Literature DB >> 8195655

Automatic selection of tidal volume, respiratory frequency and minute ventilation in intubated ICU patients as start up procedure for closed-loop controlled ventilation.

T P Laubscher1, A Frutiger, S Fanconi, H Jutzi, J X Brunner.   

Abstract

OBJECTIVE: Before a patient can be connected to a mechanical ventilator, the controls of the apparatus need to be set up appropriately. Today, this is done by the intensive care professional. With the advent of closed loop controlled mechanical ventilation, methods will be needed to select appropriate start up settings automatically. The objective of our study was to test such a computerized method which could eventually be used as a start-up procedure (first 5-10 minutes of ventilation) for closed-loop controlled ventilation.
DESIGN: Prospective Study. SETTINGS: ICU's in two adult and one children's hospital. PATIENTS: 25 critically ill adult patients (age > or = 15 y) and 17 critically ill children selected at random were studied.
INTERVENTIONS: To stimulate 'initial connection', the patients were disconnected from their ventilator and transiently connected to a modified Hamilton AMADEUS ventilator for maximally one minute. During that time they were ventilated with a fixed and standardized breath pattern (Test Breaths) based on pressure controlled synchronized intermittent mandatory ventilation (PCSIMV).
MEASUREMENTS AND MAIN RESULTS: Measurements of airway flow, airway pressure and instantaneous CO2 concentration using a mainstream CO2 analyzer were made at the mouth during application of the Test-Breaths. Test-Breaths were analyzed in terms of tidal volume, expiratory time constant and series dead space. Using this data an initial ventilation pattern consisting of respiratory frequency and tidal volume was calculated. This ventilation pattern was compared to the one measured prior to the onset of the study using a two-tailed paired t-test. Additionally, it was compared to a conventional method for setting up ventilators. The computer-proposed ventilation pattern did not differ significantly from the actual pattern (p > 0.05), while the conventional method did. However the scatter was large and in 6 cases deviations in the minute ventilation of more than 50% were observed.
CONCLUSIONS: The analysis of standardized Test Breaths allows automatic determination of an initial ventilation pattern for intubated ICU patients. While this pattern does not seem to be superior to the one chosen by the conventional method, it is derived fully automatically and without need for manual patient data entry such as weight or height. This makes the method potentially useful as a start up procedure for closed-loop controlled ventilation.

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Year:  1994        PMID: 8195655     DOI: 10.1007/bf01132840

Source DB:  PubMed          Journal:  Int J Clin Monit Comput        ISSN: 0167-9945


  9 in total

1.  An integrated graphic data display improves detection and identification of critical events during anesthesia.

Authors:  P Michels; D Gravenstein; D R Westenskow
Journal:  J Clin Monit       Date:  1997-07

2.  The automatic selection of ventilation parameters during the initial phase of mechanical ventilation.

Authors:  T P Laubscher; A Frutiger; S Fanconi; J X Brunner
Journal:  Intensive Care Med       Date:  1996-03       Impact factor: 17.440

Review 3.  Automating the weaning process with advanced closed-loop systems.

Authors:  Karen E A Burns; Francois Lellouche; Martin R Lessard
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4.  A pilot prospective study on closed loop controlled ventilation and oxygenation in ventilated children during the weaning phase.

Authors:  Philippe Jouvet; Allen Eddington; Valérie Payen; Alice Bordessoule; Guillaume Emeriaud; Ricardo Lopez Gasco; Marc Wysocki
Journal:  Crit Care       Date:  2012-05-16       Impact factor: 9.097

5.  Adaptive support ventilation: State of the art review.

Authors:  Jaime Fernández; Dayra Miguelena; Hernando Mulett; Javier Godoy; Federico Martinón-Torres
Journal:  Indian J Crit Care Med       Date:  2013-01

6.  Advanced modes of mechanical ventilation and optimal targeting schemes.

Authors:  Matthias van der Staay; Robert L Chatburn
Journal:  Intensive Care Med Exp       Date:  2018-08-22

7.  Transparent decision support for mechanical ventilation using visualization of clinical preferences.

Authors:  Stephen Edward Rees; Savino Spadaro; Francesca Dalla Corte; Nilanjan Dey; Jakob Bredal Brohus; Gaetano Scaramuzzo; David Lodahl; Robert Ravnholt Winding; Carlo Alberto Volta; Dan Stieper Karbing
Journal:  Biomed Eng Online       Date:  2022-01-24       Impact factor: 2.819

8.  Stochastic integrated model-based protocol for volume-controlled ventilation setting.

Authors:  Jay Wing Wai Lee; Yeong Shiong Chiew; Xin Wang; Mohd Basri Mat Nor; J Geoffrey Chase; Thomas Desaive
Journal:  Biomed Eng Online       Date:  2022-02-11       Impact factor: 2.819

9.  Human versus Computer Controlled Selection of Ventilator Settings: An Evaluation of Adaptive Support Ventilation and Mid-Frequency Ventilation.

Authors:  Eduardo Mireles-Cabodevila; Enrique Diaz-Guzman; Alejandro C Arroliga; Robert L Chatburn
Journal:  Crit Care Res Pract       Date:  2012-10-15
  9 in total

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