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Prediction equation to estimate dead space to tidal volume fraction correlates with mortality in critically ill patients
Prediction equation to estimate dead space to tidal volume fraction correlates with mortality in critically ill patients
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Prediction equation to estimate dead space to tidal volume fraction correlates with mortality in critically ill patients
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Prediction equation to estimate dead space to tidal volume fraction correlates with mortality in critically ill patients
Prediction equation to estimate dead space to tidal volume fraction correlates with mortality in critically ill patients

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Prediction equation to estimate dead space to tidal volume fraction correlates with mortality in critically ill patients
Prediction equation to estimate dead space to tidal volume fraction correlates with mortality in critically ill patients
Journal Article

Prediction equation to estimate dead space to tidal volume fraction correlates with mortality in critically ill patients

2014
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Overview
The measurement of dead space to tidal volume fraction (Vd/Vt) using various methodologies has been shown to be a reliable predictor of mortality in critically ill patients. In this study, we evaluated the correlation of a validated equation using clinically available information to predict calculation of Vd/Vt with clinically relevant outcome parameters in patients requiring mechanical ventilation. Calculations of Vd/Vt were obtained based upon a previously published prediction equation for dead space ventilation fraction: Vd/Vt = 0.320 + 0.0106 (Paco2 − end-tidal carbon dioxide measurement) + 0.003 (respiratory rate per minute) + 0.0015 (age in years) on study days 1, 3 to 4, 6 to 9, and 14 after initiation of mechanical ventilation in adult patients who satisfied 1 of the 3 study defined diseases: (1) acute bacterial pneumonia, (2) acute respiratory distress syndrome, or (3) cystic fibrosis. Using the final/last available time point calculation of Vd/Vt, a significant difference was observed between survivors and nonsurvivors both in relation to mean and median values (56.5% vs 71.2% and 56.0% vs 65.0%, respectively). In addition, sequential analyses of Vd/Vt calculations over time also demonstrated a statistically significant difference between survivors and nonsurvivors for days 6 to 9. In this study-specific population of critically ill patients, the prediction equation of Vd/Vt using clinically available parameters correlates with mortality. In addition, we provide a simple method to estimate Vd/Vt that can be potentially applicable to all critically ill intensive care unit patients.

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