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Mathematical modelling to centre low tidal volumes following acute lung injury: A study with biologically variable ventilation

BACKGROUND: With biologically variable ventilation [BVV – using a computer-controller to add breath-to-breath variability to respiratory frequency (f) and tidal volume (V(T))] gas exchange and respiratory mechanics were compared using the ARDSNet low V(T )algorithm (Control) versus an approach using...

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Detalles Bibliográficos
Autores principales: Graham, M Ruth, Haberman, Craig J, Brewster, John F, Girling, Linda G, McManus, Bruce M, Mutch, W Alan C
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1200564/
https://www.ncbi.nlm.nih.gov/pubmed/15985159
http://dx.doi.org/10.1186/1465-9921-6-64
Descripción
Sumario:BACKGROUND: With biologically variable ventilation [BVV – using a computer-controller to add breath-to-breath variability to respiratory frequency (f) and tidal volume (V(T))] gas exchange and respiratory mechanics were compared using the ARDSNet low V(T )algorithm (Control) versus an approach using mathematical modelling to individually optimise V(T )at the point of maximal compliance change on the convex portion of the inspiratory pressure-volume (P-V) curve (Experimental). METHODS: Pigs (n = 22) received pentothal/midazolam anaesthesia, oleic acid lung injury, then inspiratory P-V curve fitting to the four-parameter logistic Venegas equation F(P) = a + b[1 + e(-(P-c)/d)](-1 )where: a = volume at lower asymptote, b = the vital capacity or the total change in volume between the lower and upper asymptotes, c = pressure at the inflection point and d = index related to linear compliance. Both groups received BVV with gas exchange and respiratory mechanics measured hourly for 5 hrs. Postmortem bronchoalveolar fluid was analysed for interleukin-8 (IL-8). RESULTS: All P-V curves fit the Venegas equation (R(2 )> 0.995). Control V(T )averaged 7.4 ± 0.4 mL/kg as compared to Experimental 9.5 ± 1.6 mL/kg (range 6.6 – 10.8 mL/kg; p < 0.05). Variable V(T)s were within the convex portion of the P-V curve. In such circumstances, Jensen's inequality states "if F(P) is a convex function defined on an interval (r, s), and if P is a random variable taking values in (r, s), then the average or expected value (E) of F(P); E(F(P)) > F(E(P))." In both groups the inequality applied, since F(P) defines volume in the Venegas equation and (P) pressure and the range of V(T)s varied within the convex interval for individual P-V curves. Over 5 hrs, there were no significant differences between groups in minute ventilation, airway pressure, blood gases, haemodynamics, respiratory compliance or IL-8 concentrations. CONCLUSION: No difference between groups is a consequence of BVV occurring on the convex interval for individualised Venegas P-V curves in all experiments irrespective of group. Jensen's inequality provides theoretical proof of why a variable ventilatory approach is advantageous under these circumstances. When using BVV, with V(T )centred by Venegas P-V curve analysis at the point of maximal compliance change, some leeway in low V(T )settings beyond ARDSNet protocols may be possible in acute lung injury. This study also shows that in this model, the standard ARDSNet algorithm assures ventilation occurs on the convex portion of the P-V curve.