Anesth Analg 2003;97:1840-1845
© 2003 International Anesthesia Research Society
GENERAL ARTICLES
Validation of an Original Mathematical Model of CO2 Elimination and Dead Space Ventilation
Jonathan G. Hardman, FRCA, and
Alan R. Aitkenhead, FRCA
From the University Department of Anesthesia, University Hospital, Nottingham, UK
Address correspondence and reprint requests to Dr. Jonathan G. Hardman, Clinical Senior Lecturer, University Department of Anesthesia, University Hospital, Nottingham, NG7 2UH, UK. Address email to j.hardman{at}nottingham.ac.uk
We present an original, mathematical model of ventilation and gas-exchange. Our aim was to validate it using data from previous clinical investigations, allowing our use of it in future investigations. The first previous investigation used a low-dead space, double-lumen, tracheal tube (DLT). We matched the models PaCO2 and airway pressures (PAW) to the patient mean during use of the DLT and a single-lumen tube (SLT). The models resulting PaCO2, PÉCO2 and PAW were compared with the patients as tidal volume (VT) changed with constant minute volume. The second investigation examined dead space during anesthesia. The models VT, respiratory rate, CO2 production, temperature, and alveolar and anatomical dead spaces were matched to each mechanically ventilated subject. Bias and precision in predictions of PaCO2 and PÉCO2 were calculated. The models bias in prediction of dead space reduction by the DLT was 6.9%. Bias in prediction of PAW was 0.1% (peak) and -5.13% (mean), of PaCO2 was 1.2% (DLT) and 1.5% (SLT) and of PÉCO2 was 1.7% (DLT) and 1.3% (SLT). Prediction of PaCO2 and PÉCO2 in the second investigation (as 95% confidence interval of bias): PaCO2 -2.6% to 0.8% and PÉCO2 -4.9% to 1.2%. This validation allows future application of our model in appropriate theoretical investigations.
IMPLICATIONS: We present an original, mathematical model of ventilation and gas exchange. We validate it against previously published clinical data to allow its use in future theoretical investigations where data may be unavailable from patients.
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