Generalized estimation of the ventilatory distribution from the multiple-breath washout: a bench evaluation study

BACKGROUND: The multiple-breath washout (MBW) is able to provide information about the distribution of ventilation-to-volume (v/V) ratios in the lungs. However, the classical, all-parallel model may return skewed results due to the mixing effect of a common dead space. The aim of this work is to examine whether a novel mathematical model and algorithm is able to estimate v/V of a physical model, and to compare its results with those of the classical model. The novel model takes into account a dead space in series with the parallel ventilated compartments, allows for variable tidal volume (V(T)) and end-expiratory lung volume (EELV), and does not require a ideal step change of the inert gas concentration. METHODS: Two physical models with preset v/V units and a common series dead space (v(d)) were built and mechanically ventilated. The models underwent MBW with N(2) as inert gas, throughout which flow and N(2) concentration signals were acquired. Distribution of v/V was estimated—via nonnegative least squares, with Tikhonov regularization—with the classical, all-parallel model (with and without correction for non-ideal inspiratory N(2) step) and with the new, generalized model including breath-by-breath v(d) estimates given by the Fowler method (with and without constrained V(T) and EELV). RESULTS: The v/V distributions estimated with constrained EELV and V(T) by the generalized model were practically coincident with the actual v/V distribution for both physical models. The v/V distributions calculated with the classical model were shifted leftwards and broader as compared to the reference. CONCLUSIONS: The proposed model and algorithm provided better estimates of v/V than the classical model, particularly with constrained V(T) and EELV. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12938-018-0442-3) contains supplementary material, which is available to authorized users.