Anesthesia induction is associated with frequent blood pressure fluctuation such as hypotension and hypertension. If it is possible to precisely predict blood pressure a few minutes ahead, anesthesiologists can proactively give anesthetic management before patients develop hemodynamic problem. The objective of this study is to develop a real-time model for predicting 3-min-ahead blood pressure from the start of anesthesia induction to surgical incision. We used only vital signs and anesthesia-related data obtained during anesthesia-induction phase and designed a bidirectional recurrent neural network followed by fully connected layers. We conducted experiments on our collected data of 102 patients, and obtained mean absolute errors between 8.2 mmHg and 11.1 mmHg and standard deviation between 8.7 mmHg and 12.7 mmHg. The average elapsed time for prediction of a batch of 100 unseen data was about 26.56 milliseconds. We believe that this study shows feasibility of real-time prediction of future blood pressures, and the performance will be improved by collecting more data and finding better model structures.

Allele and genotype frequencies for four tetrameric short tandem repeat (STR) loci, HumFES/FPS, HumFOLP23, HumGABRB15, and HumCYAR04, have been determined by polymerase chain reaction (PCR) amplification and subsequent polyacrylamide gel electrophoresis from approximately 200 genetically unrelated Koreans. This method allows a single base pair resolution and rapid typing with silver staining. The allele and genotype distributions satisfy Hardy-Weinberg expectation. Also, these STR loci have proven to be useful for forensic analyses and paternity tests in which the variable number of tandem repeat (VNTR) loci have some limitations.

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BackgroundThe word "geop" is a unique Korean term commonly used to describe fright, fear and anxiety, and similar concepts. The purpose of this pilot study is to examine the correlation between the Numeric Rating Scale (NRS) score of geop and three different questionnaires on pain perception.MethodsPatients aged 20 to 70 years who visited our outpatient pain clinics were evaluated. They were requested to rate the NRS score (range: 0-100) if they felt geop. Next, they completed questionnaires on pain perception, in this case the Korean version of the Pain Sensitivity Questionnaire (PSQ), the Pain Catastrophizing Scale (PCS), and the Pain Anxiety Symptoms Scale (PASS). The correlations among each variable were evaluated by statistical analyses.ResultsThere was no statistically significant correlation between the NRS score of geop and the PSQ score (r = 0.075, P = 0.5605). The NRS score of geop showed a significant correlation with the PCS total score (r = 0.346, P = 0.0063). Among the sub-scales, Rumination (r = 0.338, P = 0.0077) and Magnification (r = 0.343, P = 0.0069) were correlated with the NRS score of geop. In addition, the NRS score of geop showed a significant correlation with the PASS total score (r = 0.475, P = 0.0001). The cognitive (r = 0.473, P = 0.0002) and fear factors (r = 0.349, P = 0.0063) also showed significant correlations with the NRS score of geop.ConclusionsThis study marks the first attempt to introduce the concept of "geop." The NRS score of geop showed a moderate positive correlation with the total PCS and PASS score. However, further investigations are required before the "geop" concept can be used practically in clinical fields.

Background Intraoperative hypertension and blood pressure (BP) fluctuation are known to be associated with negative patient outcomes. During robotic lower abdominal surgery, the patient’s abdominal cavity is filled with CO2, and the patient’s head is steeply positioned toward the floor (Trendelenburg position). Pneumoperitoneum and the Trendelenburg position together with physiological alterations during anesthesia, interfere with predicting BP changes. Recently, deep learning using recurrent neural networks (RNN) was shown to be effective in predicting intraoperative BP. A model for predicting BP rise was designed using RNN under special scenarios during robotic laparoscopic surgery and its accuracy was tested. Methods Databases that included adult patients (over 19 years old) undergoing low abdominal da Vinci robotic surgery (ovarian cystectomy, hysterectomy, myomectomy, prostatectomy, and salpingo-oophorectomy) at Soonchunhyang University Bucheon Hospital from October 2018 to March 2021 were used. An RNN-based model was designed using Python3 language with the PyTorch packages. The model was trained to predict whether hypertension (20% increase in the mean BP from baseline) would develop within 10 minutes after pneumoperitoneum. Results Eight distinct datasets were generated and the predictive power was compared. The macro-average F1 scores of the datasets ranged from 68.18% to 72.33%. It took only 3.472 milliseconds to obtain 39 prediction outputs. Conclusions A prediction model using the RNN may predict BP rises during robotic laparoscopic surgery.

Background: The reversal of a neuromuscular blockade has typically been achieved with a cholinesterase inhibitor and the concomitant use of an anticholinergic agent, and this remains a popular method. Since the introduction of sugammadex in the market, its use has been increasing because of the rapid recovery from a neuromuscular blockade achieved by rocuronium. The occurrence of anaphylaxis or an anaphylactic reaction resulting from sugammadex is rare and has been reported sparsely. Thus, one may not recognize the possibility of sugammadex-induced hypersensitivity when sudden lifethreatening hypotension occurs, especially without skin manifestations during the emergence of anesthesia. This may delay treatment and increase morbidity.Case: We report a case of a sugammadex-related hypersensitivity reaction which manifested as pure cardiovascular collapse during the emergence of anesthesia. Conclusions:We emphasize that vigilance should be paid for at least five minutes following sugammadex administration in daily clinical practice.

General anesthesia is associated with a risk for postoperative pulmonary complications. The risk is even higher in patients with chronic respiratory failure, and postoperative mortality rates are high. Proper perioperative anesthetic management is important in such patients. Therefore, it is essential to optimize the patient’s physical status before anesthesia and to determine the optimal anesthesia technique based on the pre-anesthesia evaluation of the patient’s pulmonary function. We successfully performed abdominal surgery under spinal anesthesia in a patient with severe chronic respiratory failure.

Antimicrobial filters that prevent cross-contamination through anesthesia equipment are commonly used in operating rooms. Occlusion of this filter leads to the patient' s airway obstruction, which may lead to fatal outcomes. We report a case of the airway obstruction by antimicrobial filter occlusion during general anesthesia, and symptoms recovered immediately after removal of the filter.