Journal of Clinical Anesthesia最新文献

英文中文

Flow-controlled versus pressure-controlled ventilation in thoracic surgery with one-lung ventilation – A randomized controlled trial

IF 5 2区医学 Q1 ANESTHESIOLOGY

Journal of Clinical Anesthesia

Pub Date : 2025-02-27 DOI: 10.1016/j.jclinane.2025.111785

Julia Abram MD , Patrick Spraider PhD , Judith Martini MD , Corinna Velik-Salchner MD , Hannes Dejaco MD , Florian Augustin MD , Gabriel Putzer MD , Tobias Hell PhD , Tom Barnes PhD , Dietmar Enk MD

Study objective

Comparison of flow-controlled ventilation (FCV) to standard of pressure-controlled ventilation (PCV) in thoracic surgery procedures requiring one-lung ventilation.

Design

Prospective, non-blinded, randomized, controlled trial.

Setting

Operating theater at a university hospital, Austria.

Patients

Patients scheduled for elective, thoracic surgery.

Interventions

Participants received ventilation randomly either with FCV or PCV per-protocol for the duration of anesthesia.

Measurements

The primary endpoint was oxygenation assessed by paO₂ / FiO₂ ratio 30 min after the start of OLV. Secondary endpoints included the required minute volume for CO₂ removal, applied mechanical power and incidence of postoperative pulmonary complications.

Main results

A total of 46 patients were enrolled and 43 included in the primary analysis. The primary endpoint paO₂ / FiO₂ ratio was significantly higher in the FCV group (n = 21) compared to the control group (PCV n = 22) (187 vs 136 mmHg, MD 39 (95 % CI 1 to 75); p = 0.047). The required respiratory minute volume to obtain comparable mild hypercapnia during OLV was significantly lower in FCV (3.0 vs 4.5 l/min, MD -1.3 (95 % CI -1.9 to −0.8); p < 0.001). The applied mechanical power was also significantly lower (3.5 versus 7.6 J/min, MD -3.8 (95 % CI -5.3 to −2.7); p < 0.001).

Conclusions

In this single-center randomized controlled trial, flow-controlled ventilation improved gas exchange parameters in terms of oxygenation and carbon dioxide removal during one-lung ventilation in patients undergoing thoracic surgery and reduced the mechanical impact of artificial ventilation.

{"title":"Flow-controlled versus pressure-controlled ventilation in thoracic surgery with one-lung ventilation – A randomized controlled trial","authors":"Julia Abram MD , Patrick Spraider PhD , Judith Martini MD , Corinna Velik-Salchner MD , Hannes Dejaco MD , Florian Augustin MD , Gabriel Putzer MD , Tobias Hell PhD , Tom Barnes PhD , Dietmar Enk MD","doi":"10.1016/j.jclinane.2025.111785","DOIUrl":"10.1016/j.jclinane.2025.111785","url":null,"abstract":"<div><h3>Study objective</h3><div>Comparison of flow-controlled ventilation (FCV) to standard of pressure-controlled ventilation (PCV) in thoracic surgery procedures requiring one-lung ventilation.</div></div><div><h3>Design</h3><div>Prospective, non-blinded, randomized, controlled trial.</div></div><div><h3>Setting</h3><div>Operating theater at a university hospital, Austria.</div></div><div><h3>Patients</h3><div>Patients scheduled for elective, thoracic surgery.</div></div><div><h3>Interventions</h3><div>Participants received ventilation randomly either with FCV or PCV per-protocol for the duration of anesthesia.</div></div><div><h3>Measurements</h3><div>The primary endpoint was oxygenation assessed by paO<sub>2</sub> / FiO<sub>2</sub> ratio 30 min after the start of OLV. Secondary endpoints included the required minute volume for CO<sub>2</sub> removal, applied mechanical power and incidence of postoperative pulmonary complications.</div></div><div><h3>Main results</h3><div>A total of 46 patients were enrolled and 43 included in the primary analysis. The primary endpoint paO<sub>2</sub> / FiO<sub>2</sub> ratio was significantly higher in the FCV group (<em>n</em> = 21) compared to the control group (PCV <em>n</em> = 22) (187 vs 136 mmHg, MD 39 (95 % CI 1 to 75); <em>p</em> = 0.047). The required respiratory minute volume to obtain comparable mild hypercapnia during OLV was significantly lower in FCV (3.0 vs 4.5 l/min, MD -1.3 (95 % CI -1.9 to −0.8); <em>p</em> < 0.001). The applied mechanical power was also significantly lower (3.5 versus 7.6 J/min, MD -3.8 (95 % CI -5.3 to −2.7); p < 0.001).</div></div><div><h3>Conclusions</h3><div>In this single-center randomized controlled trial, flow-controlled ventilation improved gas exchange parameters in terms of oxygenation and carbon dioxide removal during one-lung ventilation in patients undergoing thoracic surgery and reduced the mechanical impact of artificial ventilation.</div></div>","PeriodicalId":15506,"journal":{"name":"Journal of Clinical Anesthesia","volume":"103 ","pages":"Article 111785"},"PeriodicalIF":5.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decision-making profiles of anesthetists in selecting neuromuscular blocking agents for general anesthesia: A survey study

IF 5 2区医学 Q1 ANESTHESIOLOGY

Journal of Clinical Anesthesia

Pub Date : 2025-02-24 DOI: 10.1016/j.jclinane.2025.111790

Chloe Zanoni Msc , Mathieu Servant PhD , Guillaume Besch MD, PhD , Francis Berthier MD , Emmanuel Samain MD, PhD , Sebastien Pili-Floury MD, PhD , Djamila Bennabi MD, PhD , David Ferreira MD, PhD

引用次数: 0

Pain medication tapering in chronic pain patients: a concept analysis

IF 5 2区医学 Q1 ANESTHESIOLOGY

Journal of Clinical Anesthesia

Pub Date : 2025-02-22 DOI: 10.1016/j.jclinane.2025.111784

Elke Wuyts , Frenn Bultinck , Lisa Goudman , Dries Ceulemans , Cleo Lina Crunelle , Dominique Van de Velde , Hubert Van Puyenbroeck , Maarten Moens

Study objective

When risks and side effects of pain medication use outweigh its benefits, pain medication tapering (PMT) should be considered. PMT gained prominence in the treatment plan for patients with chronic pain (CP) and consist of heterogeneous components. This study aims to clarify the concept of PMT by conceptualizing essential components for use in CP patients.

Design

Concept analysis based on the eight-step method of Walker and Avant.

Data sources

A comprehensive literature search up to July 2023 was performed in six databases: MEDLINE (via PubMed), Web of Science, Embase, Scopus, PsychINFO and the Cochrane database.

Patients

CP patients on long-term pain medication therapy to whom PMT is beneficial.

Interventions

Attributes, illustrative cases, antecedents, consequences and empirical referents were developed. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines were used for transparency and reproducibility of the search, and to increase readability and clarity.

Main results

Out of 4,162 articles, 110 articles were included. Six attributes were identified: medication use and decrease, support, patient preparation/education, personalization, controlling and monitoring throughout and following tapering, and alternative treatments for pain relief. Three cases were developed, illustrating PMT programs containing all, some or none of the attributes. Antecedents such as suitability for tapering, convinced patient, experienced multidisciplinary team and well-established patient-physician relationship were identified, and consequences were described according to the International Classification of Functioning, Disability and Health, with results predominantly found in the body functions and structures category.

Conclusion

Conceptualization of PMT for patients with CNCP creates a common ground for improving current knowledge about PMT programs and can serve as a starting point for development of future research into PMT interventions.

{"title":"Pain medication tapering in chronic pain patients: a concept analysis","authors":"Elke Wuyts , Frenn Bultinck , Lisa Goudman , Dries Ceulemans , Cleo Lina Crunelle , Dominique Van de Velde , Hubert Van Puyenbroeck , Maarten Moens","doi":"10.1016/j.jclinane.2025.111784","DOIUrl":"10.1016/j.jclinane.2025.111784","url":null,"abstract":"<div><h3>Study objective</h3><div>When risks and side effects of pain medication use outweigh its benefits, pain medication tapering (PMT) should be considered. PMT gained prominence in the treatment plan for patients with chronic pain (CP) and consist of heterogeneous components. This study aims to clarify the concept of PMT by conceptualizing essential components for use in CP patients.</div></div><div><h3>Design</h3><div>Concept analysis based on the eight-step method of Walker and Avant.</div></div><div><h3>Data sources</h3><div>A comprehensive literature search up to July 2023 was performed in six databases: MEDLINE (via PubMed), Web of Science, Embase, Scopus, PsychINFO and the Cochrane database.</div></div><div><h3>Patients</h3><div>CP patients on long-term pain medication therapy to whom PMT is beneficial.</div></div><div><h3>Interventions</h3><div>Attributes, illustrative cases, antecedents, consequences and empirical referents were developed. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines were used for transparency and reproducibility of the search, and to increase readability and clarity.</div></div><div><h3>Main results</h3><div>Out of 4,162 articles, 110 articles were included. Six attributes were identified: medication use and decrease, support, patient preparation/education, personalization, controlling and monitoring throughout and following tapering, and alternative treatments for pain relief. Three cases were developed, illustrating PMT programs containing all, some or none of the attributes. Antecedents such as suitability for tapering, convinced patient, experienced multidisciplinary team and well-established patient-physician relationship were identified, and consequences were described according to the International Classification of Functioning, Disability and Health, with results predominantly found in the body functions and structures category.</div></div><div><h3>Conclusion</h3><div>Conceptualization of PMT for patients with CNCP creates a common ground for improving current knowledge about PMT programs and can serve as a starting point for development of future research into PMT interventions.</div></div>","PeriodicalId":15506,"journal":{"name":"Journal of Clinical Anesthesia","volume":"102 ","pages":"Article 111784"},"PeriodicalIF":5.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing the accuracy of ChatGPT in interpreting blood gas analysis results ChatGPT-4 in blood gas analysis

IF 5 2区医学 Q1 ANESTHESIOLOGY

Journal of Clinical Anesthesia

Pub Date : 2025-02-21 DOI: 10.1016/j.jclinane.2025.111787

Engin İhsan Turan , Abdurrahman Engin Baydemir , Anıl Berkay Balıtatlı , Ayça Sultan Şahin

Background

Arterial blood gas (ABG) analysis is a critical component of patient management in intensive care units (ICUs), operating rooms, and general wards, providing essential information on acid-base balance, oxygenation, and metabolic status. Interpretation requires a high level of expertise, potentially leading to variability in accuracy. This study explores the feasibility and accuracy of ChatGPT-4, an AI-based model, in interpreting ABG results compared to experienced anesthesiologists.

Methods

This prospective observational study, approved by the institutional ethics board, included 400 ABG samples from ICU patients, anonymized and assessed by ChatGPT-4. The model analyzed parameters including acid-base status, oxygenation, hemoglobin levels, and metabolic markers, and provided both diagnostic and treatment recommendations. Two anesthesiologists, trained in ABG interpretation, independently evaluated the model's predictions to determine accuracy in potential diagnoses and treatment.

Results

ChatGPT-4 achieved high accuracy across most ABG parameters, with 100 % accuracy for pH, oxygenation, sodium, and chloride. Hemoglobin accuracy was 92.5 %, while bilirubin interpretation showed limitations at 72.5 %. In several cases, the model recommended unnecessary bicarbonate treatment, suggesting an area for improvement in clinical judgment for acid-base balance management. The model's overall performance was statistically significant across most parameters (p < 0.05).

Discussion

ChatGPT-4 demonstrated potential as a supplementary tool for ABG interpretation in high-demand clinical settings, supporting rapid, reliable decision-making. However, the model's limitations in interpreting complex metabolic markers highlight the need for clinician oversight. Future refinements should focus on enhancing AI training for nuanced metabolic interpretation, particularly for markers like bilirubin, to ensure safe and effective application across diverse clinical contexts.

{"title":"Assessing the accuracy of ChatGPT in interpreting blood gas analysis results ChatGPT-4 in blood gas analysis","authors":"Engin İhsan Turan , Abdurrahman Engin Baydemir , Anıl Berkay Balıtatlı , Ayça Sultan Şahin","doi":"10.1016/j.jclinane.2025.111787","DOIUrl":"10.1016/j.jclinane.2025.111787","url":null,"abstract":"<div><h3>Background</h3><div>Arterial blood gas (ABG) analysis is a critical component of patient management in intensive care units (ICUs), operating rooms, and general wards, providing essential information on acid-base balance, oxygenation, and metabolic status. Interpretation requires a high level of expertise, potentially leading to variability in accuracy. This study explores the feasibility and accuracy of ChatGPT-4, an AI-based model, in interpreting ABG results compared to experienced anesthesiologists.</div></div><div><h3>Methods</h3><div>This prospective observational study, approved by the institutional ethics board, included 400 ABG samples from ICU patients, anonymized and assessed by ChatGPT-4. The model analyzed parameters including acid-base status, oxygenation, hemoglobin levels, and metabolic markers, and provided both diagnostic and treatment recommendations. Two anesthesiologists, trained in ABG interpretation, independently evaluated the model's predictions to determine accuracy in potential diagnoses and treatment.</div></div><div><h3>Results</h3><div>ChatGPT-4 achieved high accuracy across most ABG parameters, with 100 % accuracy for pH, oxygenation, sodium, and chloride. Hemoglobin accuracy was 92.5 %, while bilirubin interpretation showed limitations at 72.5 %. In several cases, the model recommended unnecessary bicarbonate treatment, suggesting an area for improvement in clinical judgment for acid-base balance management. The model's overall performance was statistically significant across most parameters (<em>p</em> < 0.05).</div></div><div><h3>Discussion</h3><div>ChatGPT-4 demonstrated potential as a supplementary tool for ABG interpretation in high-demand clinical settings, supporting rapid, reliable decision-making. However, the model's limitations in interpreting complex metabolic markers highlight the need for clinician oversight. Future refinements should focus on enhancing AI training for nuanced metabolic interpretation, particularly for markers like bilirubin, to ensure safe and effective application across diverse clinical contexts.</div></div>","PeriodicalId":15506,"journal":{"name":"Journal of Clinical Anesthesia","volume":"102 ","pages":"Article 111787"},"PeriodicalIF":5.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial: The global need for standardized education in airway management.

IF 5 2区医学 Q1 ANESTHESIOLOGY

Journal of Clinical Anesthesia

Pub Date : 2025-02-21 DOI: 10.1016/j.jclinane.2025.111781

Gregor Massoth, Maria Wittmann

引用次数: 0

The expanding role of critical care anesthesiologists outside the ICU.

IF 5 2区医学 Q1 ANESTHESIOLOGY

Journal of Clinical Anesthesia

Pub Date : 2025-02-20 DOI: 10.1016/j.jclinane.2025.111779

Siddharth Dave, Brigid Flynn, Kunal Karamchandani

引用次数: 0

A closer look at a decade of industry payments to anesthesiologists

IF 5 2区医学 Q1 ANESTHESIOLOGY

Journal of Clinical Anesthesia

Pub Date : 2025-02-19 DOI: 10.1016/j.jclinane.2025.111775

Caitlin Sebastian BS , Catherine Cha MD , Brittany N. Burton MD, MHS, MAS

引用次数: 0

Machine learning or traditional statistical methods for predictive modelling in perioperative medicine: A narrative review

IF 5 2区医学 Q1 ANESTHESIOLOGY

Journal of Clinical Anesthesia

Pub Date : 2025-02-19 DOI: 10.1016/j.jclinane.2025.111782

Jason Mann , Mathew Lyons , John O'Rourke , Simon Davies

Prediction of outcomes in perioperative medicine is key to decision-making and various prediction models have been created to help quantify and communicate those risks to both patients and clinicians. Increasingly, machine learning (ML) is being favoured over more traditional techniques to improve prediction of outcomes, however, the studies are of varying quality. It is also not known whether any increase in predictive performance using ML algorithms transpires into a clinically meaningful benefit. This coupled with the difficulty in interrogating ML algorithms is a potential cause of concern within the medical community. In this review, we provide a concise appraisal of studies which develop perioperative predictive ML models and compare predictive performance to traditional statistical models.

The search strategy, title and abstract screening, and full-text reviews produced 37 studies for data extraction. Initially designed as a systematic review but due to the heterogeneity of the population and outcomes, was written in the narrative.

Perioperative ML and traditional predictive models continue to be developed and published across a range of populations. This review highlights several studies which show that ML can enhance perioperative prediction models, although this is not universal, and performance for both methods remain context dependent. By focusing on relevant patient-centred outcomes, model interpretability, external validation, and maintaining high standards of reporting and methodological transparency, researchers can develop ML models alongside traditional methods to enhance clinical decision-making and improve patient care.

{"title":"Machine learning or traditional statistical methods for predictive modelling in perioperative medicine: A narrative review","authors":"Jason Mann , Mathew Lyons , John O'Rourke , Simon Davies","doi":"10.1016/j.jclinane.2025.111782","DOIUrl":"10.1016/j.jclinane.2025.111782","url":null,"abstract":"<div><div>Prediction of outcomes in perioperative medicine is key to decision-making and various prediction models have been created to help quantify and communicate those risks to both patients and clinicians. Increasingly, machine learning (ML) is being favoured over more traditional techniques to improve prediction of outcomes, however, the studies are of varying quality. It is also not known whether any increase in predictive performance using ML algorithms transpires into a clinically meaningful benefit. This coupled with the difficulty in interrogating ML algorithms is a potential cause of concern within the medical community. In this review, we provide a concise appraisal of studies which develop perioperative predictive ML models and compare predictive performance to traditional statistical models.</div><div>The search strategy, title and abstract screening, and full-text reviews produced 37 studies for data extraction. Initially designed as a systematic review but due to the heterogeneity of the population and outcomes, was written in the narrative.</div><div>Perioperative ML and traditional predictive models continue to be developed and published across a range of populations. This review highlights several studies which show that ML can enhance perioperative prediction models, although this is not universal, and performance for both methods remain context dependent. By focusing on relevant patient-centred outcomes, model interpretability, external validation, and maintaining high standards of reporting and methodological transparency, researchers can develop ML models alongside traditional methods to enhance clinical decision-making and improve patient care.</div></div>","PeriodicalId":15506,"journal":{"name":"Journal of Clinical Anesthesia","volume":"102 ","pages":"Article 111782"},"PeriodicalIF":5.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preoperative LDL-C and major cardiovascular and cerebrovascular events after non-cardiac surgery

IF 5 2区医学 Q1 ANESTHESIOLOGY

Journal of Clinical Anesthesia

Pub Date : 2025-02-16 DOI: 10.1016/j.jclinane.2025.111783

David Rehe MD, MBA , Varun Subashchandran MD , Yan Zhang MPH , Germaine Cuff PhD , Mitchell Lee MD , Jeffrey S. Berger MD, MS , Nathaniel R. Smilowitz MD, MS

Study objective

To determine whether preoperative LDL-C concentration affects the risk of perioperative major adverse cardiovascular or cerebrovascular events (MACCE) after noncardiac surgery.

Design

Single center retrospective cohort study.

Setting

Hospital (including medical and surgical floor, intensive care unit) and patient disposition location (including the patient's home or any other receiving facility).

Patients

43,348 non-cardiac surgeries at NYU Langone Health between January 2016 and September 2020.

Interventions

Patients were grouped based on preoperative LDL-C.

Measurements

Complete serum lipid panel obtained within one year prior to the date of noncardiac surgery and rate of perioperative MACCE, defined as a composite of in-hospital non-fatal myocardial infarction, in-hospital acute ischemic stroke, myocardial injury after noncardiac surgery, and death from any cause within 30 days of surgery.

Main results

Perioperative MACCE occurred in 1093 patients (2.5 %) overall. After multivariable adjustment, odds of MACCE were significantly lower in patients with higher (≥100 mg/dL) versus lower (<100 mg/dL) LDL-C (adjusted odds ratio [aOR] 0.783, 95 % CI, 0.660–0.926]).

Conclusions

In a large cohort of patients undergoing non-cardiac surgery at a major academic health system in New York City, lower LDL-C concentrations were not associated with a lower incidence of perioperative MACCE. Further investigation into modifiable perioperative cardiovascular risk factors is needed to improve perioperative outcomes.

{"title":"Preoperative LDL-C and major cardiovascular and cerebrovascular events after non-cardiac surgery","authors":"David Rehe MD, MBA , Varun Subashchandran MD , Yan Zhang MPH , Germaine Cuff PhD , Mitchell Lee MD , Jeffrey S. Berger MD, MS , Nathaniel R. Smilowitz MD, MS","doi":"10.1016/j.jclinane.2025.111783","DOIUrl":"10.1016/j.jclinane.2025.111783","url":null,"abstract":"<div><h3>Study objective</h3><div>To determine whether preoperative LDL-C concentration affects the risk of perioperative major adverse cardiovascular or cerebrovascular events (MACCE) after noncardiac surgery.</div></div><div><h3>Design</h3><div>Single center retrospective cohort study.</div></div><div><h3>Setting</h3><div>Hospital (including medical and surgical floor, intensive care unit) and patient disposition location (including the patient's home or any other receiving facility).</div></div><div><h3>Patients</h3><div>43,348 non-cardiac surgeries at NYU Langone Health between January 2016 and September 2020.</div></div><div><h3>Interventions</h3><div>Patients were grouped based on preoperative LDL-C.</div></div><div><h3>Measurements</h3><div>Complete serum lipid panel obtained within one year prior to the date of noncardiac surgery and rate of perioperative MACCE, defined as a composite of in-hospital non-fatal myocardial infarction, in-hospital acute ischemic stroke, myocardial injury after noncardiac surgery, and death from any cause within 30 days of surgery.</div></div><div><h3>Main results</h3><div>Perioperative MACCE occurred in 1093 patients (2.5 %) overall. After multivariable adjustment, odds of MACCE were significantly lower in patients with higher (≥100 mg/dL) versus lower (<100 mg/dL) LDL-C (adjusted odds ratio [aOR] 0.783, 95 % CI, 0.660–0.926]).</div></div><div><h3>Conclusions</h3><div>In a large cohort of patients undergoing non-cardiac surgery at a major academic health system in New York City, lower LDL-C concentrations were not associated with a lower incidence of perioperative MACCE. Further investigation into modifiable perioperative cardiovascular risk factors is needed to improve perioperative outcomes.</div></div>","PeriodicalId":15506,"journal":{"name":"Journal of Clinical Anesthesia","volume":"102 ","pages":"Article 111783"},"PeriodicalIF":5.0,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The rise of Zyn: Implications for anesthesiology and perioperative management

IF 5 2区医学 Q1 ANESTHESIOLOGY

Journal of Clinical Anesthesia

Pub Date : 2025-02-16 DOI: 10.1016/j.jclinane.2025.111780

Jamie Kim BS , Kazi Maisha BS , Rogelio Perez BS , Robert White MD, MS , Rohan Jotwani MD, MBA

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Journal of Clinical Anesthesia

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀