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Combining Volumetric Capnography And Barometric Plethysmography To Measure The Lung Structure-function Relationship
Published on: January 8, 2019
Bedside quantification of dead-space fraction using routine clinical data in patients with acute lung injury:
Hassan Siddiki1, Marija Kojicic, Guangxi Li
1Department of Radiology, Mayo Clinic College of Medicine, 200 1stStreet, Rochester 55905, USA. hassansiddiki@hotmail.com.
Introduction:
Dead-space fraction (Vd/Vt) has been shown to be a powerful predictor of mortality in acute lung injury (ALI) patients. The measurement of Vd/Vt is based on the analysis of expired CO2 which is not a part of standard practice thus limiting widespread clinical application of this method. The objective of this study was to determine prognostic value of Vd/Vt estimated from routinely collected pulmonary variables.
Methods:
Secondary analysis of the original data from two prospective studies of ALI patients. Estimated Vd/Vt was calculated using the rearranged alveolar gas equation: Vd/Vt=1-[(0.86×VCO2est)/(VE×PaCO2)] where VCO2est is the estimated CO2 production calculated from the Harris Benedict equation, minute ventilation (VE) is obtained from the ventilator rate and expired tidal volume and PaCO2 from arterial gas analysis. Logistic regression models were created to determine the prognostic value of estimated Vd/Vt.
Results:
One hundred and nine patients in Mayo Clinic validation cohort and 1896 patients in ARDS-net cohort demonstrated an increase in percent mortality for every 10% increase in Vd/Vt in a dose response fashion. After adjustment for non-pulmonary and pulmonary prognostic variables, both day 1 (adjusted odds ratio-OR = 1.07, 95%CI 1.03 to 1.13) and day 3 (OR = 1.12, 95% CI 1.06 to 1.18) estimated dead-space fraction predicted hospital mortality.
Conclusions:
Elevated estimated Vd/Vt predicts mortality in ALI patients in a dose response manner. A modified alveolar gas equation may be of clinical value for a rapid bedside estimation of Vd/Vt, utilizing routinely collected clinical data.
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