Enhanced recovery after surgery: Prediction for early extubation in video-assisted thoracic surgery using a response surface model in anesthesia

Abstract

Background/Purpose: Enhanced recovery after surgery (ERAS) is a growing tendency in modern perioperative period management, but no protocol has been established for a strategy that optimally facilitates rapid recovery from anesthesia. We hypothesized that applying a total intravenous anesthesia (TIVA) method to the response surface model (RSM) would allow prediction of the emergence and endotracheal tube extubation in cases undergoing video-assisted thoracotomy surgery (VATS). Methods: Thirty patients who were scheduled to undergo VATs under TIVA were enrolled. Pharmacokinetic profiles were calculated using a Tivatrainer. Emergence from anesthesia was observed and the exact time point of the regained response (RR) was recorded. The effect of concentration was analyzed and applied to a response surface model. Results: The cumulative prediction curve of the RR was closer to the 50% probability as set by the OAA/S ≥ 4 than by the OAA/S ≥ 2 model. The median, averages, and standard deviations of the time differences were 14.5, 22.05 ± 19.23 min for the OAA/S ≥2 model and 10.4, 14.26 ± 10.40 min for the OAA/S ≥ 4 model. Conclusion: The OAA/S ≥ 4 model could identify the target concentration in propofol–remifentanil pairs that predicted the time of emergence from VATS in 10 min. Our results indicate that RSM can be used to derive an ERAS protocol for VATS under TIVA. Further studies should investigate application of RSM to predict ERAS for various types of procedures. Keywords: Enhanced recovery after surgery (ERAS), Response surface model, Total intravenous anesthesia (TIVA), Video-assisted thoracotomy surgery (VATS)