Literature DB >> 17342965

Depth of anesthesia monitoring.

T Andrew Bowdle1.   

Abstract

Depth-of-anesthesia monitoring with EEG or EEG combined with mLAER is becoming widely used in anesthesia practice. Evidence shows that this monitoring improves outcome by reducing the incidence of intra-operative awareness while reducing the average amount of anesthesia that is administered, resulting in faster wake-up and recovery, and perhaps reduced nausea and vomiting. As with any monitoring device, there are limitations in the use of the monitors and the anesthesiologist must be able to interpret the data accordingly. The limitations include the following. The currently available monitoring algorithms do not account for all anesthetic drugs, including ketamine, nitrous oxide and halothane. EMG and other high-frequency electrical artifacts are common and interfere with EEG interpretation. Data processing time produces a lag in the computation of the depth-of-anesthesia monitoring index. Frequently the EEG effects of anesthetic drugs are not good predictors of movement in response to a surgical stimulus because the main site of action for anesthetic drugs to prevent movement is the spinal cord. The use of depth-of-anesthesia monitoring in children is not as well understood as in adults. Several monitoring devices are commercially available. The BIS monitor is the most thoroughly studied and most widely used, but the amount of information about other monitors is growing. In the future, depth-of-anesthesia monitoring will probably help in further refining and better understanding the process of administering anesthesia.

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Year:  2006        PMID: 17342965     DOI: 10.1016/j.atc.2006.08.006

Source DB:  PubMed          Journal:  Anesthesiol Clin        ISSN: 1932-2275


  10 in total

1.  Using a short-term parameter of heart rate variability to distinguish awake from isoflurane anesthetic states.

Authors:  Hui-Hsun Huang; Yi-Hui Lee; Hsiao-Lung Chan; Yong-Ping Wang; Chi-Hsiang Huang; Shou-Zen Fan
Journal:  Med Biol Eng Comput       Date:  2008-04-15       Impact factor: 2.602

2.  The effects of low-dose ketamine on the analgesia nociception index (ANI) measured with the novel PhysioDoloris™ analgesia monitor: a pilot study.

Authors:  Laurent Bollag; Clemens M Ortner; Srdjan Jelacic; Cyril Rivat; Ruth Landau; Philippe Richebé
Journal:  J Clin Monit Comput       Date:  2014-07-26       Impact factor: 2.502

3.  Bispectral Index in Evaluating Effects of Sedation Depth on Drug-Induced Sleep Endoscopy.

Authors:  Yu-Lun Lo; Yung-Lun Ni; Tsai-Yu Wang; Ting-Yu Lin; Hsueh-Yu Li; David P White; Jr-Rung Lin; Han-Pin Kuo
Journal:  J Clin Sleep Med       Date:  2015-09-15       Impact factor: 4.062

4.  Evaluation of the SEDline to improve the safety and efficiency of conscious sedation.

Authors:  Thomas D Caputo; Michael A E Ramsay; Jeffrey A Rossmann; M Miles Beach; Garth R Griffiths; Benjamin Meyrat; James B Barnes; David G Kerns; Brad Crump; Barnett Bookatz; Paul Ezzo
Journal:  Proc (Bayl Univ Med Cent)       Date:  2011-07

5.  A comparison of SNAP II and bispectral index monitoring in patients undergoing sedation.

Authors:  S R Springman; A-C Andrei; K Willmann; D A Rusy; M E Warren; S Han; M Lee
Journal:  Anaesthesia       Date:  2010-06-25       Impact factor: 6.955

6.  A novel empirical wavelet SODP and spectral entropy based index for assessing the depth of anaesthesia.

Authors:  Thomas Schmierer; Tianning Li; Yan Li
Journal:  Health Inf Sci Syst       Date:  2022-06-06

7.  Recent advance in patient monitoring.

Authors:  Tomoki Nishiyama
Journal:  Korean J Anesthesiol       Date:  2010-09-20

Review 8.  Enhanced Recovery After Surgery (ERAS) for gastrointestinal surgery, part 2: consensus statement for anaesthesia practice.

Authors:  A Feldheiser; O Aziz; G Baldini; B P B W Cox; K C H Fearon; L S Feldman; T J Gan; R H Kennedy; O Ljungqvist; D N Lobo; T Miller; F F Radtke; T Ruiz Garces; T Schricker; M J Scott; J K Thacker; L M Ytrebø; F Carli
Journal:  Acta Anaesthesiol Scand       Date:  2015-10-30       Impact factor: 2.105

9.  EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks.

Authors:  Quan Liu; Yi-Feng Chen; Shou-Zen Fan; Maysam F Abbod; Jiann-Shing Shieh
Journal:  Comput Math Methods Med       Date:  2015-09-28       Impact factor: 2.238

10.  To clarify features of photoplethysmography in monitoring balanced anesthesia, compared with Cerebral State Index.

Authors:  Lieliang Zhang; Lei Xu; Juan Zhu; Yujie Gao; Zhonghua Luo; Hongyu Wang; Zhongliang Zhu; Yi Yu; Hongwei Shi; Hongguang Bao
Journal:  Med Sci Monit       Date:  2014-03-25
  10 in total

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