Pressure Support Ventilation Advisory System Provides Valid Recommendations for Setting Ventilator

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BACKGROUND: Pressure support ventilation (PSV) should be applied so that the inspiratory muscles are unloaded appropriately. We developed a computerized advisory system that assesses the load on the inspiratory muscles to spontaneously inhale, reflected by the automatically and noninvasively measured work of breathing per minute, and tolerance for that load, reflected by spontaneous breathing frequency and tidal volume, in a fuzzy-logic algorithm that provides recommendations for setting PSV. We call this a load and tolerance strategy for determining PSV. METHODS: In a clinical validation study, we compared the recommendations from our PSV advisory system to the recommendations of experienced critical-care Registered Respiratory Therapists (RRTs) for setting PSV in patients with respiratory failure. With 76 adult patients in a university medical center surgical intensive care unit receiving PSV, a combined pressure/flow sensor, positioned between the endotracheal tube and patient Y-piece, sent measurements to the PSV advisory system. We compared the advisory system's recommendations (increase, maintain, or decrease the pressure support) to the RRTs' recommendations at the bedside. RESULTS: There were no significant differences between the RRTs' and the advisory system's recommendations (n = 109) to increase, maintain, or decrease PSV. The RRTs agreed with 91% of the advisory system's recommendations (kappa statistic 0.85, P < .001). The advisory system was very good at predicting the RRTs' pressure support recommendations (r2 = 0.87, P < .02). CONCLUSIONS: A load and tolerance strategy with a computerized PSV advisory system provided valid recommendations for setting PSV to unload the inspiratory muscles, and the recommendations were essentially the same as the recommendations from experienced critical-care RRTs. The PSV advisory system operates continuously and automatically and may be useful in clinical environments where experts are not always available.

Keywords: acute respiratory failure; artificial neural networks; mechanical ventilation; pressure support ventilation; respiratory monitoring; work of breathing

Document Type: Research Article


Affiliations: Department of Anesthesiology, University of Florida College of Medicine, Gainesville, Florida, USA

Publication date: March 1, 2011

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