Literature DB >> 23892513

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

Dirk Schädler1, Stefan Mersmann, Inéz Frerichs, Gunnar Elke, Thomas Semmel-Griebeler, Oliver Noll, Sven Pulletz, Günther Zick, Matthias David, Wolfgang Heinrichs, Jens Scholz, Norbert Weiler.   

Abstract

To describe the principles and the first clinical application of a novel prototype automated weaning system called Evita Weaning System (EWS). EWS allows an automated control of all ventilator settings in pressure controlled and pressure support mode with the aim of decreasing the respiratory load of mechanical ventilation. Respiratory load takes inspired fraction of oxygen, positive end-expiratory pressure, pressure amplitude and spontaneous breathing activity into account. Spontaneous breathing activity is assessed by the number of controlled breaths needed to maintain a predefined respiratory rate. EWS was implemented as a knowledge- and model-based system that autonomously and remotely controlled a mechanical ventilator (Evita 4, Dräger Medical, Lübeck, Germany). In a selected case study (n = 19 patients), ventilator settings chosen by the responsible physician were compared with the settings 10 min after the start of EWS and at the end of the study session. Neither unsafe ventilator settings nor failure of the system occurred. All patients were successfully transferred from controlled ventilation to assisted spontaneous breathing in a mean time of 37 ± 17 min (± SD). Early settings applied by the EWS did not significantly differ from the initial settings, except for the fraction of oxygen in inspired gas. During the later course, EWS significantly modified most of the ventilator settings and reduced the imposed respiratory load. A novel prototype automated weaning system was successfully developed. The first clinical application of EWS revealed that its operation was stable, safe ventilator settings were defined and the respiratory load of mechanical ventilation was decreased.

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Year:  2013        PMID: 23892513     DOI: 10.1007/s10877-013-9489-7

Source DB:  PubMed          Journal:  J Clin Monit Comput        ISSN: 1387-1307            Impact factor:   2.502


  69 in total

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Review 3.  Low vs high positive end-expiratory pressure in the ventilatory management of acute lung injury.

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Journal:  N Engl J Med       Date:  2006-04-27       Impact factor: 91.245

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Journal:  Intensive Care Med       Date:  2012-03-30       Impact factor: 17.440

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Journal:  Am J Respir Crit Care Med       Date:  1998-01       Impact factor: 30.528

8.  Weaning from mechanical ventilation.

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Journal:  Eur Respir J       Date:  2007-05       Impact factor: 16.671

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Authors:  Fleur T Tehrani; James H Roum
Journal:  J Clin Monit Comput       Date:  2008-03-07       Impact factor: 2.502

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  3 in total

Review 1.  Journal of clinical monitoring and computing 2014 end of year summary: respiration.

Authors:  D S Karbing; S E Rees; M B Jaffe
Journal:  J Clin Monit Comput       Date:  2015-03-04       Impact factor: 2.502

2.  The European Society for Computing and Technology in Anaesthesia and Intensive Care (ESCTAIC): a special issue of full papers (Erlangen meeting 2011) and conference abstracts (Timisoara, meeting 2014).

Authors:  Stephen Edward Rees
Journal:  J Clin Monit Comput       Date:  2014-10       Impact factor: 2.502

3.  Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform.

Authors:  Yan Shi; Guoliang Wang; Jinglong Niu; Qimin Zhang; Maolin Cai; Baoqing Sun; Dandan Wang; Mei Xue; Xiaohua Douglas Zhang
Journal:  Int J Biol Sci       Date:  2018-05-22       Impact factor: 6.580

  3 in total

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