Literature DB >> 11732512

Model-based automatic feedback control versus human control of end-tidal isoflurane concentration using low-flow anaesthesia.

T J Sieber1, C W Frei, M Derighetti, P Feigenwinter, D Leibundgut, A M Zbinden.   

Abstract

We studied the clinical use of an automatic feedback control system to adjust the end-tidal anaesthetic concentration with a low-flow method. The end-tidal controller uses two input signals (the end-tidal and inspiratory concentrations) to control the isoflurane concentration in the fresh gas flow, using a model-based algorithm. We studied 22 ASA I-III patients during elective surgery lasting more than 2 h. The anaesthetist was asked to make four step changes of the target end-tidal concentration (+0.3, +0.6, -0.3, -0.6 vol%), either manually (Group A) or by setting the target value for the feedback controller (Group B), and then the control was changed and the step changes were repeated, in a crossover design. Eighty step changes with each control method were compared in terms of response time, maximal overshoot and stability. The automatic control system was more accurate and stable than the human controller for step increases and step decreases, with less overshoot/undershoot and greater stability [e.g. maximal overshoot 14.7 (SD 3.7)% and 18 (8.1)% respectively for +0.6 vol% step changes, and 19.8 (3.7)% and 30.7 (13.2)% respectively for +0.3 vol% step changes]. However, the automatic control system showed a faster response time than the manual method only with large increasing steps (e.g. 149 (32) s and 205 (57) s respectively for +0.6 vol% step changes) and was not different from manual control for decreasing steps. Automatic control of the end-tidal isoflurane concentration can be better than human control in a clinical setting, and this task could be done automatically.

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Year:  2000        PMID: 11732512     DOI: 10.1093/bja/85.6.818

Source DB:  PubMed          Journal:  Br J Anaesth        ISSN: 0007-0912            Impact factor:   9.166


  6 in total

1.  End-tidal versus manually-controlled low-flow anaesthesia.

Authors:  Umberto Lucangelo; Giuliana Garufi; Emanuele Marras; Massimo Ferluga; Federica Turchet; Francesca Bernabè; Lucia Comuzzi; Giorgio Berlot; Walter A Zin
Journal:  J Clin Monit Comput       Date:  2013-10-11       Impact factor: 2.502

2.  New technology in anaesthesia: friend or foe?

Authors:  R Ross Kennedy
Journal:  J Clin Monit Comput       Date:  2014-04       Impact factor: 2.502

3.  Anaesthesia monitoring using fuzzy logic.

Authors:  Mirza Mansoor Baig; Hamid Gholamhosseini; Abbas Kouzani; Michael J Harrison
Journal:  J Clin Monit Comput       Date:  2011-10-28       Impact factor: 2.502

4.  Optimizing target control of the vessel rich group with volatile anesthetics.

Authors:  Christopher W Connor
Journal:  J Clin Monit Comput       Date:  2018-06-21       Impact factor: 2.502

Review 5.  Fuzzy logic and decision-making in anaesthetics.

Authors:  Paul Grant; Ole Naesh
Journal:  J R Soc Med       Date:  2005-01       Impact factor: 18.000

6.  Cost efficiency of target-controlled inhalational anesthesia.

Authors:  Meenoti Pramod Potdar; Laxmi L Kamat; Manjeet P Save
Journal:  J Anaesthesiol Clin Pharmacol       Date:  2014-04
  6 in total

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