Articles: operative.
-
Neuronavigation improves intraoperative visualization of the cranial structures, which is valuable in percutaneous surgical treatments for patients with trigeminal neuralgia (TN) who are refractory to pharmacotherapy or reluctant to receive open surgery. The objective of this review was to evaluate the available neuronavigation-guided percutaneous surgical treatment modalities with cannulation of foramen ovale to TN, and their relative benefits and limitations. ⋯ Neuronavigation-guided percutaneous trigeminal rhizotomies showed possible superior pain relief outcomes to that of conventional rhizotomies in TN, with the benefits of radiation reduction and lower complication development rates. The limitations of neuronavigation remain its high cost and limited availability. Higher-quality prospective studies and randomized clinical trials of neuronavigation-guided percutaneous trigeminal rhizotomy were lacking.
-
Anesthesia and analgesia · Apr 2024
Randomized Controlled TrialVirtual Reality Distraction for Reducing Acute Postoperative Pain After Hip Arthroplasty: A Randomized Trial.
Relaxation and distraction provided by virtual reality presentations might be analgesic and reduce the need for opioid analgesia. We tested the hypothesis that a virtual reality program (AppliedVR) decreases acute postoperative pain and opioid requirements in patients recovering from hip arthroplasty. We also evaluated whether virtual reality distraction improves patient mobility and reduces the need for antiemetics. ⋯ We did not observe statistically significant or clinically meaningful reductions in average pain scores or opioid consumption. As used in our trial, virtual reality did not reduce acute postoperative pain.
-
Anesthesia and analgesia · Apr 2024
ReviewAnesthesiologists and Community Engagement: A Scoping Review of the Literature.
Millions of individuals require anesthesia services each year. Although anesthesia-associated mortality rates have declined, anesthetic-related morbidity remains high, particularly among vulnerable populations. Disparities in perioperative screening, optimization, surveillance, and follow-up contribute to worse outcomes in these populations. ⋯ Results indicate that most initiatives representing deeper levels of community engagement, at the collaborate or empower level, occur internationally. Efforts that occur in the United States tend to emphasize engagement of individual patients rather than communities. There is a need to pursue deeper, more meaningful community-engaged efforts within the field of anesthesiology at a local and national level.
-
J Clin Monit Comput · Apr 2024
ReviewClosed-loop anesthesia: foundations and applications in contemporary perioperative medicine.
A closed-loop automatically controls a variable using the principle of feedback. Automation within anesthesia typically aims to improve the stability of a controlled variable and reduce workload associated with simple repetitive tasks. This approach attempts to limit errors due to distractions or fatigue while simultaneously increasing compliance to evidence based perioperative protocols. ⋯ Despite their advantages, these tools still require that a well-trained practitioner maintains situation awareness, understands how closed-loop systems react to each variable, and is ready to retake control if the closed-loop systems fail. In the future, multiple input multiple output closed-loop systems will control anesthetic, fluid and vasopressor titration and may perhaps integrate other key systems, such as the anesthesia machine. Human supervision will nonetheless always be indispensable as situation awareness, communication, and prediction of events remain irreplaceable human factors.
-
Anesthesia and analgesia · Apr 2024
User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications.
Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians. ⋯ Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.