Literature DB >> 8630586

Evaluation of a knowledge-based system providing ventilatory management and decision for extubation.

M Dojat1, A Harf, D Touchard, M Laforest, F Lemaire, L Brochard.   

Abstract

We evaluated whether a knowledge-based system (KBS) connected to a ventilator in pressure support mode could correctly predict the ability of patients to tolerate total withdrawal from ventilatory support. The KBS was designed to continuously adapt ventilatory assistance to the needs of the patient, to manage a strategy of gradually decreasing ventilatory assistance, and to indicate when the patient was able to breathe without assistance. Thirty-eight patients for whom weaning was being considered were evaluated using a conventional battery of parameters, including weaning criteria, tolerance of a T-piece trial, and outcome 48h after permanent withdrawal of ventilation. The results of this evaluation were compared with the suggestions made by the KBS at the end of a period of KBS-driven mechanical ventilation inserted in the conventional weaning procedure. The positive predictive value of the KBS was 89%, versus 77% for the conventional procedure and 81% for the rapid shallow breathing index alone. The KBS correctly predicted the course of five patients who tolerated a T-piece trial but required ventilation within 48 h. We conclude that our KBS ensured appropriate patient management during the weaning period and improved our ability to predict responses to weaning.

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Year:  1996        PMID: 8630586     DOI: 10.1164/ajrccm.153.3.8630586

Source DB:  PubMed          Journal:  Am J Respir Crit Care Med        ISSN: 1073-449X            Impact factor:   21.405


  17 in total

Review 1.  Variable performance of weaning-predictor tests: role of Bayes' theorem and spectrum and test-referral bias.

Authors:  Martin J Tobin; Amal Jubran
Journal:  Intensive Care Med       Date:  2006-11-08       Impact factor: 17.440

2.  A knowledge- and model-based system for automated weaning from mechanical ventilation: technical description and first clinical application.

Authors:  Dirk Schädler; Stefan Mersmann; Inéz Frerichs; Gunnar Elke; Thomas Semmel-Griebeler; Oliver Noll; Sven Pulletz; Günther Zick; Matthias David; Wolfgang Heinrichs; Jens Scholz; Norbert Weiler
Journal:  J Clin Monit Comput       Date:  2013-07-28       Impact factor: 2.502

3.  Automatic adjustment of pressure support by a computer-driven knowledge-based system during noninvasive ventilation: a feasibility study.

Authors:  Anne Battisti; Jean Roeseler; Didier Tassaux; Philippe Jolliet
Journal:  Intensive Care Med       Date:  2006-06-28       Impact factor: 17.440

4.  Computer-driven management of prolonged mechanical ventilation and weaning: a pilot study.

Authors:  Lila Bouadma; François Lellouche; Belen Cabello; Solenne Taillé; Jordi Mancebo; Michel Dojat; Laurent Brochard
Journal:  Intensive Care Med       Date:  2005-08-23       Impact factor: 17.440

5.  Automated mechanical ventilation: adapting decision making to different disease states.

Authors:  S Lozano-Zahonero; D Gottlieb; C Haberthür; J Guttmann; K Möller
Journal:  Med Biol Eng Comput       Date:  2010-11-11       Impact factor: 2.602

6.  Clinical evaluation of a computer-controlled pressure support mode.

Authors:  M Dojat; A Harf; D Touchard; F Lemaire; L Brochard
Journal:  Am J Respir Crit Care Med       Date:  2000-04       Impact factor: 21.405

7.  Volume-guaranteed pressure-support ventilation facing acute changes in ventilatory demand.

Authors:  Samir Jaber; Jean-Marc Delay; Stefan Matecki; Mustapha Sebbane; Jean-Jacques Eledjam; Laurent Brochard
Journal:  Intensive Care Med       Date:  2005-07-20       Impact factor: 17.440

8.  A randomised, controlled trial of conventional versus automated weaning from mechanical ventilation using SmartCare/PS.

Authors:  Louise Rose; Jeffrey J Presneill; Linda Johnston; John F Cade
Journal:  Intensive Care Med       Date:  2008-06-25       Impact factor: 17.440

Review 9.  Automating the weaning process with advanced closed-loop systems.

Authors:  Karen E A Burns; Francois Lellouche; Martin R Lessard
Journal:  Intensive Care Med       Date:  2008-06-03       Impact factor: 17.440

10.  Wean Earlier and Automatically with New technology (the WEAN study): a protocol of a multicentre, pilot randomized controlled trial.

Authors:  Karen E A Burns; Maureen O Meade; Martin R Lessard; Sean P Keenan; Francois Lellouche
Journal:  Trials       Date:  2009-09-04       Impact factor: 2.279

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