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Abstract Delirium can result in undesirable outcomes including increased length of stays and mortality in patients admitted to the intensive care unit (ICU). Dexmedetomidine has emerged for delirium prevention in these patients; however, optimal dosing is challenging. A reinforcement learning-based Artificial Intelligence model for Delirium prevention (AID) is proposed to optimize dexmedetomidine dosing. The model was developed and internally validated using 2416 patients (2531 ICU admissions) and externally validated on 270 patients (274 ICU admissions). The estimated performance return of the AID policy was higher than that of the clinicians’ policy in both derivation (0.390 95% confidence interval [CI] 0.361 to 0.420 vs. −0.051 95% CI −0.077 to −0.025) and external validation (0.186 95% CI 0.139 to 0.236 vs. −0.436 95% CI −0.474 to −0.402) cohorts. Our finding indicates that AID might support clinicians’ decision-making regarding dexmedetomidine dosing to prevent delirium in ICU patients, but further off-policy evaluation is required.

Abstract The American Society of Anesthesiologist’s Physical Status (ASA-PS) classification system assesses comorbidities before sedation and analgesia, but inconsistencies among raters have hindered its objective use. This study aimed to develop natural language processing (NLP) models to classify ASA-PS using pre-anesthesia evaluation summaries, comparing their performance to human physicians. Data from 717,389 surgical cases in a tertiary hospital (October 2004–May 2023) was split into training, tuning, and test datasets. Board-certified anesthesiologists created reference labels for tuning and test datasets. The NLP models, including ClinicalBigBird, BioClinicalBERT, and Generative Pretrained Transformer 4, were validated against anesthesiologists. The ClinicalBigBird model achieved an area under the receiver operating characteristic curve of 0.915. It outperformed board-certified anesthesiologists with a specificity of 0.901 vs. 0.897, precision of 0.732 vs. 0.715, and F1-score of 0.716 vs. 0.713 (all p

Abstract Titrating tacrolimus concentration in liver transplantation recipients remains a challenge in the early post-transplant period. This multicenter retrospective cohort study aimed to develop and validate a machine-learning algorithm to predict tacrolimus concentration. Data from 443 patients undergoing liver transplantation between 2017 and 2020 at an academic hospital in South Korea were collected to train machine-learning models. Long short-term memory (LSTM) and gradient-boosted regression tree (GBRT) models were developed using time-series doses and concentrations of tacrolimus with covariates of age, sex, weight, height, liver enzymes, total bilirubin, international normalized ratio, albumin, serum creatinine, and hematocrit. We conducted performance comparisons with linear regression and populational pharmacokinetic models, followed by external validation using the eICU Collaborative Research Database collected in the United States between 2014 and 2015. In the external validation, the LSTM outperformed the GBRT, linear regression, and populational pharmacokinetic models with median performance error (8.8%, 25.3%, 13.9%, and − 11.4%, respectively; P

Abstract We present the INSPIRE dataset, a publicly available research dataset in perioperative medicine, which includes approximately 130,000 surgical operations at an academic institution in South Korea over a ten-year period between 2011 and 2020. This comprehensive dataset includes patient characteristics such as age, sex, American Society of Anesthesiologists physical status classification, diagnosis, surgical procedure code, department, and type of anaesthesia. The dataset also includes vital signs in the operating theatre, general wards, and intensive care units (ICUs), laboratory results from six months before admission to six months after discharge, and medication during hospitalisation. Complications include total hospital and ICU length of stay and in-hospital death. We hope this dataset will inspire collaborative research and development in perioperative medicine and serve as a reproducible external validation dataset to improve surgical outcomes.

Background Perioperative stroke is a severe complication following surgery. To identify patients at risk for perioperative stroke, several prediction models based on the preoperative factors were suggested. Prediction models often focus on preoperative patient characteristics to assess stroke risk. However, most existing models primarily base their predictions on the patient's baseline characteristics before surgery. We aimed to develop a machine‐learning model incorporating both pre‐ and intraoperative variables to predict perioperative stroke. Methods and Results This study included patients who underwent noncardiac surgery at 2 hospitals with the data of 15 752 patients from Seoul National University Hospital used for development and temporal internal validation, and the data of 449 patients from Boramae Medical Center used for external validation. Perioperative stroke was defined as a newly developed ischemic lesion on diffusion‐weighted imaging within 30 days of surgery. We developed a prediction model composed of pre‐ and intraoperative factors (integrated model) and compared it with a model consisting of preoperative features alone (preoperative model). Perioperative stroke developed in 109 (0.69%) patients in the Seoul National University Hospital group and 11 patients (2.45%) in the Boramae Medical Center group. The integrated model demonstrated superior predictive performance with area under the curve values of 0.824 (95% CI, 0.762–0.880) versus 0.584 (95% CI, 0.499–0.667; P

Abstract This study evaluated the effect of hyperbilirubinemia on the accuracy of continuous non-invasive hemoglobin (SpHb) measurements in liver transplantation recipients. Overall, 1465 SpHb and laboratory hemoglobin (Hb) measurement pairs (n = 296 patients) were analyzed. Patients were grouped into normal ( 3.0 mg/dL) hyperbilirubinemia groups based on the preoperative serum total bilirubin levels. Bland–Altman analysis showed a bias of 0.20 (95% limit of agreement, LoA: − 2.59 to 3.00) g/dL, 0.98 (95% LoA: − 1.38 to 3.35) g/dL, and 1.23 (95% LoA: − 1.16 to 3.63) g/dL for the normal, mild-to-moderate, and severe groups, respectively. The four-quadrant plot showed reliable trending ability in all groups (concordance rate > 92%). The rates of possible missed transfusion (SpHb > 7.0 g/dL for Hb

Background Use of endotracheal tubes (ETTs) with appropriate size and depth can help minimize intubation-related complications in pediatric patients. Existing age-based formulae for selecting the optimal ETT size present several inaccuracies. We developed a machine learning model that predicts the optimal size and depth of ETTs in pediatric patients using demographic data, enabling clinical applications. Methods Data from 37,057 patients younger than 12 years who underwent general anesthesia with endotracheal intubation were retrospectively analyzed. Gradient boosted regression tree (GBRT) model was developed and compared with traditional age-based formulae. Results The GBRT model demonstrated the highest macro-averaged F1 scores of 0.502 (95% CI [0.486, 0.568]) and 0.669 (95% CI [0.640, 0.694]) for predicting the uncuffed and cuffed ETT size (internal diameter), outperforming the age-based formulae that yielded 0.163 (95% CI [0.140, 0.196], P < 0.001) and 0.392 (95% CI [0.378, 0.406], P < 0.001), respectively. In predicting the ETT depth (distance from tip to lip corner), the GBRT model showed the lowest mean absolute error of 0.71 cm (95% CI [0.69, 0.72]) and 0.72 cm (95% CI [0.70, 0.74]) compared to the age-based formulae that showed an error of 1.18 cm (95% CI [1.16, 1.20], P < 0.001) and 1.34 cm (95% CI [1.31, 1.38], P < 0.001) for uncuffed and cuffed ETT, respectively. Conclusions The GBRT model using only demographic data accurately predicted the ETT size and depth. If these results are validated, the model may be practical for predicting optimal ETT size and depth for pediatric patients.

Abstract Background Based on the controversy surrounding pulmonary artery catheterization (PAC) in surgical patients, we investigated the interchangeability of cardiac index (CI) and systemic vascular resistance (SVR) measurements between ClearSight™ and PAC during living-donor liver transplantation (LDLT). Methods This prospective study included consecutively selected LDLT patients. ClearSight™-based CI and SVR measurements were compared with those from PAC at seven LDLT-stage time points. ClearSight™-based systolic (SAP), mean (MAP), and diastolic (DAP) arterial pressures were also compared with those from femoral arterial catheterization (FAC). For the comparison and analysis of ClearSight™ and the reference method, Bland-Altman analysis was used to analyze accuracy while polar and four-quadrant plots were used to analyze the trending ability. Results From 27 patients, 189 pairs of ClearSight™ and reference values were analyzed. The CI and SVR performance errors (PEs) exhibited poor accuracy between the two methods (51.52 and 51.73%, respectively) in the Bland-Altman analysis. CI and SVR also exhibited unacceptable trending abilities in both the polar and four-quadrant plot analyses. SAP, MAP, and DAP PEs between the two methods displayed favorable accuracy (24.28, 21.18, and 26.26%, respectively). SAP and MAP exhibited acceptable trending ability in the four-quadrant plot between the two methods, but not in the polar plot analyses. Conclusions During LDLT, CI and SVR demonstrated poor interchangeability, while SAP and MAP exhibited acceptable interchangeability between ClearSight™ and FAC.

Abstract Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosting machine (LGBM) algorithm was used to develop the proposed model for predicting in-hospital cardiac arrest within 0.5–24 h. The LGBM model using 33 HRV measures achieved an area under the receiver operating characteristic curve of 0.881 (95% CI: 0.875–0.887) and an area under the precision-recall curve of 0.104 (95% CI: 0.093–0.116). The most important feature was the baseline width of the triangular interpolation of the RR interval histogram. As our model uses only ECG data, it can be easily applied in clinical practice.

Abstract Neurological pupil index (NPi) calculated by automated pupillometry predicts clinical outcomes in critically ill patients. However, there are few data on intraoperative NPi and postoperative outcome after cardiac surgery. We evaluated the relationships between intraoperative NPi and clinical outcomes, such as delirium, in cardiac surgery patients. NPi was measured at baseline, after anesthesia induction, at 30 min intervals after initiation of cardiopulmonary bypass or anastomosis of coronary artery bypass graft, and at skin closure. Abnormal NPi was defined as one or more measurements of NPi

Abstract Ventilation should be assisted without asynchrony or cardiorespiratory instability during anesthesia emergence until sufficient spontaneous ventilation is recovered. In this multicenter cohort study, we develop and validate a reinforcement learning-based Artificial Intelligence model for Ventilation control during Emergence (AIVE) from general anesthesia. Ventilatory and hemodynamic parameters from 14,306 surgical cases at an academic hospital between 2016 and 2019 are used for training and internal testing of the model. The model’s performance is also evaluated on the external validation cohort, which includes 406 cases from another academic hospital in 2022. The estimated reward of the model’s policy is higher than that of the clinicians’ policy in the internal (0.185, the 95% lower bound for best AIVE policy vs. −0.406, the 95% upper bound for clinicians’ policy) and external validation (0.506, the 95% lower bound for best AIVE policy vs. 0.154, the 95% upper bound for clinicians’ policy). Cardiorespiratory instability is minimized as the clinicians’ ventilation matches the model’s ventilation. Regarding feature importance, airway pressure is the most critical factor for ventilation control. In conclusion, the AIVE model achieves higher estimated rewards with fewer complications than clinicians’ ventilation control policy during anesthesia emergence.

With the growing interest of researchers in machine learning and artificial intelligence (AI) based on large data, their roles in medical research have become increasingly prominent. Despite the proliferation of predictive models in perioperative medicine, external validation is lacking. Open datasets, defined as publicly available datasets for research, play a crucial role by providing high-quality data, facilitating collaboration, and allowing an objective evaluation of the developed models. Among the available datasets for surgical patients, VitalDB has been the most widely used, with the Medical Informatics Operating Room Vitals and Events Repository recently launched and the Informative Surgical Patient dataset for Innovative Research Environment expected to be released soon. For critically ill patients, the available resources include the Medical Information Mart for Intensive Care, the eICU Collaborative Research Database, the Amsterdam University Medical Centers Database, and the High time Resolution ICU Dataset, with the anticipated release of the Intensive Care Network with Million Patients' information for the AI Clinical decision support system Technology dataset. This review presents a detailed comparison of each to enrich our understanding of these open datasets for data science and AI research in perioperative medicine.

Abstract Blocking the abrupt increase in systolic blood pressure associated with autonomic response during bladder hydrodistention in patients with interstitial cystitis/bladder pain syndrome (IC/BPS) is essential for patient safety. We conducted this study to compare autonomic responses during bladder hydrodistention in patients with IC/BPS under general and spinal anaesthesia. Thirty-six patients were randomly allocated to a general anaesthesia (GA, n = 18) or a spinal anaesthesia (SA, n = 18) group. Blood pressure and heart rate were measured continuously and ΔSBP, defined as maximum increases in SBP during bladder hydrodistention from baseline, was compared between groups. Heart rate variability was analysed using electrocardiograms. The post-anaesthesia care unit assessed postoperative pain using a numeric (0–10) rating scale. Our analyses yield a significantly greater ΔSBP (73.0 [26.0–86.1] vs. 2.0 [− 4.0 to 6.0] mmHg), a significantly lower root-mean-square of successive differences in heart rate variability after bladder hydrodistention (10.8 [7.7–19.8] vs. 20.6 [15.1–44.7] ms), and significantly higher postoperative pain scores (3.5 [0.0–5.5] vs. 0.0 [0.0–0.0]) in the GA compared to the SA group. These findings suggest that SA has advantages over GA for bladder hydrodistention in preventing an abrupt increase in SBP and postoperative pain in IC/BPS patients.

Abstract Continuous, comfortable, convenient (C3), and accurate blood pressure (BP) measurement and monitoring are needed for early diagnosis of various cardiovascular diseases. To supplement the limited C3 BP measurement of existing cuff-based BP technologies, though they may achieve reliable accuracy, cuffless BP measurement technologies, such as pulse transit/arrival time, pulse wave analysis, and image processing, have been studied to obtain C3 BP measurement. One of the recent cuffless BP measurement technologies, innovative machine-learning and artificial intelligence-based technologies that can estimate BP by extracting BP-related features from photoplethysmography (PPG)-based waveforms have attracted interdisciplinary attention of the medical and computer scientists owing to their handiness and effectiveness for both C3 and accurate, i.e., C3A, BP measurement. However, C3A BP measurement remains still unattainable because the accuracy of the existing PPG-based BP methods was not sufficiently justified for subject-independent and highly varying BP, which is a typical case in practice. To circumvent this issue, a novel convolutional neural network(CNN)- and calibration-based model (PPG2BP-Net) was designed by using a comparative paired one-dimensional CNN structure to estimate highly varying intrasubject BP. To this end, approximately $$70\%$$ 70 % , $$20\%$$ 20 % , and $$10\%$$ 10 % of 4185 cleaned, independent subjects from 25,779 surgical cases were used for training, validating, and testing the proposed PPG2BP-Net, respectively and exclusively (i.e., subject-independent modelling). For quantifying the intrasubject BP variation from an initial calibration BP, a novel ‘standard deviation of subject-calibration centring (SDS)’ metric is proposed wherein high SDS represents high intrasubject BP variation from the calibration BP and vice versa. PPG2BP-Net achieved accurately estimated systolic and diastolic BP values despite high intrasubject variability. In 629-subject data acquired after 20 minutes following the A-line (arterial line) insertion, low error mean and standard deviation of $$0.209\pm 7.509$$ 0.209 ± 7.509 and $$0.150\pm 4.549\;\textrm{mmHg}$$ 0.150 ± 4.549 mmHg for highly varying A-line systolic and diastolic BP values, respectively, where their SDSs are 15.375 and 8.745. This study moves one step forward in developing the C3A cuffless BP estimation devices that enable the push and agile pull services.

Abstract Background The endotracheal cuff pressure depends on the airway pressure during positive-pressure ventilation. A high endotracheal cuff pressure may be related to intraoperative coughing, which can be detrimental during neurosurgery. We investigated the incidence of intraoperative coughing and its association with peak inspiratory pressure (PIP) during neurosurgery under general anesthesia without neuromuscular blockade. Methods This retrospective study divided 1656 neurosurgical patients who underwent total intravenous anesthesia without additional neuromuscular blockade after tracheal intubation into high (PIP > 21.6 cmH2O, n = 318) and low (PIP ≤ 21.6 cmH2O, n = 1338) PIP groups. After propensity score matching, 206 patients were selected in each group. Demographic, preoperative, surgical, and anesthetic data were collected retrospectively from electronic medical records and continuous ventilator, infusion pump, and bispectral index data from a data registry. Results Intraoperative coughing occurred in 30 (1.8%) patients, including 9 (0.5%) during the main surgical procedure. Intraoperative coughing was more frequent in the high PIP group than in the low PIP group before (14/318 [4.4%] vs. 16/1338 [1.2%], P

Abstract R-peak detection is an essential step in analyzing electrocardiograms (ECGs). Previous deep learning models reported their performance primarily in a single database, and some models did not perform at the highest levels when applied to a database different from the testing database. To achieve high performances in cross-database validations, we developed a novel deep learning model for R-peak detection using stationary wavelet transform (SWT) and separable convolution. Three databases (i.e., the MIT-BIH Arrhythmia [MIT-BIH], the Institute of Cardiological Technics [INCART], and the QT) were used in both the training and testing models, and the MIT-BIH ST Change (MIT-BIH-ST), European ST-T, TELE and MIT-BIH Noise Stress Test (MIT-BIH-NST) databases were further used for testing. The detail coefficient of level 4 decomposition by SWT and the first derivative from filtered ECGs were used for model inputs, and the interval of 150 ms centered at marked peaks was used for labels. Separable convolution with atrous spatial pyramidal pooling was selected as the model’s architecture, and noise-augmented waveforms of 5.69 s duration (2048 size in 360 Hz) were used in training. The model performance was evaluated using cross-database validation. The F1 scores of the peak detection model were 0.9994, 0.9985, and 0.9999 in the MIT-BIH, INCART, and QT databases, respectively. When the above three databases were pooled, the F1 scores were 0.9993 for fivefold cross-validation and 0.9991 for cross-database validation. The model performance remained high for MIT-BIH-ST, European ST-T, and TELE, with F1 scores of 0.9995, 0.9988, and 0.9790, respectively. The model performance when trained by severe noise augmentation increased for the MIT-BIH-NST database (F1 scores from 0.9504 to 0.9759) and decreased for the MIT-BIH database (F1 scores from 0.9994 to 0.9991). The present SWT and separable convolution-based model for R-peak detection yields a high performance even for cross-database validations.

Abstract Background To evaluate the impact of intraoperative hypotension and hemodynamic instability on survival outcomes in patients with high-grade serous ovarian carcinoma (HGSOC). Methods We retrospectively identified patients with HGSOC, who underwent primary or interval debulking surgery between August 2013 and December 2019. We collected anesthesia-related variables, including the arterial blood pressure measurements (at 1-min intervals) during the surgery of patients. The cumulative duration of mean arterial blood pressure (MAP) readings under 65 mmHg and two performance measurements (median performance error [MDPE] and wobble) were calculated. We investigated associations between the factors indicating hemodynamic instability and prognosis. Results In total, 338 patients were included. Based on the cumulative duration of MAP under 65 mmHg, we divided patients into two groups: ≥30 min and

Background Post-reperfusion syndrome (PRS) results in sudden hemodynamic instability following graft reperfusion. Although PRS is known to influence outcomes following liver transplantation, little is known regarding the effects of anesthetics on PRS. This study investigated the association between the type of anesthetic agent and PRS in liver transplantation. Methods This single-center retrospective cohort study included patients who underwent liver transplantation between June 2016 and December 2019. Patients were divided into sevoflurane and propofol groups according to the anesthetic agent used. Stabilized inverse probability of treatment weighting (IPTW) analysis was performed to investigate the association between PRS identified based on blood pressure recordings and the type of anesthesia. Associations between the anesthetic agent and the duration of hypotension as well as early postoperative outcomes were also investigated. Results Data were analyzed for 398 patients, 304 (76.4%) and 94 (23.6%) of whom were anesthetized with propofol and sevoflurane, respectively. PRS developed in 40.7% of the 398 patients. Following stabilized IPTW analysis, the association with PRS was lower in the sevoflurane group than in the propofol group (odds ratio, 0.47; P = 0.018). However, there was no association between the type of anesthetic used and early postoperative outcomes. Conclusions The association of PRS was lower in the sevoflurane group than in the propofol group. However, there was no association between the type of anesthetic and the early postoperative outcomes. Further studies are required to determine the optimal anesthetic for liver transplantation.

Abstract Accurate prediction of postoperative mortality is important for not only successful postoperative patient care but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study aimed to create a machine-learning prediction model for 30-day mortality after a non-cardiac surgery that adapts to the manageable amount of clinical information as input features and is validated against multi-centered rather than single-centered data. Data were collected from 454,404 patients over 18 years of age who underwent non-cardiac surgeries from four independent institutions. We performed a retrospective analysis of the retrieved data. Only 12–18 clinical variables were used for model training. Logistic regression, random forest classifier, extreme gradient boosting (XGBoost), and deep neural network methods were applied to compare the prediction performances. To reduce overfitting and create a robust model, bootstrapping and grid search with tenfold cross-validation were performed. The XGBoost method in Seoul National University Hospital (SNUH) data delivers the best performance in terms of the area under receiver operating characteristic curve (AUROC) (0.9376) and the area under the precision-recall curve (0.1593). The predictive performance was the best when the SNUH model was validated with Ewha Womans University Medical Center data (AUROC, 0.941). Preoperative albumin, prothrombin time, and age were the most important features in the model for each hospital. It is possible to create a robust artificial intelligence prediction model applicable to multiple institutions through a light predictive model using only minimal preoperative information that can be automatically extracted from each hospital.

Measurement(s) vital signs of patients during surgery • perioperative patient information Technology Type(s) Vital Signs Measurement • Electronic Medical Record Factor Type(s) vital signs data including various numeric and waveform data acquired from multiple patient monitors • perioperative patient information acquired from the electronic medical record system Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment hospital Sample Characteristic - Location South Korea

Recent advancements in artificial intelligence (AI) techniques have enabled the development of accurate prediction models using clinical big data. AI models for perioperative risk stratification, intraoperative event prediction, biosignal analyses, and intensive care medicine have been developed in the field of perioperative medicine. Some of these models have been validated using external datasets and randomized controlled trials. Once these models are implemented in electronic health record systems or software medical devices, they could help anesthesiologists improve clinical outcomes by accurately predicting complications and suggesting optimal treatment strategies in real-time. This review provides an overview of the AI techniques used in perioperative medicine and a summary of the studies that have been published using these techniques. Understanding these techniques will aid in their appropriate application in clinical practice.

Background Appropriate monitoring of intradialytic biosignals is essential to minimize adverse outcomes because intradialytic hypotension and arrhythmia are associated with cardiovascular risk in hemodialysis patients. However, a continuous monitoring system for intradialytic biosignals has not yet been developed. Methods This study investigated a cloud system that hosted a prospective, open-source registry to monitor and collect intradialytic biosignals, which was named the CONTINUAL (Continuous mOnitoriNg viTal sIgN dUring hemodiALysis) registry. This registry was based on real-time multimodal data acquisition, such as blood pressure, heart rate, electrocardiogram, and photoplethysmogram results. Results We analyzed session information from this system for the initial 8 months, including data for some cases with hemodynamic complications such as intradialytic hypotension and arrhythmia. Conclusion This biosignal registry provides valuable data that can be applied to conduct epidemiological surveys on hemodynamic complications during hemodialysis and develop artificial intelligence models that predict biosignal changes which can improve patient outcomes.

Currently, robot-assisted latissimus dorsi muscle flap (RLDF) surgery is used in treating patients with Poland syndrome and for breast reconstruction. However, conventional RLDF surgery has several inherent issues. We resolved the existing problems of the conventional system by introducing the da Vinci single-port system in patients with Poland syndrome. Overall, three patients underwent RLDF surgery using the da Vinci single-port system with gas insufflation. In the female patient, after performing RLDF with silicone implant, augmentation mammoplasty was also performed on the contralateral side. Both surgeries were performed as single-port robotic-assisted surgery through the transaxillary approach. The mean operating time was 449 (335–480) minutes; 8.67 (4–14) minutes were required for docking and 59 (52–67) minutes for robotic dissection and LD harvesting. No patients had perioperative complication and postoperative problems related to gas inflation. The single-port robot-assisted surgical system overcomes the drawbacks of previous robotic surgery in patients with Poland syndrome, significantly shortens the procedure time of robotic surgery, has superior cosmetic outcomes in a surgical scar, and improves the operator's convenience. Furthermore, concurrent application to another surgery demonstrates the possibility in the broad application of the robotic single-port surgical system.

Abstract Purpose Sepsis is a leading cause of morbidity and mortality worldwide and is characterized by vascular leak. Treatment for sepsis, specifically intravenous fluids, may worsen deterioration in the context of vascular leak. We therefore sought to quantify vascular leak in sepsis patients to guide fluid resuscitation. Methods We performed a retrospective cohort study of sepsis patients in four ICU databases in North America, Europe, and Asia. We developed an intuitive vascular leak index (VLI) and explored the relationship between VLI and in-hospital death and fluid balance using generalized additive models (GAM). Results Using a GAM, we found that increased VLI is associated with an increased risk of in-hospital death. Patients with a VLI in the highest quartile (Q4), across the four datasets, had a 1.61–2.31 times increased odds of dying in the hospital compared to patients with a VLI in the lowest quartile (Q1). VLI Q2 and Q3 were also associated with increased odds of dying. The relationship between VLI, treated as a continuous variable, and in-hospital death and fluid balance was statistically significant in the three datasets with large sample sizes. Specifically, we observed that as VLI increased, there was increase in the risk for in-hospital death and 36–84 h fluid balance. Conclusions Our VLI identifies groups of patients who may be at higher risk for in-hospital death or for fluid accumulation. This relationship persisted in models developed to control for severity of illness and chronic comorbidities.

Background Although the association between an increase in anastomotic leakage (AL) and non-steroidal anti-inflammatory drugs (NSAIDs) has been reported in gastrointestinal surgeries, this issue has rarely been addressed for pancreaticoduodenectomy (PD). We aimed to investigate the association between postoperative NSAIDs administration and clinically relevant AL (CR-AL) following PD. Methods We retrospectively evaluated 2,163 consecutive patients who underwent PD between 2007 and 2019. The patients were divided into two groups; patients who received and did not receive NSAIDs by postoperative day (POD) 5. We conducted a propensity score analysis using inverse probability of treatment weighting (IPTW) to adjust the baseline differences between both groups. We compared the occurrence of CR-AL and other postoperative outcomes before and after IPTW. Further, we used the multivariable binary logistic regression method for a sensitivity analysis for CR-AL. Results A total of 2,136 patients were included in the analysis. Of these, 222 (10.4%) received NSAIDs by POD 5. The overall occurrence rate of CR-AL was 14.9%. After IPTW, postoperative NSAIDs were significantly associated with CR-AL (odds ratio [OR]: 1.24, 95% CI [1.05, 1.47], P = 0.012), prolonged postoperative hospitalization (OR: 1.31, 95% CI [1.14, 1.50], P < 0.001), and unplanned readmission within 30 days postoperatively (OR 1.48: 95% CI [1.15, 1.91], P = 0.002). However, this association was not consistent in the sensitivity analysis. Conclusions Postoperative NSAIDs use was significantly associated with an increase in CR-AL incidence following PD. However, sensitivity analysis failed to show its association, which precludes a firm conclusion of its detrimental effect.

Background Reducing the duration of intraoperative hypoxemia in pediatric patients by means of rapid detection and early intervention is considered crucial by clinicians. We aimed to develop and validate a machine learning model that can predict intraoperative hypoxemia events 1 min ahead in children undergoing general anesthesia. Methods This retrospective study used prospectively collected intraoperative vital signs and parameters from the anesthesia ventilator machine extracted every 2 s in pediatric patients undergoing surgery under general anesthesia between January 2019 and October 2020 in a tertiary academic hospital. Intraoperative hypoxemia was defined as oxygen saturation Results In total, 1,540 (11.73%) patients with intraoperative hypoxemia out of 13,130 patients’ records with 2,367 episodes were included for developing the model dataset. After model development, 200 (13.25%) of the 1,510 patients’ records with 289 episodes were used for holdout validation. Among the models developed, the GBM had the highest AUROC of 0.904 (95% confidence interval [CI] 0.902 to 0.906), which was significantly higher than that of the LSTM (0.843, 95% CI 0.840 to 0.846 P < .001) and the transformer model (0.885, 95% CI, 0.882–0.887, P < .001). In holdout validation, GBM also demonstrated best performance with an AUROC of 0.939 (95% CI 0.936 to 0.941) which was better than LSTM (0.904, 95% CI 0.900 to 0.907, P < .001) and the transformer model (0.929, 95% CI 0.926 to 0.932, P < .001). Conclusions Machine learning models can be used to predict upcoming intraoperative hypoxemia in real-time based on the biosignals acquired by patient monitors, which can be useful for clinicians for prediction and proactive treatment of hypoxemia in an intraoperative setting.

Abstract For pheochromocytoma and sympathetic paraganglioma (PPGL), surgery can be used as a curative treatment; however, the life-threatening risk of perioperative hemodynamic instability (HI) presents challenges. This study aimed to analyze the incidence and predictive factors of perioperative HI. The electronic medical records of 114 consecutive patients who underwent surgery for PPGLs at our institution were retrospectively reviewed. HI was defined as one or more episodes of systolic blood pressure > 200 mmHg or mean blood pressure

Abstract Background The cross-sectional area of the subclavian vein (csSCV) is a crucial factor in the successful catheterization of the subclavian vein. This randomized controlled study investigated the effects of the csSCV on landmark-based subclavian vein catheterization. Methods This study was performed using a two-stage protocol. During stage I, the csSCV was measured in 17 patients placed in the supine, 20° ipsilateral tilt, and 20° contralateral tilt positions in a random order. During stage II, landmark-based subclavian vein catheterization was randomly performed in patients placed in either the supine (group S, n = 107) or the ipsilateral tilt (group I, n = 109) position. The primary outcome measure was the csSCV in stage I and the primary venipuncture success rate in stage II. Secondary outcome measures were the time to successful venipuncture, the total catheterization time, the first-pass success rate, and the incidence of mechanical complications during catheterization. Results The csSCV was significantly larger in the ipsilateral tilt than in either the supine or contralateral tilt position (1.01 ± 0.35 vs. 0.84 ± 0.32 and 0.51 ± 0.26 cm2, P = .006 and

Abstract Background The relationship between intraoperative low bispectral index (BIS) values and poor clinical outcomes has been controversial. Intraoperative hypotension is associated with postoperative complication. The purpose of this study was to investigate the influence of intraoperative low BIS values and hypotension on postoperative mortality in patients undergoing major abdominal surgery. Methods This retrospective study analyzed 1862 cases of general anesthesia. We collected the cumulative time of BIS values below 20 and 40 as well as electroencephalographic suppression and documented the incidences in which these states were maintained for at least 5 min. Durations of intraoperative mean arterial pressures (MAP) less than 50 mmHg were also recorded. Multivariable logistic regression was used to evaluate the association between suspected risk factors and postoperative mortality. Results Ninety-day mortality and 180-day mortality were 1.5 and 3.2% respectively. The cumulative time in minutes for BIS values falling below 40 coupled with MAP falling below 50 mmHg was associated with 90-day mortality (odds ratio, 1.26; 95% confidence interval, 1.04–1.53; P = .019). We found no association between BIS related values and 180-day mortality. Conclusions The cumulative duration of BIS values less than 40 concurrent with MAP less than 50 mmHg was associated with 90-day postoperative mortality, not 180-day postoperative mortality.

Abstract Background The Optiscope™ can be used for intubation with minimal neck motion. We retrospectively investigated radiographic predictors of difficult intubation using the Optiscope™ by analyzing preoperative radiographic images. Methods One hundred eighty-four patients who were intubated with the Optiscope™ under manual in-line cervical stabilization for cervical spine surgery were enrolled. Radiographic indices were measured on preoperative cervical spine lateral X-ray and magnetic resonance imaging images. Difficult intubation was defined as failure or time consumption more than 90 s on the first attempt. To identify significant predictors of difficult intubation using the Optiscope™ and evaluate their diagnostic value, multivariable logistic regression and receiver operating characteristic analyses were used. Results Fourty-seven patients showed difficult intubation. There was no significant difference in radiographic indices between the difficult and easy intubation groups, but higher body mass index (BMI) (26.5 [3.0] vs. 24.6 [3.5] kg/m2, P = 0.001), shorter sternomental distance (SMD) (122.0 [104.0 to 150.0] vs. 150.0 [130.0 to 170.0] mm, P = 0.001), shorter interincisor gap (40.0 [35.0 to 45.0] vs. 43.0 [40.0 to 50.0] mm, P = 0.006), and higher incidence of excessive oral secretions (10.6% vs. 2.9%, P = 0.049) were observed in patients with difficult intubation. In multivariable analysis, BMI (odds ratio [95% confidence interval]; 1.15 [1.03 to 1.28], P = 0.011) and SMD (odds ratio [95% confidence interval]; 0.98 [0.97 to 1.00], P = 0.008) were associated with difficult intubation with the Optiscope™. In receiver operating characterstic analysis, the area under the curve for body mass index was 0.68 (95% confidence interval; 0.60 to 0.77, P

BackgroundDespite an improved understanding of pheochromocytoma and extra-adrenal sympathetic parganglioma (PPGL), including diagnosis and management, some PPGLs are postoperatively diagnosed. Clinical characteristics and intraoperative haemodynamic instability (HI) in postoperatively diagnosed PPGL patients have been poorly defined. Thus, we investigated the clinical characteristics and HI in patients with postoperatively diagnosed PPGLs compared to patients with preoperatively diagnosed PPGLs.MethodsWe obtained clinical and haemodynamic data from the electronic medical records of 256 patients with pathologically confirmed PPGLs at our institution from January 2005 to December 2019. We assessed the intraoperative HI (systolic blood pressure [SBP]>160 mmHg (min) or mean blood pressure [MBP]160 mmHg (median [IQR]; 7.9% [2.5; 11.9] % vs. 4.6% [0.0; 11.9], P=0.088) but a significantly lower percentage of time with MBP

To develop deep learning models for predicting Interoperative hypotension (IOH) using waveforms from arterial blood pressure (ABP), electrocardiogram (ECG), and electroencephalogram (EEG), and to determine whether combination ABP with EEG or CG improves model performance. Data were retrieved from VitalDB, a public data repository of vital signs taken during surgeries in 10 operating rooms at Seoul National University Hospital from January 6, 2005, to March 1, 2014. Retrospective data from 14,140 adult patients undergoing non-cardiac surgery with general anaesthesia were used. The predictive performances of models trained with different combinations of waveforms were evaluated and compared at time points at 3, 5, 10, 15 minutes before the event. The performance was calculated by area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), sensitivity and specificity. The model performance was better in the model using both ABP and EEG waveforms than in all other models at all time points (3, 5, 10, and 15 minutes before an event) Using high-fidelity ABP and EEG waveforms, the model predicted IOH with a AUROC and AUPRC of 0.935 [0.932 to 0.938] and 0.882 [0.876 to 0.887] at 5 minutes before an IOH event. The output of both ABP and EEG was more calibrated than that using other combinations or ABP alone. The results demonstrate that a predictive deep neural network can be trained using ABP, ECG, and EEG waveforms, and the combination of ABP and EEG improves model performance and calibration.

BackgroundArterial pressure-based cardiac output (APCO) is a less invasive method for estimating cardiac output without concerns about complications from the pulmonary artery catheter (PAC). However, inaccuracies of currently available APCO devices have been reported. Improvements to the algorithm by researchers are impossible, as only a subset of the algorithm has been released. ObjectiveIn this study, an open-source algorithm was developed and validated using a convolutional neural network and a transfer learning technique. MethodsA retrospective study was performed using data from a prospective cohort registry of intraoperative bio-signal data from a university hospital. The convolutional neural network model was trained using the arterial pressure waveform as input and the stroke volume (SV) value as the output. The model parameters were pretrained using the SV values from a commercial APCO device (Vigileo or EV1000 with the FloTrac algorithm) and adjusted with a transfer learning technique using SV values from the PAC. The performance of the model was evaluated using absolute error for the PAC on the testing dataset from separate periods. Finally, we compared the performance of the deep learning model and the FloTrac with the SV values from the PAC. ResultsA total of 2057 surgical cases (1958 training and 99 testing cases) were used in the registry. In the deep learning model, the absolute errors of SV were 14.5 (SD 13.4) mL (10.2 [SD 8.4] mL in cardiac surgery and 17.4 [SD 15.3] mL in liver transplantation). Compared with FloTrac, the absolute errors of the deep learning model were significantly smaller (16.5 [SD 15.4] and 18.3 [SD 15.1], P

Abstract Background A tracheal tube stylet can be used to assist successful tracheal intubation, especially during videolaryngoscopic intubation because videolaryngoscopes with a Macintosh-type blade such as McGrath® MAC videolaryngoscope have more acute angle than conventional Macintosh laryngoscope. However, the use of a stylet during tracheal intubation can raise concerns about stylet-induced postoperative airway complications, such as sore throat, subglottic injury, and hoarseness. In this study, we compared the incidence of postoperative airway complications after McGrath® MAC videolaryngoscopic intubation with versus without a stylet in patients with a high Mallampati score. Methods In 104 patients with Mallampati score III or IV and who were scheduled for lumbar or thoracic spine surgery, McGrath® MAC videolaryngoscopic intubation was performed either with a stylet (group S, n = 52) or without a stylet (group N, n = 52). The primary outcome measure was the incidences of sore throat evaluated at 1 and 24 h postoperatively. Secondary outcome measures were the incidences of subglottic injury and postoperative hoarseness. Results The incidence of CL grade III in group S and N was 3.8 and 5.8%, respectively. No patient showed CL grade IV. The incidences of sore throat at 1 (26.9 vs 19.2%, P = 0.485) and 24 h (17.3 vs 13.5%, P = 0.786, respectively) postoperatively were not significantly different between the group S and N. However, the incidence of subglottic injury was significantly higher in the group S, compared with the group N (65.4 vs 42.3%, P = 0.030). The incidence of postoperative hoarseness did not differ significantly between the two groups. Conclusions The use of a stylet during McGrath® MAC videolaryngoscopic intubation did not have a significant impact on the incidence of postoperative sore throat in patients with a high Mallampati score. Avoiding the use of a stylet during intubation with McGrath® MAC videolaryngoscope may reduce the incidence of subglottic injury in such patients. Trial registration Clinical Research Information Service (identifier: KCT0002427, date of registration: June 12, 2017).

Anesthesia with desflurane and remifentanil can be maintained with either fixed or titrated desflurane concentration. We hypothesized that the fixed-gas concentration (FG) method would reduce the number of anesthetic titrations without hypnotic and hemodynamic instability compared to the bispectral index (BIS)-guided (BG) method. Forty-eight patients were randomly allocated to the FG or BG groups. In the FG group, desflurane vaporizer setting was fixed at 1 age-corrected minimum alveolar concentration (MAC). In the BG group, desflurane was titrated to target a BIS level at 50. Remifentanil was titrated to maintain a systolic arterial pressure (SAP) of 120 mmHg in both groups. Our primary endpoint was the hypnotic stability measured by the wobble of BIS in performance analysis, and the secondary endpoints included the wobble of SAP, mean BIS value during surgery, and the number of anesthetic titrations. The BIS in the FG group showed significantly less wobble (3.9 ± 1.1% vs 5.5 ± 1.5%, P

Abstract Background The cross-sectional area of the subclavian vein (csSCV) is an important factor determining the success rate of SCV catheterization. The head-down position increases the csSCV. However, the effects of lateral tilting on subclavian venous cross-sectional area have not yet been explored. In this trial, we test our hypothesis that ipsilateral tilt during right SCV catheterization may significantly increase the csSCV by impeding blood flow to the heart, thereby increasing the primary venipuncture success rate and reducing the complication rate and procedure time. Methods/design This is a two-staged, prospective, randomized, controlled trial conducted on 237 neurosurgical patients requiring SCV catheterization. Seventeen patients in stage I will be placed in supine, 20° ipsilateral tilt, and 20° contralateral tilt positions in random order. The right csSCV will be measured using ultrasonography at each position. In stage II, 220 patients will be randomly assigned to the ipsilateral tilt group (n = 110) and supine group (n = 110) according to the position for right SCV catheterization. Data on catheterization-related characteristics and complications will be collected during and after catheterization. The primary outcome measures are the right csSCV for stage I and primary venipuncture success rate for stage II. The secondary outcome measures for stage II are time to venipuncture, total catheterization time, first-pass success rate, and complications, such as arterial puncture, hematoma, pneumothorax, air embolism, and catheter misplacement. Discussion This is the first trial to investigate the effects of the ipsilateral tilt position on right SCV catheterization. We will attest the beneficial effects of the ipsilateral tilt position on the csSCV and the primary venipuncture success rate during right SCV catheterization. Furthermore, comparisons of the first-pass success rate, complications, and total catheterization time during SCV catheterization in the ipsilateral tilt position vs. the supine position will help us determine which position is better for safe and easy SCV catheterization. Trial registration ClinicalTrials.gov, ID: NCT03296735. Registered on 25 September 2017 for stage I; NCT03303274 Registered on 6 October 2017 for stage II.

Abstract Profound hypotension during liver transplantation is aggressively treated with vasopressors thus frequently unrevealed in a retrospective study. The relationship between concealed intraoperative hypotension and in-hospital mortality after liver transplantation was evaluated using performance measurement (PM) of systolic arterial pressure (SAP). Median performance error (MDPE), median absolute performance error (MDAPE), and wobble of SAP were calculated using preoperative SAP as the reference value, and prereperfusion and postreperfusion SAPs as measured values. Univariable and multivariable logistic regression analyses were performed using 6 PM parameters and 36 traditional SAP-derived parameters to predict in-hospital mortality. In-hospital mortality was 3.9% (22/569 cases). Prereperfusion MDAPE and postreperfusion wobble were the only significant SAP-derived predictors of in-hospital mortality. The area under receiver operating characteristic curve of prediction model was 0.769 (95% confidence interval 0.732–0.803, P

Objective Ischaemic conditioning (IC) has organ-protective effects, but its clinical results have been inconsistent. Tissue oxygen saturation (StO 2 ) and heart rate variability (HRV) reflect peripheral microcirculation and autonomic nervous system activity, but their changes during IC have not been well documented. We assessed StO 2 and HRV during IC in patients undergoing cardiac surgery and healthy volunteers. Methods Ten patients undergoing cardiac surgery and 10 healthy male volunteers underwent remote IC (four 5-minute cycles of ischaemia/reperfusion) applied to the upper arm. Changes in StO 2 at the thenar eminence and HRV according to the R-R intervals were recorded during IC. Results The lowest StO 2 during ischaemia significantly decreased in patients and significantly increased in volunteers. Among the HRV parameters, the low-frequency domain, which corresponds to sympathetic activity, significantly increased after IC in volunteers but not in patients. Other variables were similar between the groups. Conclusions These results suggest that the minimum tissue oxygen content is depleted during ischaemia in patients and preserved in healthy volunteers. Sympathetic nervous activity seems to increase after IC in healthy volunteers but remains unaffected in patients. Thus, IC may act differently between patients undergoing cardiac surgery and healthy subjects.

Objective Positive end-expiratory pressure (PEEP) causes carotid baroreceptor unloading, which leads to thermoregulatory peripheral vasoconstriction. However, the effects of PEEP on intraoperative thermoregulation in the prone position remain unknown. Methods Thirty-seven patients undergoing spine surgery in the prone position were assigned at random to receive either 10 cmH 2 O PEEP (Group P) or no PEEP (Group Z). The primary endpoint was core temperature 180 minutes after intubation. Secondary endpoints were delta core temperature (difference in core temperature between 180 minutes and immediately after tracheal intubation), incidence of intraoperative hypothermia (core temperature of

The widespread use of remifentanil during total intravenous anesthesia (TIVA) has raised concerns about the risk of postoperative remifentanil-associated pain. Although a recent meta-analysis suggests that remifentanil-associated pain is unlikely to occur in patients with TIVA because of the protective effect of co-administered propofol, the evidence is not conclusive. We retrospectively assessed 635 patients who received robotic thyroid surgery under TIVA to evaluate the risk of remifentanil-associated pain. Postoperative pain was evaluated using 11-point numeric rating scale (NRS). Time dependent Cox proportional hazards regression analysis was used to determine the risk factors of treatment-requiring pain (NRS > 4) during the first 48 postoperative hours. Postoperative pain rapidly decreased, and treatment-requiring pain remained in 12.8% (81 out of 635) of patients at 48 hours postoperatively. After adjusting for the time-dependent analgesic consumption, intraoperative use of remifentanil > 0.2 mcg/kg/min was a positive predictor of postoperative pain with a hazard ratio of 1.296 (95% C.I., 1.014-1.656, P = 0.039) during 48 hours after surgery. In conclusion, excessive use of remifentanil during TIVA was associated with increased risk of pain after robotic thyroid surgery. Prospective trials are required to confirm these results and determine whether decreasing remifentanil consumption below the threshold can reduce postoperative pain.

The discrepancy between spatial orientations of an endoscopic image and a physician's working environment can make it difficult to interpret endoscopic images. In this study, we developed and evaluated a device that corrects the endoscopic image orientation using an accelerometer and gyrosensor. The acceleration of gravity and angular velocity were retrieved from the accelerometer and gyrosensor attached to the handle of the endoscope. The rotational angle of the endoscope handle was calculated using a Kalman filter with transmission delay compensation. Technical evaluation of the orientation correction system was performed using a camera by comparing the optical rotational angle from the captured image with the rotational angle calculated from the sensor outputs. For the clinical utility test, fifteen anesthesiology residents performed a video endoscopic examination of an airway model with and without using the orientation correction system. The participants reported numbers written on papers placed at the left main, right main, and right upper bronchi of the airway model. The correctness and the total time it took participants to report the numbers were recorded. During the technical evaluation, errors in the calculated rotational angle were less than 5 degrees. In the clinical utility test, there was a significant time reduction when using the orientation correction system compared with not using the system (median, 52 vs. 76 seconds; P = .012). In this study, we developed a real-time endoscopic image orientation correction system, which significantly improved physician performance during a video endoscopic exam.

Prolonged air leak (PAL), defined as air leak more than 5 days after lung resection, has been associated with various adverse outcomes. However, studies on intraoperative risk factors for PAL are not sufficient. We investigated whether the intraoperative ventilatory leak (VL) can predict PAL. A retrospective study of 1060 patients with chest tubes after lung resection was conducted. Tidal volume data were retrieved from the electronic anesthesia records. Ventilatory leak (%) was calculated as [(inspiratory tidal volume-expiratory tidal volume)/ inspiratory tidal volume × 100] and was measured after restart of two-lung ventilation. Cox proportional hazards regression analysis was performed using VL as a predictor, and PAL as the dependent outcome. The odds ratio of the VL was then adjusted by adding possible risk factors including patient characteristics, pulmonary function and surgical factors. The incidence of PAL was 18.7%. VL >9.5% was a significant predictor of PAL in univariable analysis. VL remained significant as a predictor of PAL (1.59, 95% CI, 1.37-1.85, P 60 years, body mass index 2.1 hours. C-statistic of the prediction model was 0.80 (95% CI, 0.77-0.82). In conclusion, VL was a quantitative measure of intraoperative air leakage and an independent predictor of postoperative PAL. Monitoring VL during lung resection may be uselful in recommending additional surgical repair or use of adjuncts and thus, help reduce postoperative PAL.

BACKGROUND:The association between preoperative aspirin use and postoperative acute kidney injury (AKI) in cardiovascular surgery is unclear. We sought to evaluate the effect of preoperative aspirin use on postoperative AKI in cardiac surgery. METHODS:A total of 770 patients who underwent cardiovascular surgery under cardiopulmonary bypass were reviewed. Perioperative clinical parameters including preoperative aspirin administration were retrieved. We matched 108 patients who took preoperative aspirin continuously with patients who stopped aspirin more than 7 days or did not take aspirin for the month before surgery. The parameters used in the matching included variables related to surgery type, patient's demographics, underlying medical conditions and preoperative medications. RESULTS:In the first seven postoperative days, 399 patients (51.8%) developed AKI, as defined by the Kidney Disease Improving Global Outcomes (KDIGO) criteria and 128 patients (16.6%) required hemodialysis. Most patients took aspirin 100 mg once daily (n = 195, 96.5%) and the remaining 75 mg once daily. Multivariable analysis showed that preoperative maintenance of aspirin was independently associated with decreased incidence of postoperative AKI (odds ratio [OR] 0.46, 95% confidence interval [CI] 0.21-0.98, P = 0.048; after propensity score matching: OR 0.39, 95% CI 0.22-0.67, P = 0.001). Preoperative maintenance of aspirin was associated with less incidence of AKI defined by KDIGO both in the entire and matched cohort (n = 44 [40.7%] vs. 69 [63.9%] in aspirin and non-aspirin group, respectively in matched sample, relative risk [RR] 0.64, 95% CI 0.49, 0.83, P = 0.001). Preoperative aspirin was associated with decreased postoperative hospital stay after matching (12 [9-18] days vs. 16 [10-25] in aspirin and non-aspirin group, respectively, P = 0.038). Intraoperative estimated or calculated blood loss using hematocrit difference and estimated total blood volume showed no difference according to aspirin administration in both entire and matched cohort. CONCLUSIONS:Preoperative low dose aspirin administration without discontinuation was protective against postoperative AKI defined by KDIGO criteria independently in both entire and matched cohort. Preoperative aspirin was also associated with decreased hemodialysis requirements and decreased postoperative hospital stay without increasing bleeding. However, differences in AKI and hospital stay were not associated with in-hospital mortality.

An 18-year-old male with a Fontan circulation underwent excision of a pheochromocytoma after conversion from laparoscopic surgery. The pneumoperitoneum established for laparoscopic surgery may have adverse effects on the Fontan circulation, because it increases the intra-abdominal pressure (IAP), intra-thoracic pressure, pulmonary vascular resistance, and systemic vascular resistance (SVR), and decreases cardiac preload and cardiac output. Meticulous monitoring is also required during carbon dioxide exsufflation, because a rapid decrease in IAP can provoke hemodynamic deterioration by decreasing venous return and SVR. Furthermore, catecholamines released by the pheochromocytoma can worsen the hemodynamic status of Fontan circulation during surgery. Therefore, sophisticated intraoperative anesthetic care is required during laparoscopic pheochromocytoma excision in patients with a Fontan circulation.

BackgroundPain on injection of rocuronium is a common clinical problem. We compared the efficacy of lidocaine, ketorolac, and the 2 in combination as pretreatment for the prevention of rocuronium-induced withdrawal movement.MethodsFor this prospective, randomized, placebo-controlled, double-blind study a total of 140 patients were randomly allocated to one of 4 treatment groups to receive intravenously placebo (saline), lidocaine (20 mg), ketorolac (10 mg), or both (n = 35 for each group), with venous occlusion. The tourniquet was released after 2 min and anesthesia was performed using 5 mg/kg thiopental sodium followed by 0.6 mg/kg rocuronium. The withdrawal response was graded on a 4-point scale in a double-blind manner.ResultsThe overall incidence of withdrawal movements after rocuronium was 34.3% with lidocaine (P = 0.001), 40% with ketorolac (P = 0.004), and 8.6% with both (P < 0.001), compared with 74.3% with placebo. There was a significantly lower incidence of withdrawal movements in patients receiving the lidocaine/ketorolac combination than in those receiving lidocaine or ketorolac alone (P = 0.009 and 0.002, respectively). The incidence of moderate to severe withdrawal movements was 14.3% with lidocaine, 17.2% with ketorolac, and 2.9% with lidocaine/ketorolac combination, as compared to 45.7% with the placebo. There was no significant difference in withdrawal movement between the lidocaine group and the ketorolac group.ConclusionsKetorolac pretreatment had an effect comparable to that of lidocaine in attenuating rocuronium-induced withdrawal movements and the lidocaine/ketorolac combination pretreatment, compared with lidocaine or ketorolac alone, effectively reduced withdrawal movements during rocuronium injection.

BackgroundThe chemical reaction of carbon dioxide absorbent and sevoflurane is known to produce compound A. However, carbon dioxide absorbents are not controlled by the Food and Drug Administration, but are treated as industrial products in some nations. Moreover, carbon dioxide absorbents differ in their capacities to produce compound A, because their chemical compositions differ. In this study, we compared the renal safety between carbon dioxide absorbent products in patients under sevoflurane anesthesia.MethodsEighty patients with no preexisting renal disease undergoing elective gynecologic surgery were randomly assigned to receive sevoflurane or isoflurane anesthesia with one of four carbon dioxide absorbent products (Sodasorblime®, Sodalyme®, Sodasorb®, Spherasorb®) at the same fresh gas flow of 2 L/min. The renal safety was evaluated by changes of blood urea nitrogen (BUN), creatinine and urine N-acetyl-b-glucoseaminidase (NAG)-creatinine ratio at 24 hours and 72 hours after surgery from preoperative level.ResultsThere was no significant difference in the renal safety indicators between carbon dioxide absorbents during sevoflurane anesthesia (P > 0.05). However, the BUN and urine NAG-creatinine ratios at 72 hours after surgery were higher in isoflurane anesthesia in some carbon dioxide absorbent groups (P = 0.03 and 0.04, respectively).ConclusionsWe could not find significant differences of renal safety indicators with carbon dioxide absorbents. Although the adverse effect of carbon dioxide absorbents on renal function was not proved, consideration should be given to their contol by the regulation on their efficacy and safety because carbon dioxide absorbents can produce compound A.

We encountered a case of a rupture of an endotracheal tube cuff during robot-assisted thyroid surgery in a 35-year-old male patient. Two hours after commencing surgery, the bellows of the ventilator were not filled and a rupture of the endotracheal tube cuff was suspected. Once the robot-manipulator is engaged, the position of the operating table cannot be altered without removing it from the patient. Reintubation with direct laryngoscopy was performed with difficulty in the narrow space between the patient's head and robot-manipulator without moving the robot away from the patient. The rupture of the endotracheal tube cuff was confirmed by observing air bubbles exiting from the balloon in water. The patient was discharged 3 days after surgery without complications. In robot-assisted thyroid surgery, a preoperative arrangement of the robot away from the patient's head to obtain easy access to the patient is essential for safe anesthetic care.

Bupivacaine inhibits cardiac conduction and contractility. Insulin enhances cardiac repolarization and myocardial contractility. We hypothesizes that insulin therapy would be effective in resuscitating bupivacaine-induced cardiac toxicity in rabbits. Twelve rabbits were tracheally intubated and midline sternotomy was performed under general anesthesia. Cardiovascular collapse (CVC) was induced by an IV bolus injection of bupivacaine 10 mg/kg. The rabbits were treated with either saline (control) or insulin injection, administered as a 2 U/kg bolus. Internal cardiac massage was performed until the return of spontaneous circulation (ROSC) and the time to the return of sinus rhythm (ROSR) was also noted in both groups. Arterial blood pressure, and electrocardiography were continuously monitored for 30 min and plasma bupivacaine concentrations at every 5 min. The ROSC, ROSR and normalization of QRS duration were attained faster in the insulin-treated group than in the control group. At the ROSC, there was a significant difference in bupivacaine concentration between two groups. Insulin facilitates the return of myocardial contractility and conduction from bupivacaine-induced CVC in rabbits. However, recovery of cardiac conduction is dependent mainly on the change of plasma bupivacaine concentrations.