Ventilator advisory system employing load and tolerance strategy recommends appropriate pressure support ventilation settings: multisite validation study.
(Journal Article;Multicenter Study)
BACKGROUND: Loads on the respiratory muscles, reflected by noninvasive measurement of the real-time power of breathing (POBn), and tolerance of these loads, reflected by spontaneous breathing frequency (f) and tidal volume (Vt), should be considered when evaluating patients with respiratory failure. Pressure support ventilation (PSV) should be applied so that muscle loads are not too high or too low. We propose a computerized, ventilator advisory system employing a load (POBn) and tolerance (f and Vt) strategy in a fuzzy logic algorithm to provide guidance for setting PSV. To validate these recommendations, we performed a multisite study comparing the advisory system recommendations to experienced physician decisions. METHODS: Data were obtained from adults who were receiving PSV (n = 87) at three university sites via a combined pressure/flow sensor, which was positioned between the endotracheal tube and the Y-piece of the ventilator breathing circuit and was directed to the advisory system. Recommendations from the advisory system for increasing, maintaining, or decreasing PSV were compared at specific time points to decisions made by physician intensivists at the bedside. RESULTS: There were no significant differences in the recommendations by the advisory system (n = 210) compared to those of the physician intensivists to increase, maintain, or decrease PSV (p > 0.05). Physician intensivists agreed with 90.5% of all recommendations. The advisory system was very good at predicting intensivist decisions (r(2) = 0.90; p < 0.05) in setting PSV. CONCLUSIONS: The novel load-and-tolerance strategy of the advisory system provided automatic and valid recommendations for setting PSV to appropriately unload the respiratory muscles that were as good as the clinical judgment of physician intensivists.
Banner, MJ; Euliano, NR; Macintyre, NR; Layon, AJ; Bonett, S; Gentile, MA; Bshouty, Z; Peters, C; Gabrielli, A
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