Predicting the lung compliance of mechanically ventilated patients via statistical modeling

Physiological Measurement
Steven GanzertJ Guttmann

Abstract

To avoid ventilator associated lung injury (VALI) during mechanical ventilation, the ventilator is adjusted with reference to the volume distensibility or 'compliance' of the lung. For lung-protective ventilation, the lung should be inflated at its maximum compliance, i.e. when during inspiration a maximal intrapulmonary volume change is achieved by a minimal change of pressure. To accomplish this, one of the main parameters is the adjusted positive end-expiratory pressure (PEEP). As changing the ventilator settings usually produces an effect on patient's lung mechanics with a considerable time delay, the prediction of the compliance change associated with a planned change of PEEP could assist the physician at the bedside. This study introduces a machine learning approach to predict the nonlinear lung compliance for the individual patient by Gaussian processes, a probabilistic modeling technique. Experiments are based on time series data obtained from patients suffering from acute respiratory distress syndrome (ARDS). With a high hit ratio of up to 93%, the learned models could predict whether an increase/decrease of PEEP would lead to an increase/decrease of the compliance. However, the prediction of the complete pressure-volu...Continue Reading

References

Nov 7, 1999·Intensive Care Medicine·G MolsJ Guttmann
May 4, 2000·The New England Journal of Medicine·UNKNOWN Acute Respiratory Distress Syndrome NetworkArthur Wheeler
Sep 18, 2002·Artificial Intelligence in Medicine·Steven GanzertStefan Kramer
Jun 10, 2003·Critical Care : the Official Journal of the Critical Care Forum·James A Frank, Michael A Matthay
Aug 10, 2005·Seminars in Respiratory and Critical Care Medicine·P PelosiL Gattinoni
Dec 20, 2005·British Journal of Anaesthesia·G MolsJ Guttmann

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