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Is there a unified definition of anesthesia? What exactly are we monitoring? Processed electroencephalography (pEEG) monitors may not reliably assess all anesthetic endpoints. This book explains how anesthesiologists should not blindly follow the displayed numbers when they adjust their anesthetic agents' doses. This book is based on the author's 30 years of experience researching the topic of Depth of Anesthesia (DOA) monitoring. Depth of Anesthesia monitors are pEEG Monitors. All conditions that would alter the EEG (these are many) would also alter the DOA displayed numbers so each reader should learn how to correctly interpret the displayed numbers.
In many instances physicians are confronted with various paradoxical Depth of Anesthesia monitors displaying inaccurate readings that do not concur with "clinically judged Anesthetic state" whether arising from an underlying pathophysiology alteration of the patients' own EEG cerebral function or those due to shortcomings in the performance and design of the DoA.
This book - very easy to read although the topic sounds very complicated - would like to represent a reference where anesthesiologists can go back to when they are confronted with such situations. Each chapter is focused on one contributing element that could influence depth of anesthesia monitoring. It reports cases or studies of displayed numbers that do not concur with clinically assessed depth of anesthesia; these are immediately followed by the documented scientific EEG explanations.
A book that both younger and older anesthetists should read to better understand how to use DOA monitors in their daily practice and that very much simplifies the topic.
Ashraf A. Dahaba is a former Privatdozent (Venia docendi) at the Medical University of Graz, Austria; Professor emeritus of Anesthesiology and Intensive Care Medicine at the University of Suez Canal Egypt, Guest Professor at the Fourth Military Medical University, Xi'an, China, Guest Professor at Zhe Jiang University, Hang Zhou, China and Consultant of Academic Anaesthesia General Medical Council, United Kingdom.
Dr. Dahaba has been a principal investigator as he founded and directs the Multicenter Clinical Anesthesia research group (Austria, China, US and Egypt). He also has an extensive publishing experience being the first author of over 60 full research papers in top Journals with high impact factor (i.e., Pharmacokinetics / Pharmacodynamics, Anesthesiology, Intensive Care Medicine, Pain, Clinical Neurology, Hepatology and Surgery). Professor Dahaba is also keen on statistical analysis models. He is also a Senior Editor for BioMed Central, BMC Anesthesiology and serves as a reviewer for more than 60 journals. Prof. Dahaba organized and chaired the scientific committee of "Sarajevo Anesthesia Forum" in Sarajevo Bosnia Herzegovina for 10 consecutive years (2009-2018); he also gave more than 240 presentations in International Congresses where he was often the Keynote speaker of the congress. Currently, Prof. Dahaba is working on developing readily available stand-alone tablet-operated (with no laptop cable connections) Artificial Intelligence (AI) Machine learning (ML) 3 Closed-Loop Autonomous /Augmented Anesthesia that could take the burden off the practicing anesthesiologists of calculating complicated infusion rate adjustments while giving anesthesiologists 2 free hands.
1. Depth Of Anesthesia (DOA) Monitors. History and Basic Structures.- 2. "Monitors" Terminologies.- 3. "Anesthesia" Definitions.- 4. Paradoxical Inhalational anesthetics, Nitrous Oxide, Ketamine and Propofol.- 5. Clinical Conditions that could alter processed electroencephalography depth of anesthesia monitoring.- 6. Effect of electromyographic (EMG) activity and Neuromuscular Blocking Agents (NMBAs) on Depth of Anesthesia monitoring. Achilles First Heel.- 7. Effect of Noxious stimulation and Anti-noxious Opioids on Depth of Anesthesia monitoring. Achilles Second Heel (!).- 8. Off-label use of bispectral index to grade conditions other than Depth of Anesthesia.- 9. Natural sleep and sedation monitoring in critically ill patients.- 10. Artificial Intelligence-Machine learning (AI-ML), Concerto 3-channels Closed-Loop automated system.
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