Effect of an opioid-free anesthetic on postoperative opioid consumption after laparoscopic bariatric surgery: a prospective, single-blinded, randomized controlled trial.

INTRODUCTION: Opioid administration has the benefit of providing perioperative analgesia but is also associated with adverse effects. Opioid-free anesthesia (OFA) may reduce postoperative opioid consumption and adverse effects after laparoscopic bariatric surgery. In this randomized controlled study, we hypothesized that an opioid-free anesthetic using lidocaine, ketamine, and dexmedetomidine would result in a clinically significant reduction in 24-hour postoperative opioid consumption when compared with an opioid-inclusive technique. METHODS: Subjects presenting for laparoscopic or robotic bariatric surgery were randomized in a 1:1 ratio to receive either standard opioid-inclusive anesthesia (group A: control) or OFA (group B: OFA). The primary outcome was opioid consumption in the first 24 hours postoperatively in oral morphine equivalents (OMEs). Secondary outcomes included postoperative pain scores, patient-reported incidence of opioid-related adverse effects, hospital length of stay, patient satisfaction, and ongoing opioid use at 1 and 3 months after hospital discharge. RESULTS: 181 subjects, 86 from the control group and 95 from the OFA group, completed the study per protocol. Analysis of the primary outcome showed no significant difference in total opioid consumption at 24 hours between the two treatment groups (control: 52 OMEs vs OFA: 55 OMEs, p=0.49). No secondary outcomes showed statistically significant differences between groups. CONCLUSIONS: This study demonstrates that an OFA protocol using dexmedetomidine, ketamine, and lidocaine for laparoscopic or robotic bariatric surgery was not associated with a reduction in 24-hour postoperative opioid consumption when compared with an opioid-inclusive technique using fentanyl.

Evaluation of machine learning models as decision aids for anesthesiologists.

Machine Learning (ML) models have been developed to predict perioperative clinical parameters. The objective of this study was to determine if ML models can serve as decision aids to improve anesthesiologists' prediction of peak intraoperative glucose values and postoperative opioid requirements. A web-based tool was used to present actual surgical case and patient information to 10 practicing anesthesiologists. They were asked to predict peak glucose levels and post-operative opioid requirements for 100 surgical patients with and without presenting ML model estimations of peak glucose and opioid requirements. The accuracies of the anesthesiologists' estimates with and without ML estimates as reference were compared. A questionnaire was also sent to the participating anesthesiologists to obtain their feedback on ML decision support. The accuracy of peak glucose level estimates by the anesthesiologists increased from 79.0 ± 13.7% without ML assistance to 84.7 ± 11.5% (< 0.001) when ML estimates were provided as reference. The accuracy of opioid requirement estimates increased from 18% without ML assistance to 42% (p < 0.001) when ML estimates were provided as reference. When ML estimates were provided, predictions of peak glucose improved for 8 out of the 10 anesthesiologists, while predictions of opioid requirements improved for 7 of the 10 anesthesiologists. Feedback questionnaire responses revealed that the anesthesiologist primarily used the ML estimates as reference to modify their clinical judgement. ML models can improve anesthesiologists' estimation of clinical parameters. ML predictions primarily served as reference information that modified an anesthesiologist's clinical estimate.