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Anesth Analg 2003;97:1143-1148
© 2003 International Anesthesia Research Society

CRITICAL CARE AND TRAUMA

Estimating Respiratory System Compliance During Mechanical Ventilation Using Artificial Neural Networks

Gaetano Perchiazzi, MD*, Rocco Giuliani, MD{dagger}, Loreta Ruggiero, MD{dagger}, Tommaso Fiore, MD{dagger}, and Göran Hedenstierna, MD PhD*

*Department of Clinical Physiology, Uppsala University Hospital, Sweden; and {dagger}Department of Emergency and Transplantation, Bari University Hospital, Italy

Address correspondence and reprint requests to Gaetano Perchiazzi, MD, Centro di Rianimazione - Ospedale Policlinico, Piazza Giulio Cesare, 11, 70124 Bari, Italy. Address e-mail to gperchiazzi{at}rianima.uniba.it

In this study we evaluated whether a technology based on artificial neural networks (ANN) could estimate the static compliance (CRS) of the respiratory system, even in the absence of an end-inspiratory pause, during continuous mechanical ventilation. A porcine model of acute lung injury was used to provide recordings of different respiratory mechanics conditions. Each recording consisted of 10 or more consecutive breaths in volume-controlled mechanical ventilation, followed by a breath having an end-inspiratory pause used to calculate CRS according to the interrupter technique (IT). The volume-pressure loop of the breath immediately preceding the one with pause was given to the ANN for the training, together with the CRS separately calculated by the IT. The prospective phase consisted of giving only the loops to the trained ANN and comparing the results yielded by it to the compliance separately calculated by the investigators. Determination of measurement agreement between ANN and IT methods showed an error of -0.67 ± 1.52 mL/cm H2O (bias ± SD). We could conclude that ANN, during volume-controlled mechanical ventilation, can extract CRS without needing to stop inspiratory flow.

IMPLICATIONS: We studied the application of artificial neural networks (ANN) to the estimation of respiratory compliance during mechanical ventilation. The study was performed on an animal model of acute lung injury, testing the performance of ANN in both healthy and diseased conditions of the lung.






Lippincott, Williams & Wilkins Anesthesia & Analgesia® is published for the International Anesthesia Research Society® by Lippincott Williams & Wilkins with the assistance of Stanford University Libraries' HighWire Press®. Copyright 2006 by the International Anesthesia Research Society. Online ISSN: 1526-7598   Print ISSN: 0003-2999 HighWire Press
Copyright © 2003 by the International Anesthesia Research Society.