PDF(Pubmed)
Abstract：
Response surface models (RSMs) are a new trend in modern anesthesia. RSMs have demonstrated significant applicability in the field of anesthesia. However, the comparative analysis between RSMs and logistic regression (LR) in different surgeries remains relatively limited in the current literature. We hypothesized that using a total intravenous anesthesia (TIVA) technique with the response surface model (RSM) and logistic regression (LR) would predict the emergence from anesthesia in patients undergoing video-assisted thoracotomy surgery (VATS). This study aimed to prove that LR, like the RSM, can be used to improve patient safety and achieve enhanced recovery after surgery (ERAS). This was a prospective, observational study with data reanalysis. Twenty-nine patients (American Society of Anesthesiologists (ASA) class II and III) who underwent VATS for elective pulmonary or mediastinal surgery under TIVA were enrolled. We monitored the emergence from anesthesia, and the precise time point of regained response (RR) was noted. The influence of varying concentrations was examined and incorporated into both the RSM and LR. The receiver operating characteristic (ROC) curve area for Greco and LR models was 0.979 (confidence interval: 0.987 to 0.990) and 0.989 (confidence interval: 0.989 to 0.990), respectively. The two models had no significant differences in predicting the probability of regaining response. In conclusion, the LR model was effective and can be applied to patients undergoing VATS or other procedures of similar modalities. Furthermore, the RSM is significantly more sophisticated and has an accuracy similar to that of the LR model; however, the LR model is more accessible. Therefore, the LR model is a simpler tool for predicting arousal in patients undergoing VATS under TIVA with Remifentanil and Propofol.
摘要：
响应面模型（RSM）是现代麻醉的新趋势。RSM在麻醉领域表现出显著的适用性。然而,RSM和logistic回归（LR）在不同手术中的比较分析在当前文献中仍然相对有限。我们假设使用具有响应面模型（RSM）和逻辑回归（LR）的全静脉麻醉（TIVA）技术可以预测接受电视辅助开胸手术（VATS）的患者麻醉的出现。本研究旨在证明LR，像RSM一样，可用于提高患者安全性并实现手术后恢复（ERAS）。这是一个潜在的，观察性研究与数据再分析。纳入了29名患者（美国麻醉医师协会（ASA）II级和III级），他们在TIVA下接受了VATS进行选择性肺或纵隔手术。我们监测了麻醉的出现，并记录了确切的恢复反应时间点（RR）。检查了不同浓度的影响，并将其纳入RSM和LR。Greco和LR模型的受试者工作特征（ROC）曲线面积分别为0.979（置信区间：0.987至0.990）和0.989（置信区间：0.989至0.990），分别。这两个模型在预测恢复反应的概率方面没有显着差异。总之,LR模式有效,可应用于接受VATS或其他类似模式手术的患者.此外，RSM明显更复杂，精度与LR模型相似；然而，LR模型更易于访问。因此,LR模型是一种更简单的工具,可以预测在TIVA联合瑞芬太尼和丙泊酚下接受VATS的患者的觉醒.
