Remimazolam has been suggested to improve the maintenance of hemodynamic stability when compared with other agents used for general anesthesia. This study aimed to compare the effects of remimazolam and sevoflurane anesthesia on hemodynamic stability in patients undergoing robotic gastrectomy. We retrospectively reviewed the electronic medical records of 199 patients who underwent robotic gastrectomy with sevoflurane (n = 135) or remimazolam (n = 64) anesthesia from January to November 2021. Propensity scores were used for 1:1 matching between the groups. The primary outcome was the difference in use of intraoperative vasopressors between groups. Secondary outcomes included differences in incidence and dose of vasopressors, as well as intraoperative hemodynamic variables, between groups. Remimazolam anesthesia was associated with a significantly less frequent use of ephedrine (odds ratio (OR): 0.13; 95% confidence interval (CI): 0.05–0.38, p < 0.001), phenylephrine (OR: 0.12; 95% CI: 0.04–0.40, p < 0.001), and any vasopressor (OR: 0.06; 95% CI: 0.02–0.25, p < 0.001) compared with sevoflurane anesthesia. Remimazolam anesthesia enables better maintenance of hemodynamic stability than sevoflurane anesthesia. Thus, remimazolam anesthesia may be beneficial for patients who are expected to experience hypotension due to the combined effects of CO₂ pneumoperitoneum and the head-up position utilized during robotic gastrectomy.

Chae D. Transl Clin Pharmacol. 2023 Nov;31:e17. https://doi.org/10.12793/tcp.2023.31.e17

Machine learning (ML) is revolutionizing anesthesiology research. Unlike classical research methods that are largely inference-based, ML is geared more towards making accurate predictions. ML is a field of artificial intelligence concerned with developing algorithms and models to perform prediction tasks in the absence of explicit instructions. Most ML applications, despite being highly variable in the topics that they deal with, generally follow a common workflow. For classification tasks, a researcher typically tests various ML models and compares the predictive performance with the reference logistic regression model. The main advantage of ML lies in its ability to deal with many features with complex interactions and its specific focus on maximizing predictive performance. However, emphasis on data-driven prediction can sometimes neglect mechanistic understanding. This article mainly focuses on the application of supervised ML to electronic health record (EHR) data. The main limitation of EHR-based studies is in the difficulty of establishing causal relationships. However, the associated low cost and rich information content provide great potential to uncover hitherto unknown correlations. In this review, the basic concepts of ML are introduced along with important terms that any ML researcher should know. Practical tips regarding the choice of software and computing devices are also provided. Towards the end, several examples of successful ML applications in anesthesiology are discussed. The goal of this article is to provide a basic roadmap to novice ML researchers working in the field of anesthesiology.

The incidence of major hemorrhage and transfusion during liver transplantation has decreased significantly over the past decade, but major bleeding remains a common expectation. Massive intraoperative hemorrhage during liver transplantation can lead to mortality or reoperation. This study aimed to develop machine learning models for the prediction of massive hemorrhage and a scoring system which is applicable to new patients. Data were retrospectively collected from patients aged >18 years who had undergone liver transplantation. These data included emergency information, donor information, demographic data, preoperative laboratory data, the etiology of hepatic failure, the Model for End-stage Liver Disease (MELD) score, surgical history, antiplatelet therapy, continuous renal replacement therapy (CRRT), the preoperative dose of vasopressor, and the estimated blood loss (EBL) during surgery. The logistic regression model was one of the best-performing machine learning models. The most important factors for the prediction of massive hemorrhage were the disease etiology, activated partial thromboplastin time (aPTT), operation duration, body temperature, MELD score, mean arterial pressure, serum creatinine, and pulse pressure. The risk-scoring system was developed using the odds ratios of these factors from the logistic model. The risk-scoring system showed good prediction performance and calibration (AUROC: 0.775, AUPR: 0.753).