Browsing by Department "Anesthesiology"

We studied whether the 27 base pair (bp) tandem repeat polymorphism in intron 4 of the endothelial nitric oxide synthase (eNOS) gene was associated with coronary artery disease (CAD) in a hospital-based Taiwanese population. We included 219 consecutive patients who underwent coronary angiography at our institution. Two alleles, containing 4 (eNOS4a) and 5 repeats (eNOS4b), were identified after polymerase chain reaction amplifying intron 4 of the eNOS gene. The genotype frequencies for eNOS4b/b, eNOS4a/b and eNOS4a/a were 77.9, 21.5 and 0.6% in CAD subjects, and 80, 20 and 0% in control subjects (Fisher's exact test, p = 0.90), respectively. The odds ratio (OR) for CAD in patients with at least one eNOSa allele was 1.2 (0.5-2.9) after adjustment for classical CAD risk factors. The eNOS4a allele was not associated with the severity of CAD (Fisher's exact test, p = 0.90) and the occurrence of acute myocardial infarction (AMI) or unstable angina (adjusted OR 0.6, 0.3-1.6) in patients with CAD. In conclusion, the 27-bp repeat polymorphism of the eNOS gene was not associated with CAD and the occurrence of AMI or unstable angina in a hospital-based Taiwanese population. Copyright ? 2002 S. Karger AG, Basel.

Monitoring nociception under general anesthesia remains challenging due to the complexity of pain pathways and the limitations of single-parameter methods. In this study, we introduce a multimodal approach that integrates electroencephalogram (EEG), photoplethysmography (PPG), and electrocardiogram (ECG) signals to predict nociception. We collected data from patients undergoing general anesthesia at two hospitals and developed and compared two deep learning models: a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM) network. Both models were trained on expert anesthesiologists' assessments of nociception. We evaluated normalization strategies for offline and online usage and found that Min-Max normalization was most effective for our dataset. Our results demonstrate that the MLP model accurately captured nociceptive changes in response to painful surgical stimuli, whereas the LSTM model provided smoother predictions but with lower sensitivity to rapid changes. These findings underscore the potential of multimodal, deep learning-based solutions to improve real-time nociception monitoring in diverse clinical settings.

In this investigation, we proposed a continuous pain intensity scaling model derived from an innovative parameter of pain-relief demand (PRD) index for acute postoperative patients of gynecology using patient-controlled analgesia (PCA) via the occurrence frequency of pain-relief demands. To derive PRD, a simplified fluctuation time series of pain feeling was simulated according to the distribution of pain-relief demands using an exponential decay function. Furthermore, we applied the Hilbert Huang transform to obtain the time-amplitude distribution of the first intrinsic mode function (IMF) of fluctuation time series and defined the PRD index using a function of amplitude. A total of 2466 visual analogue scale (VAS) values derived from interviews with 470 postoperative patients at various time points were compared with this PRD index extracted from the PCA records of the same patients. According to the statistical result of one-way analysis of variance (ANOVA, P < 0.001), VAS was significantly linearly dependent on the PRD index. Although the result of this VAS model is not a perfect linear relationship between VAS and PRD index (r = 0.490), it is significantly better than the result of our previous study using a novel fuzzy pain demand index (FPD). This result shows this approach is promising for further study.

Our research introduces a framework that integrates edge computing, quantum transfer learning, and federated learning to revolutionize pain level assessment through ECG signal analysis. The primary focus lies in developing a robust, privacy-preserving system that accurately classifies pain levels (low, medium, and high) by leveraging the intricate relationship between pain perception and autonomic nervous system responses captured in ECG signals. At the heart of our methodology lies a signal processing approach that transforms one-dimensional ECG signals into rich, two-dimensional Continuous Wavelet Transform (CWT) images. These transformations capture both temporal and frequency characteristics of pain-induced cardiac variations, providing a comprehensive representation of autonomic nervous system responses to different pain intensities. Our framework processes these CWT images through a sophisticated quantum-classical hybrid architecture, where edge devices perform initial preprocessing and feature extraction while maintaining data privacy. The cornerstone of our system is a Quantum Convolutional Hybrid Neural Network (QCHNN) that harnesses quantum entanglement properties to enhance feature detection and classification robustness. This quantum-enhanced approach is seamlessly integrated into a federated learning framework, allowing distributed training across multiple healthcare facilities while preserving patient privacy through secure aggregation protocols. The QCHNN demonstrated remarkable performance, achieving a classification accuracy of 94.8% in pain level assessment, significantly outperforming traditional machine learning approaches.

A multilayer hierarchical structure for an intelligent analysis system is described in this paper. Four levels (patients', measurement, Web-based, and interpreting) are used to collect massive amounts from clinical information and analyze it with both traditional and artificial intelligent methods. To support this, a novel fuzzy pain demand (FPD) index derived from the interval of each bolus of patient-controlled analgesia (PCA) is designed and documented in a large-scale clinical survey. The FPD index is modeled according to a fuzzy modeling algorithm to interpret the self-titration of the drug delivery. A total of 255 patients receiving intravenous PCA using morphine (1 mg/ml) tested this index by offline analysis from this system. We found the FPD index modeled from a fuzzy modeling algorithm to interpret the self-titration of the drug delivery can show the patients' dynamic demand and past efforts to overcome the postoperative pain. Moreover, it could become an online system to monitor patients' demand or intent to treat their pain so these factors could be entered into a patient's chart along with temperature, blood pressure, pulse, and respiration rates when medical practitioners check the patients. Copyright ? 2006 IEEE.

Objective Fibromyalgia (FM) is a chronic condition characterised by widespread pain, and cognitive difficulties represent one of the most common symptoms of FM. However, subjective cognitive complaints (SCC) may not necessarily indicate significant abnormalities in objective cognitive performances, and there is limited research investigating the relationship between these two aspects. This study thus aims to analyse the differences between SCC and objective cognitive performance in FM patients and to explore their associations. Methods A total of 32 FM female patients (age: 50.91±7.06; years since diagnosis: 4.34±4.53) recruited in this study underwent a comprehensive assessment covering four domains: pain, depression, trait anxiety, SCC, and objective cognitive functions (memory, executive function, and information processing speed). Results Eighty-seven percent of patients experienced significant negative impacts from pain; meanwhile, 91% and 62% showed marked tendencies towards trait anxiety and depression, respectively. Additionally, 56% of patients reported significantly higher levels of SCC. However, less than one-third of patients demonstrated impairments in various cognitive functions. SCC significantly correlated with pain intensity, depression, information processing speed, and trait anxiety, with pain intensity being a significant predictor (R2=0.30). Furthermore, patients with significant SCC exhibited more abnormalities in pain, information processing speed, and trait anxiety compared to those without significant SCC. Conclusion SCC may not necessarily correlate with objective cognitive impairments and might be specifically linked to defective information processing speed. It thus merits that clinical assessments for FM patients should incorporate measurements of information processing speed to gain a comprehensive understanding of SCC in FM patients.

BACKGROUND

We have previously reported that dynamic preload variables predicted fluid responsiveness in adult patients with liver cirrhosis. However, pediatric patients with cirrhosis may present with unique hemodynamic characteristics, and therefore, the predictive accuracy of these variables in such patients must be clarified.

Proximal aortic dissection is frequently associated with cardiac tamponade. The treatment sometimes is difficult. We present a 69-year-old female patient who after repeated episodes of syncope received an open drainage of pericardial effusion that ended in a fatal outcome. She was also known to have mural thrombi in the aorta. However, preanesthetic trransesophageal echocardiography revealed besides pericardial effusion, also dilatation of aortic root which compressed both atria. She developed sudden cardiovascular collapse following drainage of pericardial effusion to which she succumbed in spite of vigorous resuscitation. We suggest that the patients with cardiac tamponade complicated by aortic dissection must receive direct aortic repair together with intraoperative pericardial drainage. Selective or single pericardiocentesis should better be avoided.

Objective: Primary hyperparathyroidism (PHPT) is a rare clinical entity in reproductive women. Unusual hypercalcemia causing pancreatitis in the peripartum period carries significant morbidity to both the fetus and the mother. Case Report: A 38-year-old woman developed a morbid course of intractable intra-abdominal abscess by pancreatitis, hydronephrosis by renal lithiasis, and unusual neurological presentations soon after delivery. Serial serum calcium level and imaging studies lead to the final diagnosis of PHPT due to a parathyroid adenoma. Data on 14 patients who suffered from pancreatitis due to hyperparathyroidism were collected from a MEDLINE search. The reasons for delayed diagnosis and literature review of acute pancreatitis in PHPT are discussed. Conclusion: Hypercalcemia can be masked during pregnancy and in severe pancreatitis, as was detected in about half of the case series. Clinicians should have a high level of suspicion of parathyroid adenoma in cases with a profound pancreatitis. Timely diagnosis and early therapeutic intervention are important to resolve complications and improve the outcomes of mothers and fetuses. ? 2014.

A 63-year-old man developed acute transverse myelitis (ATM) with a rapid progression of sensory and motor deficits and autonomic dysfunction 2 days after chest surgery. Thoracic epidural anesthesia/analgesia (TEA) had been administered in this case. Since the temporal and spatial relationships between TEA and ATM are so close, one may easily mistake the TEA as the cause. Therefore, we discuss here the differential diagnoses for cord damage after TEA and the characteristics of ATM, and suggest that it is unlikely that TEA is the cause of ATM in this case. Copyright ? 2013, Taiwan Society of Anesthesiologists. Published by Elsevier Taiwan LLC. All rights reserved.

Entropy as an estimate of complexity of the electroencephalogram is an effective parameter for monitoring the depth of anesthesia (DOA) during surgery. Multiscale entropy (MSE) is useful to evaluate the complexity of signals over different time scales. However, the limitation of the length of processed signal is a problem due to observing the variation of sample entropy (SE) on different scales. In this study, the adaptive resampling procedure is employed to replace the process of coarse-graining in MSE. According to the analysis of various signals and practical EEG signals, it is feasible to calculate the SE from the adaptive resampled signals, and it has the highly similar results with the original MSE at small scales. The distribution of the MSE of EEG during the whole surgery based on adaptive resampling process is able to show the detailed variation of SE in small scales and complexity of EEG, which could help anesthesiologists evaluate the status of patients. © 2012 by the authors.