Background And Objectives: There are several factors in operating rooms that increase the risk of fire. Besides being an oxygen-enriched environment, it contains combustible materials and equipment with available ignition sources. Although fires in operating rooms are a relatively rare event, the consequences are potentially serious and mostly avoidable. We present a case report of a fire occurring in the surgical drape during a blepharoplasty in which oxygen was supplemented by nasal catheter.
Case Report: Female patient, 52-years old, without comorbidities, admitted to hospital for a bilateral blepharoplasty. After monitoring and venoclysis, the patient underwent intravenous sedation and additional oxygen given via spectacle-type catheter at a flow rate of4 L.min(-1), followed by local anesthesia in the eyelids. During surgery, the use of electric scalpel provoked combustion in the surgical drapes and burns on the patient's face.
Conclusions: Anesthesiologists play an important role preventing fire in operating rooms, as they can recognize possible ignition sources and rationally administer the oxygen, especially in open systems. The first step toward prevention is to be constantly aware of potential fire.
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http://dx.doi.org/10.1016/S0034-7094(12)70143-5 | DOI Listing |
Am J Transl Res
December 2024
Department of Anesthesiology, Huai'an First People's Hospital Huai'an, Jiangsu, China.
Objective: To evaluate the clinical effect of a subspecialty standardized temperature management process in a hybrid surgery for treating acute aortic dissection.
Methods: From January 2020 to June 2021, 102 patients who underwent hybrid surgery for acute aortic dissection in the Department of Cardiovascular Surgery at the Huai'an First People's Hospital were selected as the control group, receiving routine temperature maintenance measures. From August 2021 to November 2022, 105 similar patients from the same hospital were enrolled in the experimental group, where a subspecialty standardized temperature management process was implemented.
J Med Syst
January 2025
Edward J. Bloustein School of Planning and Public Policy, Rutgers, The State University of New Jersey, 255, Civic Square Building 33 Livingston Ave #400, New Brunswick, NJ, 08901, USA.
Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management.
View Article and Find Full Text PDFCan J Surg
January 2025
From the Department of Health and Rehabilitation Sciences, Faculty of Health Sciences, Western University, London, Ont. (Kelenc, Stephenson, Bryant); the School of Physical Therapy, Faculty of Health Sciences, Western University, London, Ont. (Bryant); the Division of Orthopedic Surgery, London Health Sciences Centre, University Hospital, London, Ont. (Lanting).
Background: Robotic surgery has seen substantial growth over the years and continues to show promise, with recent implementation into orthopedic surgery. There is limited literature available on patient attitudes and comfort level with robotic compared with conventional surgery. We aimed to develop an understanding of patient views on robot-assisted knee replacement to help the development of patient education materials and facilitate successful implementation.
View Article and Find Full Text PDFTohoku J Exp Med
January 2025
Department of Anesthesiology and Surgery, The Second Hospital, Lanzhou University.
J Med Syst
January 2025
Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments.
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