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Intensive Care Med ; 29(5): 727-34, 2003 May.
Artículo en Inglés | MEDLINE | ID: mdl-12698242

RESUMEN

OBJECTIVE: To investigate whether parameters describing pulmonary gas exchange (shunt and ventilation-perfusion mismatch) can be estimated consistently by the use of non-invasive data as input to a mathematical model of oxygen transport. DESIGN: Prospective study. SETTING: Investigations were carried out in the post-anaesthesia care unit, coronary care unit, and intensive care unit. PATIENTS: Data from ninety-five patients and six normal subjects were included for the comparison. The clinical situations differed, ranging from healthy subjects to patients with acute respiratory failure in the intensive care unit. MEASUREMENTS: The experimental procedure involved changing the inspired oxygen fraction (F(I)O(2)) in 4-6 steps in order to obtain arterial oxygen saturations (S(a)O(2)) in the range from 90-100%. This procedure allows plotting a F(I)O(2)/S(a)O(2) or F(E)O(2)/S(a)O(2) curve, the shape and position of which was quantified using the mathematical model estimating pulmonary shunt and a measure of ventilation-perfusion mismatch (DeltaPO(2)). This procedure was performed using either arterial blood samples at each F(I)O(2) level (invasive approach) or using values from the pulse oximeter (non-invasive approach). MAIN RESULTS: The model provided good fit to data using both the invasive and non-invasive experimental approach. The parameter estimates were linearly correlated with highly significant correlation coefficients; shunt(invasive) vs shunt(non-invasive), r(2) = 0.74, P <0.01, and DeltaPO(2)(invasive) vs DeltaPO(2)(non-invasive), r(2) = 0.97, P <0.001. CONCLUSIONS: Pulmonary gas exchange can be described equally well using non-invasive data. The simplicity of the non-invasive approach makes the method suitable for large-scale clinical use.


Asunto(s)
Modelos Biológicos , Monitoreo Fisiológico/métodos , Intercambio Gaseoso Pulmonar , Relación Ventilacion-Perfusión/fisiología , Humanos , Oximetría , Estudios Prospectivos , Respiración Artificial , Sensibilidad y Especificidad
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