项分析的结果表明，该模型具有较好的性能，解释了改良版 Richmond 躁动-镇静量表变异的近 70%。该模型的整体预测准确性为 73.6%，表明其对预测失去反应性有用。
我们开发的模型是基于眨眼反射反应的肌电图衍生特征的信息。它能够预测全身麻醉患者的药物作用，这有助于麻醉师减少患者之间观察到的巨大变异性，并避免许多过量用药和相关风险。尽管如此，仍需要进一步的研究来考虑患者临床反应的变异性以及异丙酚和瑞芬太尼之间的相互作用。尽管如此，一种能够自动预测/检测失去反应性的方法是迈向个性化医疗的一步。
