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British Journal of Anaesthesia Aug 2023Cao and colleagues present a follow-up analysis of a previous RCT among >1200 older adults (mean age 72 yr) undergoing cancer surgery, originally designed to evaluate...
Cao and colleagues present a follow-up analysis of a previous RCT among >1200 older adults (mean age 72 yr) undergoing cancer surgery, originally designed to evaluate the effect of propofol or sevoflurane general anaesthesia on delirium, here to evaluate the effect of anaesthetic technique on overall survival and recurrence-free survival. Neither anaesthetic technique conferred an advantage on oncological outcomes. We suggest that although it is entirely plausible that the observed results are truly robust neutral findings, the present study could be limited, like most published studies in the field, by its heterogeneity and understandable absence of underlying individual patient-specific tumour genomic data. We argue for a precision oncology approach to onco-anaesthesiology research that recognises that cancer is not one but rather many diseases and that tumour genomics (and multi-omics) is a fundamental determinant relating drugs to longer-term outcomes.
Topics: Humans; Aged; Sevoflurane; Propofol; Neoplasms; Anesthesiology; Follow-Up Studies; Precision Medicine; Anesthesia, General; Anesthetics; Analgesia; Oncologists
PubMed: 36863979
DOI: 10.1016/j.bja.2023.02.001 -
Anesthesia and Analgesia Oct 2023Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. Machine vision-based techniques are... (Review)
Review
Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. Machine vision-based techniques are already in clinical use in radiology, ophthalmology, and dermatology, where some applications currently equal or exceed the performance of specialty physicians in areas of image interpretation. While machine vision in anesthesia has many potential applications, its development remains in its infancy in our specialty. Early research for machine vision in anesthesia has focused on automated recognition of anatomical structures during ultrasound-guided regional anesthesia or line insertion; recognition of the glottic opening and vocal cords during video laryngoscopy; prediction of the difficult airway using facial images; and clinical alerts for endobronchial intubation detected on chest radiograph. Current machine vision applications measuring the distance between endotracheal tube tip and carina have demonstrated noninferior performance compared to board-certified physicians. The performance and potential uses of machine vision for anesthesia will only grow with the advancement of underlying machine vision algorithm technical performance developed outside of medicine, such as convolutional neural networks and transfer learning. This article summarizes recently published works of interest, provides a brief overview of techniques used to create machine vision applications, explains frequently used terms, and discusses challenges the specialty will encounter as we embrace the advantages that this technology may bring to future clinical practice and patient care. As machine vision emerges onto the clinical stage, it is critically important that anesthesiologists are prepared to confidently assess which of these devices are safe, appropriate, and bring added value to patient care.
Topics: Humans; Artificial Intelligence; Anesthesiology; Anesthesia, Conduction; Anesthesiologists; Algorithms
PubMed: 37712476
DOI: 10.1213/ANE.0000000000006679 -
International Journal of Obstetric... Aug 2023Competency-based training and active teaching methods are increasingly becoming accepted and utilized in medical schools and hospitals, and obstetric anesthesiology...
Competency-based training and active teaching methods are increasingly becoming accepted and utilized in medical schools and hospitals, and obstetric anesthesiology training is expected to follow this process. This article summarizes current modalities of obstetric anesthesiology training in five countries from various parts of the world. Analysis of these curricula shows that implementation of new educational methods is variable, incomplete, and lacking in data related to patient outcomes. Research in assessments and practical applications are required to avoid wide ranges of educational strategies.
Topics: Humans; Anesthesiology; Curriculum; Internship and Residency; Hospitals; Clinical Competence
PubMed: 37270857
DOI: 10.1016/j.ijoa.2023.103896 -
Current Pain and Headache Reports Nov 2023The use of simulation-based education (SBE) in medical training has expanded greatly and has grown to include high fidelity and task simulation along with hybrid models... (Review)
Review
PURPOSE OF REVIEW
The use of simulation-based education (SBE) in medical training has expanded greatly and has grown to include high fidelity and task simulation along with hybrid models using patient actors to enhance education and training of critical events as well as technical skills.
RECENT FINDINGS
In the field of anesthesiology, SBE has been particularly useful for crisis resource management and rare critical scenarios and new research into the use of SBE using task simulation for procedural skill development has been done highlighting the benefits to subspecialty procedural training. Medical simulation has become a common practice in medical training and research. SBE has demonstrated positive outcomes in improving technical skills, knowledge, comfort, and clinical performance. The widespread implementation of SBE in regional anesthesia and chronic pain training varies, with cost and availability being factors. Nonetheless, SBE has shown great potential in enhancing education and preparing physicians in subspecialties of anesthesia.
Topics: Humans; Chronic Pain; Anesthesiology; Anesthesia, Conduction
PubMed: 37715889
DOI: 10.1007/s11916-023-01164-9 -
Anesthesiology Clinics Mar 2024Preoperative care exists as part of perioperative continuum during which anesthesiologists and surgeons optimize patients for surgery. These multispecialty efforts are... (Review)
Review
Preoperative care exists as part of perioperative continuum during which anesthesiologists and surgeons optimize patients for surgery. These multispecialty efforts are important, particularly for patients with complex medical histories and those requiring major surgery. Preoperative care improves planning and determines the clinical pathway and discharge disposition. The role of nonmedical social factors in the preoperative planning is not well described in anesthesiology. Research to improve outcomes based on social factors is not well described for anesthesiologists but could be instrumental in decreasing disparities and advancing health equity in surgical patients.
Topics: Humans; Social Determinants of Health; Social Factors; Preoperative Care; Anesthesiology; Anesthesiologists
PubMed: 38278595
DOI: 10.1016/j.anclin.2023.07.002 -
Anesthesiology Clinics Dec 2023Anesthesiology presents a challenge to a traditional simplifying approach given the ever-increasing amount of medical data and a more demanding environment. Systems... (Review)
Review
Anesthesiology presents a challenge to a traditional simplifying approach given the ever-increasing amount of medical data and a more demanding environment. Systems anesthesiology is a modern approach to perioperative care, integrating the complexity of multifactorial knowledge and data to achieve a more adequate representation of reality, while including both patient-related medical aspects as well as economic and organizational challenges. We discuss the value of some innovative technologies such as the emergence of anesthesia information systems, the use of tele-medicine, predictive monitoring, or closed-loop systems as it pertains to the changes in the current standards of care in anesthesiology. Furthermore, we highlight the importance of systems anesthesiology in operating room planning, anesthesia research, and education.
Topics: Humans; Anesthesiology; Operating Rooms; Anesthesia
PubMed: 37838388
DOI: 10.1016/j.anclin.2023.05.006 -
Annals of Surgery Oct 2023Examine between-hospital and between-anesthesiologist variation in anesthesiology provider-volume (PV) and delivery of high-volume anesthesiology care.
OBJECTIVE
Examine between-hospital and between-anesthesiologist variation in anesthesiology provider-volume (PV) and delivery of high-volume anesthesiology care.
BACKGROUND
Better outcomes for anesthesiologists with higher PV of complex gastrointestinal cancer surgery have been reported. The factors linking anesthesiology practice and organization to volume are unknown.
METHODS
We identified patients undergoing elective esophagectomy, hepatectomy, and pancreatectomy using linked administrative health data sets (2007-2018). Anesthesiology PV was the annual number of procedures done by the primary anesthesiologist in the 2 years before the index surgery. High-volume anesthesiology was PV>6 procedures/year. Funnel plots to described variation in anesthesiology PV and delivery of high-volume care. Hierarchical regression models examined between-anesthesiologist and between-hospital variation in delivery of high-volume care use with variance partition coefficients (VPCs) and median odds ratios (MORs).
RESULTS
Among 7893 patients cared for at 17 hospitals, funnel plots showed variation in anesthesiology PV (median ranging from 1.5, interquartile range: 1-2 to 11.5, interquartile range: 8-16) and delivery of HV care (ranging from 0% to 87%) across hospitals. After adjustment, 32% (VPC 0.32) and 16% (VPC: 0.16) of the variation were attributable to between-anesthesiologist and between-hospital differences, respectively. This translated to an anesthesiologist MOR of 4.81 (95% CI, 3.27-10.3) and hospital MOR of 3.04 (95% CI, 2.14-7.77).
CONCLUSIONS
Substantial variation in anesthesiology PV and delivery of high-volume anesthesiology care existed across hospitals. The anesthesiologist and the hospital were key determinants of the variation in high-volume anesthesiology care delivery. This suggests that targeting anesthesiology structures of care could reduce variation and improve delivery of high-volume anesthesiology care.
Topics: Humans; Anesthesiology; Anesthesiologists; Digestive System Surgical Procedures; Delivery of Health Care; Gastrointestinal Neoplasms
PubMed: 36727738
DOI: 10.1097/SLA.0000000000005811 -
Journal of Surgical Education Sep 2023The objectives of this study were to use a multivariable regression model to determine what application factors made anesthesiology and surgery applicants more or less...
PURPOSE
The objectives of this study were to use a multivariable regression model to determine what application factors made anesthesiology and surgery applicants more or less likely to match into an anesthesiology or surgery residency program.
METHODS
Surgery and Anesthesiology applicants listed on the final National Resident Matching Program (NRMP) Rank Order Lists from WMC in the 2020-2021 application cycle were included in analysis. All applicant data were collected through the Electronic Residency Application Service (ERAS). All ERAS and letters of recommendation (LOR) data were deidentified and LOR were subsequently inputted into a linguistics software to analyze the language use in LOR. Descriptive analyses were conducted to compare variables between applicants that matched to a specific residency program and those who matched elsewhere. A multivariable regression model was then used to determine characteristics of anesthesiology and surgery applicants that were indicative of matching to a specific rank of residency program.
RESULTS
A total of 116 anesthesiology and 78 surgery applicants were included in final analysis. Analysis of anesthesiology applicants yielded four significant application characteristics that influenced matching to a higher or lower ranked residency program: USMLE Step 2 CK scores, medical school attended, insight category words in LOR, and anger category words in LOR. Similarly, analysis of surgery applicants yielded four significant characteristics: Race, USMLE Step 1 scores, insight category words, and see category words.
CONCLUSION
Our results demonstrated that specialties of anesthesiology and surgery considered different metrics regarding the residency application process. Among the many factors that were analyzed, USMLE scores and language in LOR were considered significant in both specialties. As the application process continues to evolve, we may see a shift in what application factors are considered more important than others.
Topics: United States; Anesthesiology; Internship and Residency; Electronics
PubMed: 37455190
DOI: 10.1016/j.jsurg.2023.06.021 -
Anesthesiology Jul 2023
Topics: Brain; Anesthesia; Anesthesiology
PubMed: 37279104
DOI: 10.1097/ALN.0000000000004571 -
Minerva Anestesiologica 2024Innovation and new technologies have always impacted significantly the anesthesiology practice all along the perioperative course, as it is recognized as one of the most... (Review)
Review
Innovation and new technologies have always impacted significantly the anesthesiology practice all along the perioperative course, as it is recognized as one of the most transformative medical specialties specifically regarding patient's safety. Beside a number of major changes in procedures, equipment, training, and organization that aggregated to establish a strong safety culture with effective practices, anesthesiology is also a stakeholder in disruptive innovation. The present review is not exhaustive and aims to provide an overview on how innovation could change and improve anesthesiology practices through some examples as telemedicine (TM), machine learning and artificial intelligence (AI). For example, postoperative complications can be accurately predicted by AI from automated real-time electronic health record data, matching physicians' predictive accuracy. Clinical workflow could be facilitated and accelerated with mobile devices and applications, assuming that these tools should remain at the service of patients and care providers. Care providers and patients connections have improved, thanks to these digital and innovative transformations, without replacing existing relationships between them. It also should give time back to physicians and nurses to better spend it in the perioperative care, and to provide "personalized" medicine keeping a high level of standard of care.
Topics: Humans; Artificial Intelligence; Anesthesiology; Telemedicine; Physicians
PubMed: 37526467
DOI: 10.23736/S0375-9393.23.17464-5