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Purpose: Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive arterial blood pressure monitors. This study tested whether routine non-invasive monitors could also predict intraoperative hypotension using deep learning algorithms. Methods: An open-source database of non-cardiac surgery patients (https://vitadb.net/dataset) was used to develop the deep learning algorithm. The algorithm was validated using external data obtained from a tertiary Korean hospital. Intraoperative hypotension was defined as a systolic blood pressure less than 90 mmHg. The input data included five monitors: non-invasive blood pressure, electrocardiography, photoplethysmography, capnography, and bispectral index. The primary outcome was the performance of the deep learning model as assessed by the area under the receiver operating characteristic curve (AUROC). Results: Data from 4754 and 421 patients were used for algorithm development and external validation, respectively. The fully connected model of Multi-head Attention architecture and the Globally Attentive Locally Recurrent model with Focal Loss function were able to predict intraoperative hypotension 5 min before its occurrence. The AUROC of the algorithm was 0.917 (95% confidence interval [CI], 0.915–0.918) for the original data and 0.833 (95% CI, 0.830–0.836) for the external validation data. Attention map, which quantified the contributions of each monitor, showed that our algorithm utilized data from each monitor with weights ranging from 8 to 22% for determining hypotension. Conclusions: A deep learning model utilizing multi-channel non-invasive monitors could predict intraoperative hypotension with high accuracy. Future prospective studies are needed to determine whether this model can assist clinicians in preventing hypotension in patients undergoing surgery with non-invasive monitoring. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 <0.01). This approach will facilitate automatic and objective ASA-PS classification, thereby streamlining the clinical workflow. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (< 1.2 mg/dL), mild-to-moderate (1.2–3.0 mg/dL), and severe (> 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 < 7.0 g/dL) were higher in the hyperbilirubinemia groups (2%, 7%, and 12% for the normal, mild-to-moderate, and severe group, respectively. all P < 0.001). The possible over-transfusion rate was less than 1% in all groups. In conclusion, the use of SpHb in liver transplantation recipients with preoperative hyperbilirubinemia requires caution due to the positive bias and high risk of missed transfusion. However, the reliable trending ability indicated its potential use in clinical settings. [ABSTRACT FROM AUTHOR]

Many amniote vertebrate species including humans can form identical twins from a single embryo, but this only occurs rarely. It has been suggested that the primitive-streak-forming embryonic region emits signals that inhibit streak formation elsewhere but the signals involved, how they are transmitted and how they act has not been elucidated. Here we show that short tracks of calcium firing activity propagate through extraembryonic tissue via gap junctions and prevent ectopic primitive streak formation in chick embryos. Cross-regulation of calcium activity and an inhibitor of primitive streak formation (Bone Morphogenetic Protein, BMP) via NF-κB and NFAT establishes a long-range BMP gradient spanning the embryo. This mechanism explains how embryos of widely different sizes can maintain positional information that determines embryo polarity. We provide evidence for similar mechanisms in two different human embryo models and in Drosophila, suggesting an ancient evolutionary origin. Identical twins from a single embryo are formed in multiple species, albeit rarely. It is not clear how such twinning is suppressed during early development. Here they show that calcium signalling activity is propagated through the extraembryonic tissue to prevent ectopic primitive streak formation during gastrulation. [ABSTRACT FROM AUTHOR]

Purpose: Several technical aspects of the Fick method limit its use intraoperatively. A data-driven modification of the Fick method may enable its use in intraoperative settings. Methods: This two-center retrospective observational study included 57 (28 and 29 in each center) patients who underwent off-pump coronary artery bypass graft (OPCAB) surgery. Intraoperative recordings of physiological data were obtained and divided into training and test datasets. The Fick equation was used to calculate cardiac output (CO-Fick) using ventilator-determined variables, intraoperative hemoglobin level, and SvO2, with continuous thermodilution cardiac output (CCO) used as a reference. A modification CO-Fick was derived and validated: CO-Fick-AD, which adjusts the denominator of the original equation. Results: Increased deviation between CO-Fick and CCO was observed when oxygen extraction was low. The root mean square error of CO-Fick was decreased from 6.07 L/min to 0.70 L/min after the modification. CO-Fick-AD showed a mean bias of 0.17 (95% CI 0.00–0.34) L/min, with a 36.4% (95% CI 30.6–44.4%) error. The concordance rates of CO-Fick-AD ranged from 73.3 to 87.1% depending on the time interval and exclusion zone. Conclusions: The original Fick method is not reliable when oxygen extraction is low, but a modification using data-driven approach could enable continuous estimation of cardiac output during the dynamic intraoperative period with minimal bias. However, further improvements in precision and trending ability are needed. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 < 3.0 during surgery. The worst intraoperative NPi was recorded, then multivariate logistic regression analysis was performed to evaluate the relationship between abnormal NPi and postoperative delirium following cardiac surgery. Among 123 included patients, postoperative delirium developed in 19.5% (24/123) of patients. Intraoperative abnormal NPi was significantly associated with postoperative delirium (odds ratio 6.078; 95% confidence interval 1.845–20.025; P = 0.003) after adjustment for Society of Thoracic Surgeons Predicted Risk of Mortality score, coronary artery disease, and use of calcium channel blockers. In conclusion, abnormal intraoperative NPi independently predicted postoperative delirium following cardiac surgery. Intraoperative application of pupillometry may have prognostic value for development of postoperative delirium, thereby enabling close surveillance and early intervention in high-risk patients. Registry number: ClinicalTrials.gov (NCT04136210). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 % , 20 % , and 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 ± 7.509 and 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 FROM AUTHOR]

Acute kidney injury (AKI) is one of the most common complications after cardiac surgery, associated with increased mortality and morbidity. Near-infrared spectroscopy (NIRS) continuously measures regional oxygen saturation(rSO2) in real-time. This exploratory retrospective study aimed to investigate the association between intraoperative plantar rSO2 and postoperative AKI in cardiac surgery patients. Between August 2019 and March 2021, 394 patients were included. Plantar and cerebral rSO2 were monitored using NIRS intraoperatively. The primary outcome was AKI within 7 postoperative days. The nonlinear association between plantar rSO2, cerebral rSO2, and mean arterial blood pressure (MBP) and AKI was assessed, and plantar rSO2<45% was related to an increased risk of AKI. Multivariable logistic regression analyses revealed that longer duration and higher area under the curve below plantar rSO2<45% and MBP<65 mmHg were more likely to be associated with increased odds of AKI. In additional multivariable regression analyses, association between plantar rSO2<45% and AKI was still maintained after adjusting the duration or AUC of MBP<65 mmHg as a covariate. Cerebral rSO2 levels were not associated with AKI. Independent of MAP, intraoperative plantar rSO2 was associated with AKI after cardiac surgery. However, intraoperative cerebral rSO2 was not associated with AKI. Intraoperative plantar rSO2 monitoring may be helpful in preventing AKI. [ABSTRACT FROM AUTHOR]

During primitive streak formation in the chick embryo, cells undergo mesendoderm specification and convergent extension at the same time and in the same cells. Previous work has implicated cVG1 (GDF3) as a key factor for induction of primitive streak identity and positioning the primitive streak, whereas FGF signalling was implicated in regulating cell intercalation via regulation of components of the WNT-planar cell polarity (PCP) pathway. FGF has also been reported to be able to induce a primitive streak (but lacking the most axial derivatives such as notochord/prechordal mesendoderm). These signals emanate from different cell populations in the embryo, so how do they interact to ensure that the same cells undergo both cell intercalation and acquire primitive streak identity? Here we begin to address this question by examining in more detail the ability of the two classes of signals in regulating the two developmental events. Using misexpression of inducers and/or exposure to inhibitors and in situ hybridisation, we study how these two signals regulate expression of Brachyury (TBXT) and PRICKLE1 as markers for the primitive streak and the PCP, respectively. We find that both signals can induce both properties, but while FGF seems to be required for induction of the streak by cVG1, it is not necessary for induction of PRICKLE1. The results are consistent with cVG1 being a common regulator for both primitive streak identity and the initiation of convergent extension that leads to streak elongation. [ABSTRACT FROM AUTHOR]

The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83–0.87), 0.76 (0.70–0.82), and 0.95 (0.92–0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable resource for biosignal research and development. 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 [ABSTRACT FROM AUTHOR]

Unexpected cardiorespiratory compromise has been reported during ophthalmic arterial chemotherapy in pediatric patients with retinoblastoma. Although the underlying mechanisms remain unclear, autonomic responses are presumed to contribute to these events. We hypothesized that periprocedural heart rate variability would differ between patients with and without events. Between April 2018 and September 2019, 38 patients (age under 7 years) were included. Heart rate variability was analyzed using electrocardiogram, and oxygen reserve index was also monitored. Cardiorespiratory events were defined as > 30% changes in blood pressure or heart rate, > 20% changes in end-tidal carbon dioxide, > 40% changes in peak inspiratory pressure, or pulse oxygen saturation < 90% during ophthalmic artery catheterization. Heart rate variability and oxygen reserve index were compared between patients with and without cardiorespiratory events. Cardiorespiratory events occurred in 13/38 (34%) patients. During the events, end-tidal carbon dioxide was significantly lower (median difference [95% CI], − 2 [− 4 to − 1] mmHg, p = 0.006) and the maximum peak inspiratory pressure was higher (30 [25–37] vs. 15 [14–16] hPa, p < 0.001), compared to patients without events. Standard deviation of normal-to-normal R–R interval, total power, and very low-frequency power domain increased during selection of the ophthalmic artery in patients with events (all adjusted p < 0.0001), without predominancy of specific autonomic nervous alterations. Oxygen reserve index was significantly lower in patients with events than those without throughout the procedure (mean difference [95% CI], − 0.19 [− 0.32 to − 0.06], p = 0.005). Enhanced compensatory autonomic regulation without specific autonomic predominancy, and reduced oxygen reserve index was observed in patients with cardiorespiratory events than in patients without events. [ABSTRACT FROM AUTHOR]

The dynamic response (DR) of the arterial pressure monitoring system (APMS) may depend on the intraarterial catheter (IAC) diameter. We hypothesized that adequate DR would be more common when using a smaller IAC. We compared the DR of the AMPS (Auto Transducer™) between three IACs (BD Angiocath Plus™) with different diameters. 353 neurosurgical patients were randomized into three groups undergoing catheterization with a 20-, 22-, or 24-gauge IAC: 20G (n = 119), 22G (n = 117), and 24G (n = 117) groups, respectively. The DR, which depends on the natural frequency and damping coefficient, was divided into four types: adequate (primary outcome measure), underdamped, overdamped, and unacceptable. The frequency of intraoperative IAC malfunction was noted. Adequate DR was observed more frequently in the 22G and 24G groups than the 20G group (13.7% and 15.4% vs. 4.2%, P = 0.011 and 0.004, respectively). The frequency of underdamped DR was higher in the 20G group than the 24G group (86.6% vs. 69.2%, P = 0.001), whereas overdamped DR was more frequent in the 24G group than the 20G and 22G groups (6.0% vs. 0.0% and 0.0%, P = 0.007 and 0.014, respectively). IAC malfunctioned more frequently during surgery in the 24G group than the 20G and 22G groups (15.4% vs. 0.0% and 1.7%, P < 0.001 and P < 0.001, respectively). The frequency of adequate DR was low regardless of the IAC diameter. Nonetheless, in terms of DR and IAC malfunction, a 22-gauge BD Angiocath Plus™ was more suitable for invasive blood pressure monitoring with Auto Transducer™ than a 20- or 24-gauge BD Angiocath Plus™. Registration Registry: ClinicalTrials.gov. Registration number: NCT03642756. Date of Registration: July 27, 2018. [ABSTRACT FROM AUTHOR]

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 < 60 mmHg during surgery. The factors predictive of perioperative HI were determined using both univariate and multivariate analyses. Intraoperative HI occurred in 79 (69.3%) patients. In multivariate analysis, α-adrenergic receptor blocker duration (days) (odds ratio, 1.015; 95% confidence interval, 1.001–1.029) was a predictor for intraoperative HI. Postoperative hypotension occurred in 36 (31.6%) patients. Higher urine epinephrine levels, and greater preoperative highest heart rate (HR) were predictive factors for postoperative hypotension in PPGL patients. Caution should be taken in perioperative management for PPGL, especially with long duration of α-adrenergic receptor blocker use, higher urine epinephrine levels, and greater preoperative highest HR. [ABSTRACT FROM AUTHOR]

Performance measurement variables can be applied in clinical practice to evaluate hemodynamic instability. This study aimed to evaluate the relationship between the performance measurement of mean arterial pressure during cardiac surgery using cardiopulmonary bypass and postoperative mortality. A retrospective cohort study of patients who underwent cardiac surgery requiring cardiopulmonary bypass between 2013 and 2016 was conducted. The median performance error (MDPE) and median absolute performance error (MDAPE) were calculated using the preoperative mean arterial pressure as a reference, and intraoperative mean arterial pressures as measured values. Multivariable logistic regression analyses were performed using performance measurement variables to predict 30-day mortality. Overall survival according to performance measurement variables was evaluated using Cox proportional hazard models and Kaplan–Meier survival curves were generated to compare survival probability. Among 1203 patients, 110 (9.1%) died after surgery, and the 30-day mortality rate was 2.3% (28/1203). After adjusting for confounders, MDPE and MDAPE were significant mean arterial pressure derived predictors of 30-day mortality and overall survival. Intraoperative hypotension measured by performance measurement variables was independently associated with 30-day and overall mortality after cardiac surgery requiring cardiopulmonary bypass. Kaplan–Meier survival curves showed lower survival probability in patients with higher MDAPE during the pre- and post- cardiopulmonary bypass periods (P < 0.001 by log-rank test). Intraoperative hypotension measured by performance measurement variables was independently associated with 30-day and overall mortality after cardiac surgery requiring CPB. We propose that performance measurement variables are useful for quantifying the degree of intraoperative hypotension and predicting survival following cardiac surgery. Trial registration: ClinicalTrials.gov, identifier: NCT03785132. [ABSTRACT FROM AUTHOR]

Purpose: Diabetes insipidus (DI) develops commonly after endoscopic transsphenoidal surgery (ETS). We retrospectively investigated the incidence, onset, duration and predictors of DI after ETS in patients with non-functioning pituitary adenoma (NFPA). Methods: A total of 168 patients who underwent ETS to remove NFPAs were included. Various perioperative data on demographics, comorbidities, previous treatments, perioperative hormone deficiencies, tumor characteristics, surgery, anesthesia, intraoperative fluid balance, perioperative laboratory findings, postoperative complications, readmission and hospital length of stay were collected and analyzed. Patients were diagnosed with DI and treated with desmopressin when they showed urine output > 5 mL/kg/hr with a serum sodium concentration > 145 mmol/L or an increase ≥ 3 mmol/L in serum sodium concentration between two consecutive tests after surgery. DI was considered permanent when desmopressin was prescribed for > 6 months after surgery. Results: Seventy-seven (45.8%) patients experienced postoperative DI and 10 (6.0%) patients suffered from permanent DI. The median onset of DI and the median duration of transient DI were postoperative day 1 and 5 days, respectively. In multivariable logistic regression analysis, cephalocaudal tumor diameter (odds ratio [95% confidence interval] 2.59 [1.05–6.36], P = 0.038) was related to postoperative DI. In receiver operating characteristic analysis, its area under the curve was 0.68 (95% confidence interval 0.59–0.76, P < 0.001). Its optimal cutoff value that maximized the sum of sensitivity and specificity for postoperative DI was 2.7 cm. Conclusions: Postoperative DI was observed in 45.8% of patients undergoing ETS to remove NFPAs. A large cephalocaudal tumor diameter was predictive of postoperative DI in such patients. [ABSTRACT FROM AUTHOR]

Background: Abdominal tissue transfer has become the most commonly used tool for breast reconstruction. However, a secondary operator is often responsible for donor closure, which leaves dissatisfaction to patients due to inconsistent donor scars. Now, an absorbable dermal stapler is popularized worldwide and currently used for wound closure in many surgical fields. In this study, we aim to evaluate the abdominal donor site scar in using an absorbable dermal staple compared to a conventional suture. Methods: This is a prospective, randomized controlled and double-blinded study. Between January 2018 and April 2019, a total of 30 patients who underwent breast reconstruction using abdominal flap were included. Donor sites were divided into equal halves, and the each dermal layer was sutured with either dermal staples or traditional suturing, respectively. At 1, 3 and 6 months after operation, the scar was evaluated by two blinded plastic surgeons by using the modified Manchester scar scale (MSS). Results: An averaged sum of modified MSS was lower for the side sutured with a dermal stapler at the first month (11.76 ± 2.12 vs. 12.28 ± 2.03, p = 0.097), third month (12.17 ± 1.86 vs. 12.62 ± 2.31, p = 0.301) and sixth month (11.28 ± 2.63 vs. 12.14 ± 2.76, p = 0.051). Also, the dermal stapler side scored significantly higher for patient satisfaction than did the suture side (4.03 ± 0.98 vs 3.66 ± 0.97, p < 0.05). Conclusion: The objective outcome of the scar closed by an absorbable dermal stapler was not statistically superior to conventional suturing. (p > 0.05) In the subjective outcome, however, it showed a significantly higher patients' satisfaction (p < 0.05). Level of Evidence III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

Background: Besides 5-aminolevulinic acid (5-ALA), liver enzyme elevation after brain tumor surgery can be caused by anesthesia and medications. In this retrospective study, we determined whether preoperative 5-ALA administration is associated with postoperative elevation of liver enzymes (PELE) in brain tumor patients and identified predictive factors for PELE in patients treated with 5-ALA. Methods: In 179 patients undergoing brain tumor surgery with preoperative normal values of liver enzymes, laboratory data on serum alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), and total bilirubin (T.bil) levels were collected preoperatively and through postoperative day (POD) 45. Results: Ninety-nine PELEs (ALT, 56; AST, 34; ALP, 5; and TB, 4) were observed in 62 (34.6%) patients. Four (4.2%) patients treated with 5-ALA showed grade 3 elevation of transaminases based on the Common Terminology Criteria for Adverse Effects. Preoperative 5-ALA treatment was predictive of PELE (odds ratio [95% confidence interval], 2.30 [1.14–4.67]; P = 0.021). In patients treated with 5-ALA (n = 95), 70 PELEs (ALT, 39; AST, 22; ALP, 5; and TB, 4) were observed in 41 (43.2%) patients and significant predictive factors for PELE were preoperative ALT level (1.10 [1.04–1.17]; P = 0.001) and body mass index (BMI, 1.29 [1.08–1.56]; P = 0.006). In patients treated with 5-ALA, 13 and 36 patients, of 39 patients whose maximum postoperative ALT levels > 40 U/L, showed the normal value of serum ALT on PODs 14 and 45, respectively. Only three patients showed ALT elevation > 40 U/L on PODs 15–45, with a downward trend. Conclusions: The use of 5-ALA for brain tumor surgery in patients with preoperative normal values of liver enzymes was associated with increased transient PELE, but a low incidence of severely elevated liver transaminases levels. When 5-ALA is administered to patients with the upper normal value of preoperative serum ALT and overweight, attention is paid to PELE. [ABSTRACT FROM AUTHOR]

Background: Although many drugs or interventions have been studied to manage catheter-related bladder discomfort (CRBD), their comparative effectiveness is unknown. We attempted to assess the comparative effectiveness of the strategies to manage CRBD in patients undergoing urologic surgery including amikacin, solifenacin, darifenacin, butylscopolamine, dexmedetomidine, gabapentin, glycopyrrolate, ketamine, oxybutynin, resiniferatoxin, tolterodine, tramadol, caudal block, dorsal penile nerve block, lidocaine–prilocaine cream. Methods: We performed an arm-based network meta-analysis including 29 trials with 2841 participants. Goodness of model fit was evaluated by deviance information criteria (DIC). The incidence of CRBD at 0, 1, and 6 h after surgery and the incidence of moderate to severe CRBD at 0, 1, and 6 h after surgery were compared. Results: Random effect model was selected according to DIC. Most of the drugs significantly decreased the incidence of CRBD except amikacin, tramadol at 0 and 1 h after surgery. Dexmedetomidine, solifenacin, caudal block, dorsal penile nerve block, resiniferatoxin, and gabapentin 1200 mg p.o. significantly decreased the incidence of CRBD at 6 h after surgery (gabapentin 1200: Odds ratio [OR] 0.02; SUCRA 95.6). Dexmedetomidine and tolterodine significantly decreased the incidence of moderate to severe CRBD at 0, 1, and 6 h after surgery (tolterodine at 6 h: OR 0.05; SUCRA 73.7). Conclusions: Gabapentin was ranked best regarding the overall incidence of CRBD, while tolterodine was ranked best in reducing the severity of CRBD. However, a firm conclusion cannot be made from our analysis due to small-study number and heterogeneity regarding study setting and outcome measurement. [ABSTRACT FROM AUTHOR]

Premature senescence, a key strategy used to suppress carcinogenesis, can be driven by p53/p21 proteins in response to various stresses. Here, we demonstrate that Wig1 plays a critical role in this process through regulation of p21 mRNA stability. Wig1 controls the association of Argonaute2 (Ago2), a central component of the RNA-induced silencing complex (RISC), with target p21 mRNA via binding of the stem-loop structure near the microRNA (miRNA) target site. Depletion of Wig1 prohibited miRNA-mediated p21 mRNA decay and resulted in premature senescence. Wig1 plays an essential role in cell proliferation, as demonstrated in tumour xenografts in mice, and Wig1 and p21 mRNA levels are inversely correlated in human normal and cancer tissues. Together, our data indicate a novel role of Wig1 in RISC target accessibility, which is a key step in RNA-mediated gene silencing. In addition, these findings indicate that fine-tuning of p21 levels by Wig1 is essential for the prevention of cellular senescence. [ABSTRACT FROM AUTHOR]

Microvolt T-wave alternans (MTWA), which reflects electrical dispersion of repolarization, is known to be associated with arrhythmia or sudden cardiac death in high risk patients. In this study we investigated the relationship between MTWA and postoperative mortality in 330 cardiac surgery patients. Electrocardiogram, official national data and electric chart were analysed to provide in-hospital and mid-term outcome. MTWA at the end of surgery was significantly associated with in-hospital mortality in both univariate analysis (OR = 27.378, 95% CI 5.616–133.466, p < 0.001) and multivariate analysis (OR = 59.225, 95% CI 6.061–578.748, p < 0.001). Cox proportional hazards model revealed MTWA at the end of surgery was independently associated with mid-term mortality (HR = 4.337, 95% CI 1.594–11.795). The area under the curve of the model evaluating MTWA at the end of surgery was 0.764 (95% CI, 0.715–0.809) and it increased to 0.929 (95% CI, 0.896–0.954) when combined with the EuroSCORE II. MTWA positive at the end of surgery had a 60-fold increase in in-hospital mortality and a 4-fold increase in mid-term mortality. Moreover, MTWA at the end of surgery could predict in-hospital mortality and this predictability is more robust when combined with the EuroSCORE II. [ABSTRACT FROM AUTHOR]

Bispectral index (BIS), a useful marker of anaesthetic depth, is calculated by a statistical multivariate model using nonlinear functions of electroencephalography-based subparameters. However, only a portion of the proprietary algorithm has been identified. We investigated the BIS algorithm using clinical big data and machine learning techniques. Retrospective data from 5,427 patients who underwent BIS monitoring during general anaesthesia were used, of which 80% and 20% were used as training datasets and test datasets, respectively. A histogram of data points was plotted to define five BIS ranges representing the depth of anaesthesia. Decision tree analysis was performed to determine the electroencephalography subparameters and their thresholds for classifying five BIS ranges. Random sample consensus regression analyses were performed using the subparameters to derive multiple linear regression models of BIS calculation in five BIS ranges. The performance of the decision tree and regression models was externally validated with positive predictive value and median absolute error, respectively. A four-level depth decision tree was built with four subparameters such as burst suppression ratio, power of electromyogram, 95% spectral edge frequency, and relative beta ratio. Positive predictive values were 100%, 80%, 80%, 85% and 89% in the order of increasing BIS in the five BIS ranges. The average of median absolute errors of regression models was 4.1 as BIS value. A data driven BIS calculation algorithm using multiple electroencephalography subparameters with different weights depending on BIS ranges has been proposed. The results may help the anaesthesiologists interpret the erroneous BIS values observed during clinical practice. [ABSTRACT FROM AUTHOR]