Arterial hypotension during the early phase of anesthesia can lead to adverse outcomes such as a prolonged postoperative stay or even death. Predicting hypotension during anesthesia induction is complicated by its diverse causes. We investigated the feasibility of developing a machine-learning model to predict postinduction hypotension. Naïve Bayes, logistic regression, random forest, and artificial neural network models were trained to predict postinduction hypotension, occurring between tracheal intubation and incision, using data for the period from between the start of anesthesia induction and immediately before tracheal intubation obtained from an anesthesia monitor, a drug administration infusion pump, an anesthesia machine, and from patients’ demographics, together with preexisting disease information from electronic health records. Among 222 patients, 126 developed postinduction hypotension. The random-forest model showed the best performance, with an area under the receiver operating characteristic curve of 0.842 (95% confidence interval [CI]: 0.736-0.948). This was higher than that for the Naïve Bayes (0.778; 95% CI: 0.65-0.898), logistic regression (0.756; 95% CI: 0.630-0.881), and artificial-neural-network (0.760; 95% CI: 0.640-0.880) models. The most important features affecting the accuracy of machine-learning prediction were a patient’s lowest systolic blood pressure, lowest mean blood pressure, and mean systolic blood pressure before tracheal intubation. We found that machine-learning models using data obtained from various anesthesia machines between the start of anesthesia induction and immediately before tracheal intubation can predict hypotension occurring during the period between tracheal intubation and incision.

Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation. Vital records and electronic health records (EHR) for 282 of 319 patients who underwent laparoscopic cholecystectomy from October 2018 to July 2019 were collected. Among the 282 patients, 151 developed post-induction hypotension. Our experiments had two scenarios: using raw vital records and feature engineering on vital records. The experiments on raw data showed that CNN had the best accuracy of 72.63%, followed by random forest (70.32%) and Xgboost (64.6%). The experiments on feature engineering showed that random forest combined with feature selection had the best accuracy of 74.89%, while CNN had a lower accuracy of 68.95% than that of the experiment on raw data. Our study is an extension of previous studies to detect hypotension before intubation with a one-minute advance. To improve accuracy, we built a model using state-of-art algorithms. We found that CNN had a good performance, but that random forest had a better performance when combined with feature selection. In addition, we found that the examination period (data period) is also important.

Arterial hypotension is associated with incidence of postoperative complications, such as myocardial infarction or acute kidney injury. Little research has been conducted for the real-time prediction of hypotension, even though many studies have been performed to investigate the factors which affect hypotension events. This forecasting problem is quite challenging compared to diagnosis that detects high-risk patients at current. The forecasting problem that specifies when events occur is more challenging than the forecasting problem that does not specify the event time. In this work, we challenge the forecasting problem in 5 min advance. For that, we aim to build a systematic feature engineering method that is applicable regardless of vital sign species, as well as a machine learning model based on these features for real-time predictions 5 min before hypotension. The proposed feature extraction model includes statistical analysis, peak analysis, change analysis, and frequency analysis. After applying feature engineering on invasive blood pressure (IBP), we build a random forest model to differentiate a hypotension event from other normal samples. Our model yields an accuracy of 0.974, a precision of 0.904, and a recall of 0.511 for predicting hypotensive events.

Considering the functional role of red blood cells (RBC) in maintaining oxygen supply to tissues, RBC transfusion can be a life-saving intervention in situations of severe bleeding or anemia. RBC transfusion is often inevitable to address intraoperative massive bleeding; it is a key component in safe perioperative patient management. Unlike general medical resources, packed RBCs (pRBCs) have limited availability because their supply relies entirely on voluntary donations. Additionally, excessive utilization of pRBCs may aggravate prognosis or increase the risk of developing infectious diseases. Appropriate perioperative RBC transfusion is, therefore, crucial for the management of patient safety and medical resource conservation. These concerns motivated us to develop the present clinical practice guideline for evidence-based efficient and safe perioperative RBC transfusion management considering the current clinical landscape. This guideline was obtained after the revision and refinement of exemplary clinical practice guidelines developed in advanced countries. This was followed by rigorous evidence-based reassessment considering the healthcare environment of the country. This guideline covers all important aspects of perioperative RBC transfusion, such as preoperative anemia management, appropriate RBC storage period, and leukoreduction (removal of white blood cells using filters), reversal of perioperative bleeding tendency, strategies for perioperative RBC transfusion, appropriate blood management protocols, efforts to reduce blood transfusion requirements, and patient monitoring during a perioperative transfusion. This guideline will aid decisions related to RBC transfusion in healthcare settings and minimize patient risk associated with unnecessary pRBC transfusion.

BACKGROUND: Previous research has not evaluated the potential effect of transversus abdominis plane (TAP) block on quality of recovery following laparoscopic cholecystectomy. Therefore, we investigated whether addition of the bilateral subcostal and lateral TAP (bilateral dual TAP [BD-TAP]) blocks to multimodal analgesia would improve the quality of recovery as assessed with the Quality of Recovery-40 (QoR-40). METHODS: Patients age 18 to 60 years who were scheduled to undergo elective laparoscopic cholecystectomy were randomized to the BD-TAP or control group. The BD-TAP group received the BD-TAP block with multimodal analgesia under general anesthesia, using 0.25% ropivacaine, and the control group was treated with the same method, except that they received the sham block using 0.9% normal saline. Both groups had the same multimodal analgesia regimen, consisting of intravenous dexamethasone, propacetamol, ibuprofen, and oxycodone. The primary outcome was the QoR-40 score at 24 hours after surgery. Data were analyzed using the independent t test, Mann-Whitney U test, χ² test, and Fisher exact test. RESULTS: Thirty-eight patients in each group were recruited. The mean QoR-40 score decreased by 13.6 (95% confidence interval [CI], 8.3–18.8) in the BD-TAP group and 15.6 (95% CI, 6.7–24.5) in the control group. The postoperative QoR-40 score at 24 hours after surgery did not differ between the 2 groups (BD-TAP group, median [interquartile range], 170.5 [152–178]; control group, 161 [148–175]; median difference, 3 [95% CI, −5 to 13]; P = .427). There were no differences between the 2 groups in the pain dimension of the QoR-40: 30.5 (95% CI, 27–33) in the BD-TAP group and 31 (95% CI, 26–32) in the control group; median difference was 0 (95% CI, −2 to 2); P = .77. CONCLUSIONS: Our results indicate that the BD-TAP block does not improve the quality of recovery or analgesic outcomes following laparoscopic cholecystectomy. Our results do not support the routine use of the BD-TAP block for this surgery.