Hasil untuk "Anesthesiology"

R. Wax, M. Christian

A global health emergency has been declared by the World Health Organization as the 2019-nCoV outbreak spreads across the world, with confirmed patients in Canada. Patients infected with 2019-nCoV are at risk for developing respiratory failure and requiring admission to critical care units. While providing optimal treatment for these patients, careful execution of infection control measures is necessary to prevent nosocomial transmission to other patients and to healthcare workers providing care. Although the exact mechanisms of transmission are currently unclear, human-to-human transmission can occur, and the risk of airborne spread during aerosol-generating medical procedures remains a concern in specific circumstances. This paper summarizes important considerations regarding patient screening, environmental controls, personal protective equipment, resuscitation measures (including intubation), and critical care unit operations planning as we prepare for the possibility of new imported cases or local outbreaks of 2019-nCoV. Although understanding of the 2019-nCoV virus is evolving, lessons learned from prior infectious disease challenges such as Severe Acute Respiratory Syndrome will hopefully improve our state of readiness regardless of the number of cases we eventually manage in Canada. Une urgence sanitaire mondiale a été déclarée par l’Organisation mondiale de la Santé alors que l’épidémie de 2019-nCoV se répand dans le monde et que des cas ont été confirmés au Canada. Les patients infectés par le 2019-nCoV sont à risque d’insuffisance respiratoire et peuvent nécessiter une admission à l’unité de soins intensifs. Lors d’une prise en charge optimale de ces patients, il est indispensable de prendre soin d’exécuter rigoureusement les mesures de contrôle des infections afin de prévenir la transmission nosocomiale aux autres patients et aux travailleurs de la santé prodiguant les soins. Bien que les mécanismes précis de transmission ne soient pas encore connus, la transmission d’humain à humain peut survenir, et le risque de dissémination aérienne pendant les interventions médicales générant des aérosols est préoccupant dans certaines circonstances spécifiques. Cet article résume des considérations importantes en ce qui touche au dépistage des patients, aux contrôles environnementaux, au matériel de protection personnelle, aux mesures de réanimation (y compris l’intubation), et à la planification des activités à l’unité de soins intensifs alors que nous nous préparons à la possibilité de nouveaux cas importés ou d’éclosions locales du 2019-nCoV. Bien que la compréhension du virus 2019-nCoV continue d’évoluer, nous espérons que les leçons retenues des éclosions précédentes de maladies infectieuses telles que le syndrome respiratoire aigu sévère nous permettront d’améliorer notre degré de préparation, indépendamment du nombre de cas que nous traiterons au Canada.

K. Kim

A novel ultra-short-acting benzodiazepine (BDZ), remimazolam (CNS 7056), has been designed by ‘soft drug’ development to achieve a better sedative profile than that of the current drugs. Notably, the esterase linkage in remimazolam permits rapid hydrolysis to inactivate metabolites by non-specific tissue esterase and induces a unique and favorable pharmacological profile, including rapid onset and offset of sedation and a predictable duration of action. Similar to other BDZs, its sedative effects can be reversed using flumazenil, a BDZ antagonist. The pharmacokinetics and pharmacodynamics of remimazolam are characterized by relatively high clearance, small steady-state volume of distribution, short elimination half-life, short context-sensitive half-life, and fast onset and recovery, indicating rapid elimination, minimal tissue accumulation, and good control. In addition, remimazolam possesses a superior safety profile, including low liability for cardiorespiratory depression and injection pain, making it a preferred hypnotic agent in various clinical settings. Early clinical investigations suggest that remimazolam is well tolerated and effective for procedural sedation and for induction and maintenance of general anesthesia. To date, however, the clinical use of remimazolam has been confined to a few volunteer studies and a limited number of clinical investigations. Therefore, further studies regarding its recovery issues or postoperative complications, characteristics of electroencephalogram changes, and cost-benefit analyses are required to facilitate its widespread use.

D. Hashimoto, E. Witkowski, Lei Gao et al.

Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence. The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management. This scoping review of artificial intelligence in anesthesiology summarizes six areas of research: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event/risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Supplemental Digital Content is available in the text.

M. Gómez-Ríos, J. Sastre, X. Onrubia-Fuertes et al.

The Airway Management section of the Spanish Society of Anesthesiology, Resuscitation, and Pain Therapy (SEDAR), the Spanish Society of Emergency Medicine (SEMES), and the Spanish Society of Otorhinolaryngology and Head and Neck Surgery (SEORL-CCC) present the Guide for the comprehensive management of difficult airway in adult patients. Its principles are focused on the human factor, cognitive processes for decision-making in critical situations, and optimization in the progression of strategies application to preserve adequate alveolar oxygenation in order to enhance safety and the quality of care. The document provides evidence-based recommendations, theoretical-educational tools, and implementation tools, mainly cognitive aids, applicable to airway management in the fields of anesthesiology, critical care, emergencies, and prehospital medicine. For this purpose, an extensive literature search was conducted following PRISMA-R guidelines and was analyzed using the GRADE methodology. Recommendations were formulated according to the GRADE methodology. Recommendations for sections with low-quality evidence were based on expert opinion through consensus reached via a Delphi questionnaire.

Hannah Lonsdale, Michael L. Burns, Richard H. Epstein et al.

Interest in the potential applications of artificial intelligence in medicine, anesthesiology, and the world at large has never been higher. The Anesthesia Research Council steering committee formed an anesthesiologist artificial intelligence expert workgroup charged with evaluating the current state of artificial intelligence in anesthesiology, providing examples of future artificial intelligence applications and identifying barriers to artificial intelligence progress. The workgroup’s findings are summarized here, starting with a brief introduction to artificial intelligence for clinicians, followed by overviews of current and anticipated artificial intelligence–focused research and applications in anesthesiology. Anesthesiology’s progress in artificial intelligence is compared to that of other medical specialties, and barriers to artificial intelligence development and implementation in our specialty are discussed. The workgroup’s recommendations address stakeholders in policymaking, research, development, implementation, training, and use of artificial intelligence–based tools for perioperative care. The artificial intelligence expert workgroup of the Anesthesia Research Council outlines the current state and prospects of artificial intelligence in anesthesiology.

J. Edwards, Daniel Ahn, Daniel Alcaraz et al.

Point-of-care ultrasound (POCUS) use has become ubiquitous in the field of anesthesiology. However, POCUS training curriculum in anesthesiology residencies vary widely. We performed a survey study to better understand the structure of existing POCUS training and identify barriers to instituting a POCUS curriculum for anesthesiology trainees.

Xiang Feng, Wentao Jiang, Zengmao Wang et al.

The application of large language models (LLMs) in the medical field has garnered significant attention, yet their reasoning capabilities in more specialized domains like anesthesiology remain underexplored. To bridge this gap, we introduce AnesSuite, the first comprehensive dataset suite specifically designed for anesthesiology reasoning in LLMs. The suite features AnesBench, an evaluation benchmark tailored to assess anesthesiology-related reasoning across three levels: factual retrieval (System 1), hybrid reasoning (System 1.x), and complex decision-making (System 2). Alongside this benchmark, the suite includes three training datasets that provide an infrastructure for continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning with verifiable rewards (RLVR). Leveraging this suite, we develop Morpheus, the first baseline model collection for anesthesiology reasoning. Despite undergoing limited training with SFT and group relative policy optimization (GRPO), Morpheus not only achieves substantial improvements in anesthesiology that rival larger-scale models, but also demonstrates enhanced reasoning capabilities across general medical and broad-domain benchmarks. Furthermore, through comprehensive evaluations and experiments, we analyze the key factors influencing anesthesiology reasoning performance, including model characteristics, training strategies and training data. Both AnesSuite and Morpheus will be open-sourced at https://github.com/MiliLab/AnesSuite.

Anjali Jha, Wanqing Chen, Maxim Eckmann et al.

Clinician scheduling remains a persistent challenge due to limited clinical resources and fluctuating demands. This complexity is especially acute in large academic anesthesiology departments as physicians balance responsibilities across multiple clinical sites with conflicting priorities. Further, scheduling must account for individual clinical and lifestyle preferences to ensure job satisfaction and well-being. Traditional approaches, often based on statistical or rule-based optimization models, rely on structured data and explicit domain knowledge. However, these methods often overlook unstructured information, e.g., free-text notes from routinely administered clinician well-being surveys and scheduling platforms. These notes may reveal implicit and underutilized clinical resources. Neglecting such information can lead to misaligned schedules, increased burnout, overlooked staffing flexibility, and suboptimal utilization of available resources. To address this gap, we propose a predict-then-optimize framework that integrates classification-based clinician availability predictions with a mixed-integer programming schedule optimization model. Large language models (LLMs) are employed to extract actionable preferences and implicit constraints from unstructured schedule notes, enhancing the reliability of availability predictions. These predictions then inform the schedule optimization considering four objectives: first, ensuring clinical full-time equivalent compliance, second, reducing workload imbalances by enforcing equitable proportions of shift types, third, maximizing clinician availability for assigned shifts, and fourth, schedule consistency. By combining the interpretive power of LLMs with the rigor of mathematical optimization, our framework provides a robust, data-driven solution that enhances operational efficiency while supporting equity and clinician well-being.

Mahmoud Balata, Mohamed Ibrahim Gbreel, Mohamed Hamouda Elkasaby et al.

Abstract Background Despite the promising results of both MitraClip and PASCAL systems for the treatment of mitral regurgitation (MR), there is limited data on the comparison of both systems regarding their safety and efficacy. We aim to compare both systems for MR. Materials and methods Five databases were searched until October 2024. Original studies were only included and critically appraised using an adapted version of the Newcastle–Ottawa scale for observational cohort studies and the Cochrane risk of bias tool for randomized controlled trials. The risk ratio (RR) and mean difference (MD) with their corresponding 95% confidence interval (95% CI). Results From the database search, we identified 197 studies, of which eight studies comprising 1,612 patients who underwent transcatheter edge-to-edge repair with either MitraClip or PASCAL were included in this meta-analysis. The statistical analysis revealed no significant difference between the two devices in achieving a two-grade reduction in MR severity (RR = 0.95; 95% CI: [0.86, 1.04]; p = 0.28), one-grade reduction (RR = 1.17; 95% CI: [0.92, 1.49]; p = 0.19), or in cases with no improvement (RR = 1.23; 95% CI: [0.79, 1.90]; p = 0.36). Additionally, there were no significant differences between PASCAL and MitraClip regarding procedure time, procedural success, reinterventions, or all-cause mortality. However, PASCAL trended towards better residual MR reduction, although this was accompanied by moderate heterogeneity. Both devices demonstrated comparable safety profiles and were effective in reducing MR and improving cardiac function. Conclusion MitraClip and PASCAL devices showed comparable safety profiles and procedural success rates. However, the analysis did not reveal a statistically significant difference between the two devices in reducing the severity of MR.

Chuanzhe Wang, Jie Lv, Mengyi Yang et al.

Cardiovascular diseases (CVD) are the leading global threat to human health. The clinical application of vascular stents improved the survival rates and quality of life for patients with cardiovascular diseases. However, despite the benefits stents bring to patients, there are still notable complications such as thrombosis and in-stent restenosis (ISR). Surface modification techniques represent an effective strategy to enhance the clinical efficacy of vascular stents and reduce complications. This paper reviews the development strategies of vascular stents based on surface functional coating technologies aimed at addressing the limitations in clinical application, including the inhibition of intimal hyperplasia, promotion of re-endothelialization. These strategies have improved endothelial repair and inhibited vascular remodeling, thereby promoting vascular healing post-stent implantation. However, the pathological microenvironment of target vessels and the lipid plaques are key pathological factors in the development of atherosclerosis (AS) and impaired vascular repair after percutaneous coronary intervention (PCI). Therefore, restoring normal physiological environment and removing the plaques are also treatment focuses after PCI for promoting vascular repair. Unfortunately, research in this area is limited. This paper reviews the advancements in vascular stents based on surface engineering technologies over the past decade, providing guidance for the development of stents.

Selin Erel, Aslıhan G. Kılıç

Background: Existing studies on anesthesiologists’ attitudes toward artificial intelligence (AI) leave a global understanding underexplored. This cross-sectional study aims to investigate Turkish anesthesiologists’ attitudes toward AI, examining its perceived benefits, limitations, and associated ethical concerns. Insights from this study aim to enhance understanding of AI’s role in anesthesiology within a cultural and ethical context. Methods: This nationwide study surveyed Turkish anesthesiologists. Descriptive statistics summarized categorical variables, Pearson’s Chi-square test compared variables between groups. Binary logistic regression analyzed associations between demographic factors and positive attitudes toward AI. Results: Among 293 valid responses, 69.6% of participants expressed positive attitudes toward AI. Gender (P = 0.01), employment setting (P < 0.001), and prior AI experience (P < 0.001) were significant predictors of positive attitudes. AI applications most frequently endorsed included preoperative assessments (93.1%), academic support (95.2%), and medical education (91.2%). Ethical concerns were prominent, with liability ambiguity (87.3%) and privacy issues (62.8%) being the most cited. Logistic regression revealed that participants aged 46–55 were significantly more likely to exhibit positive attitudes (OR = 3.744, P = 0.03), while those with over 15 years of experience were less likely to do so (OR = 0.105, P = 0.04). Conclusions: Turkish anesthesiologists exhibited predominantly positive attitudes toward AI, with prior experience playing a significant role in shaping perceptions. While AI was embraced for academic, educational, and noninvasive tasks, skepticism was present toward its application in invasive procedures. These findings highlight AI’s potential to enhance efficiency and patient safety while underscoring the need for comprehensive legal and ethical frameworks.

Thushara Madathil, Praveen K. Neema

Marzena Zielińska, Alicja Bartkowska-Śniatkowska, Magdalena Mierzewska-Schmidt et al.

The anaesthesia of a young child under 3 years of age is a challenge for every anaesthetist. The peculiarities of this group of patients, particularly neonates and infants, resulting primarily from differences in both physiology, anatomy and the immaturity of individual organs which translate into different pharmacokinetics and pharmacodynamics of the drugs used in anaesthesiology, underlie the significantly more frequently recorded critical events during anaesthesia compared with the adult patient population. Concerned about the safety of children undergoing anaesthesia and aiming to ensure the highest possible quality and uniform standard of anaesthetic services, the Expert Panel of the Section of Paediatric Anaesthesiology and Intensive Care has prepared a Section position paper on anaesthesia in children under 3 years of age.

A. Khan, R. Yunus, Mahad Sohail et al.

Misaki Fujimoto, Hidetaka Kuroda, Tomomi Katayama et al.

Purpose Large language models (LLMs) are increasingly employed across various fields, including medicine and dentistry. In the field of dental anesthesiology, LLM is expected to enhance the efficiency of information gathering, patient outcomes, and education. This study evaluates the performance of different LLMs in answering questions from the Japanese Dental Society of Anesthesiology Board Certification Examination (JDSABCE) to determine their utility in dental anesthesiology. Methods The study assessed three LLMs, ChatGPT-4 (OpenAI, San Francisco, California, United States), Gemini 1.0 (Google, Mountain View, California, United States), and Claude 3 Opus (Anthropic, San Francisco, California, United States), using multiple-choice questions from the 2020 to 2022 JDSABCE exams. Each LLM answered these questions three times. The study excluded questions involving figures or deemed inappropriate. The primary outcome was the accuracy rate of each LLM, with secondary analysis focusing on six subgroups: (1) basic physiology necessary for general anesthesia, (2) local anesthesia, (3) sedation and general anesthesia, (4) diseases and patient management methods that pose challenges in systemic management, (5) pain management, and (6) shock and cardiopulmonary resuscitation. Statistical analysis was performed using one-way ANOVA with Dunnett's multiple comparisons, with a significance threshold of p<0.05. Results ChatGPT-4 achieved a correct answer rate of 51.2% (95% CI: 42.78-60.56, p=0.003) and Claude 3 Opus 47.4% (95% CI: 43.45-51.44, p<0.001), both significantly higher than Gemini 1.0, which had a rate of 30.3% (95% CI: 26.53-34.14). In subgroup analyses, ChatGPT-4 and Claude 3 Opus demonstrated superior performance in basic physiology, sedation and general anesthesia, and systemic management challenges compared to Gemini 1.0. Notably, ChatGPT-4 excelled in questions related to systemic management (62.5%) and Claude 3 Opus in pain management (61.53%). Conclusions ChatGPT-4 and Claude 3 Opus exhibit potential for use in dental anesthesiology, outperforming Gemini 1.0. However, their current accuracy rates are insufficient for reliable clinical use. These findings have significant implications for dental anesthesiology practice and education, including educational support, clinical decision support, and continuing education. To enhance LLM utility in dental anesthesiology, it is crucial to increase the availability of high-quality information online and refine prompt engineering to better guide LLM responses.

Wafaa Harfaoui, Mustapha Alilou, A. E. El Adib et al.

Anesthesiology is considered a complex medical specialty. Its history has been marked by radical advances and profound transformations, owing to technical and pharmacological developments and innovations in the field, enabling us over the years to improve patient outcomes and perform longer, more complex surgical procedures on more fragile patients. However, anesthesiology has never been safe and free of challenges. Despite the advances made, it still faces risks associated with the practice of anesthesia, for both patients and healthcare professionals, and with some of the specific challenges encountered in low and middle-income countries. In this context, certain actions and initiatives must be carried out collaboratively. In addition, recent technologies and innovations such as simulation, genomics, artificial intelligence, and robotics hold promise for further improving patient safety in anesthesiology and overcoming existing challenges, making it possible to offer safer, more effective, and personalized anesthesia. However, this requires rigorous monitoring of ethical aspects and the reliability of the studies to reap the full benefits of the new technology. This literature review presents the evolution of anesthesiology over time, its current challenges, and its promising future. It underlines the importance of the new technologies and the need to pursue efforts and strengthen research in anesthesiology to overcome the persistent challenges and benefit from the advantages of the latest technology to guarantee safe, high-quality anesthesia with universal access.

Sara Lopes, G. Rocha, Luís Guimarães-Pereira

Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context. A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted. A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods. AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals’ skills of decision-making, diagnostic accuracy, and therapeutic response.

L. Aglio, Richard D. Urman

Zhonghai Wang, Jie Jiang, Yibing Zhan et al.

Medical large language models (LLMs) have gained popularity recently due to their significant practical utility. However, most existing research focuses on general medicine, and there is a need for in-depth study of LLMs in specific fields like anesthesiology. To fill the gap, we introduce Hypnos, a Chinese Anesthesia model built upon existing LLMs, e.g., Llama. Hypnos' contributions have three aspects: 1) The data, such as utilizing Self-Instruct, acquired from current LLMs likely includes inaccuracies. Hypnos implements a cross-filtering strategy to improve the data quality. This strategy involves using one LLM to assess the quality of the generated data from another LLM and filtering out the data with low quality. 2) Hypnos employs a general-to-specific training strategy that starts by fine-tuning LLMs using the general medicine data and subsequently improving the fine-tuned LLMs using data specifically from Anesthesiology. The general medical data supplement the medical expertise in Anesthesiology and enhance the effectiveness of Hypnos' generation. 3) We introduce a standardized benchmark for evaluating medical LLM in Anesthesiology. Our benchmark includes both publicly available instances from the Internet and privately obtained cases from the Hospital. Hypnos outperforms other medical LLMs in anesthesiology in metrics, GPT-4, and human evaluation on the benchmark dataset.

Guanbo Wang, Ting-Wei Ernie Liao, David Furfaro et al.

Objective: Randomized controlled trial (RCT) results often inform clinical decision-making, but the highly curated populations of trials and the care provided during the trial are often not reflective of real-world practice. The objective of this scoping review is to identify the ability of methods to transport findings from RCTs to target populations. Study design: A scoping review was conducted on the literature focusing on the transportability of the results from RCTs to observational cohorts. Each study was assessed based on the methodology used for transportability and the extent to which the treatment effect from the RCT was estimated in the target population in observational data. Results: A total of 15 published papers were included. The research topics include cardiovascular diseases, infectious diseases, psychiatry, oncology, orthopedics, anesthesiology, and hematology. These studies show that the findings from RCTs could be translated to real-world settings, with varying degrees of effect size and precision. In some cases, the estimated treatment effect for the target population were statistically significantly different from those in RCTs. Conclusion: Despite variations in the magnitude of effects between RCTs and real-world studies, transportability methods play an important role in effectively bridging the RCTs and real-world care delivery, offering valuable insights for evidence-based medicine.