Generalized estimation of the ventilatory distribution from the multiple-breath washout: a bench evaluation study.
Biomed Eng Online
; 17(1): 3, 2018 Jan 15.
Article
en En
| MEDLINE
| ID: mdl-29335011
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 (VT) 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 (vd) were built and mechanically ventilated. The models underwent MBW with N2 as inert gas, throughout which flow and N2 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 N2 step) and with the new, generalized model including breath-by-breath vd estimates given by the Fowler method (with and without constrained VT and EELV). RESULTS: The v/V distributions estimated with constrained EELV and VT 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 VT and EELV.
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Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Respiración
/
Respiración Artificial
/
Modelos Biológicos
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Biomed Eng Online
Asunto de la revista:
ENGENHARIA BIOMEDICA
Año:
2018
Tipo del documento:
Article
País de afiliación:
Brasil
Pais de publicación:
Reino Unido