Development of a machine learning approach for prediction of red blood cell transfusion in patients undergoing Cesarean section at a single institution.

Despite recent advances in surgical techniques and perinatal management in obstetrics for reducing intraoperative bleeding, blood transfusion may occur during a cesarean section (CS). This study aims to identify machine learning models with an optimal diagnostic performance for intraoperative transfusion prediction in parturients undergoing a CS. Additionally, to address model performance degradation due to data imbalance, this study further investigated the variation in predictive model performance depending on the ratio of event to non-event data (1:1, 1:2, 1:3, and 1:4 model datasets and raw data).The area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) were evaluated to compare the predictive accuracy of different machine learning algorithms, including XGBoost, K-nearest neighbor, decision tree, support vector machine, multilayer perceptron, logistic regression, random forest, and deep neural network. We compared the predictive performance of eight prediction algorithms that were applied to five types of datasets. The intraoperative transfusion in maternal CS was 7.2% (1020/14,254). XGBoost showed the highest AUROC (0.8257) and AUPRC (0.4825) among the models. The most significant predictors for transfusion in maternal CS as per machine learning models were placenta previa totalis, haemoglobin, placenta previa partialis, and platelets. In all eight prediction algorithms, the change in predictive performance based on the AUROC and AUPRC according to the resampling ratio was insignificant. The XGBoost algorithm exhibited optimal performance for predicting intraoperative transfusion. Data balancing techniques employed to alter the event data composition ratio of the training data failed to improve the performance of the prediction model.

Comparison of Hemodynamic Parameters Based on the Administration of Remimazolam or Sevoflurane in Patients under General Anesthesia in the Beach Chair Position: A Single-Blinded Randomized Controlled Trial.

Background: We aimed to evaluate whether the administration of remimazolam as a maintenance agent for general anesthesia affects the occurrence of hypotension compared with sevoflurane when switching to the beach chair position (BCP). Methods: We conducted a prospective randomized controlled trial from June 2023 to October 2023 in adult patients undergoing orthopedic surgery under general anesthesia in the BCP. A total of 78 participants were randomly allocated to the remimazolam (R) or sevoflurane (S) groups. The primary outcome was the incidence of hypotension that occurred immediately after switching to a BCP. The secondary outcomes included differences between the study groups in perioperative blood pressure (BP), heart rate (HR), endotracheal tube extubation time, postoperative complications, and hospital length of stay (LOS). Results: The incidence of hypotension immediately after switching to a BCP was significantly higher in the S group. The risk factors associated with hypotension included sevoflurane administration and a high baseline systolic BP. In the receiver operating characteristic curve analysis for the occurrence of hypotension after the transition to a BCP, the cutoff value for systolic BP was 142 mmHg. The perioperative BP and HR were higher in the R group at several timepoints. Postoperative endotracheal tube extubation time was shorter in the R group. There were no significant differences in the postoperative complications or hospital LOS between the two groups. Conclusions: Remimazolam should be considered as an anesthetic agent to prevent hypotension when switching to BCP, and hypotension may occur frequently in patients with high baseline BP.