Journal of clinical anesthesia
-
For years, postoperative cognitive outcomes have steadily garnered attention, and in the past decade, they have remained at the forefront. This prominence is primarily due to empirical research emphasizing their potential to compromise patient autonomy, reduce quality of life, and extend hospital stays, and increase morbidity and mortality rates, especially impacting elderly patients. The underlying pathophysiological process might be attributed to surgical and anaesthesiological-induced stress, leading to subsequent neuroinflammation, neurotoxicity, burst suppression and the development of hypercoagulopathy. ⋯ Implementing multi-faceted preoperative, intraoperative, and postoperative preventive initiatives has demonstrated the potential to decrease the incidence and duration of postoperative delirium. This further validates the importance of a holistic, team-based approach in enhancing patients' clinical and functional outcomes. This review aims to present evidence-based recommendations for preventing, diagnosing, and treating postoperative neurocognitive disorders with the Safe Brain Initiative approach.
-
Intraoperative sedation plays an important role in the management of regional anesthesia. Few studies have investigated the association of sedation during spinal anesthesia with postoperative mortality in older patients as a primary outcome. This study aimed to test the hypothesis that sedation during spinal anesthesia increases postoperative mortality in older patients undergoing hip fracture surgery. ⋯ There was no association between sedation during hip fracture surgery in older patients under spinal anesthesia and postoperative mortality. However, these results are limited to our population, and further prospective studies are needed to determine the safety of sedation.
-
Post-surgical chronic pain with a neuropathic component is usually more severe and leads to worse quality of life. We conducted this systematic review to examine the evidence of topical lidocaine for post-surgical neuropathic pain. ⋯ Topical lidocaine may lead to pain relief and is safe to use for patient with post-surgical pain, though its impact on quality of life is unclear. This review supports the use of topical lidocaine for patients with post-surgical pain, and reveals the evidence gap in topical lidocaine use. (Registration: PROSPERO CRD42021294100).
-
It is well recognized that amyloid protein can infiltrate many regions of the body. This can include the peripheral nerves, the liver, kidney, spleen, the gastrointestinal tract, and most importantly the myocardium. The amyloid proteins that cause cardiomyopathy may come from genetically altered liver genes (transthyretin amyloid, ATTR) or from the bone marrow with malignant plasma cells (light chain amyloid, AL) generating the aberrant protein. ⋯ In the operating room patients are exposed to dramatic hemodynamic changes and may have difficult airways, autonomic dysfunction, and conduction abnormalities. Although the topic of amyloidosis is well described in cardiology literature, it is underdiagnosed. The purpose of this review is to describe some of the pathophysiology behind the principle proteins that cause cardiac amyloidosis and to comprehensively describe perioperative considerations for anesthesia providers.
-
Observational Study
American society of anesthesiologists physical status classification significantly affects the performances of machine learning models in intraoperative hypotension inference.
To explore how American Society of Anesthesiologists (ASA) physical status classification affects different machine learning models in hypotension prediction and whether the prediction uncertainty could be quantified. ⋯ Different ASA physical status classes present different data distributions, and thus calls for distinct machine learning models to improve prediction accuracy and reduce predictive uncertainty. Uncertainty quantification enabled by Bayesian inference provides valuable information for clinicians as an additional metric to evaluate performance of machine learning models for medical decision making.