Enhancing the estimation of PaCO₂ from etCO₂ during ventilation through non-invasive parameters in the ovine model

In mechanically ventilated neonates, the arterial partial pressure of $$ ( $$ ) is an important indicator for the adequacy of ventilation settings. Determining the $$ is commonly done using invasive blood gas analyses, which constitute risks for neonates and are typically only available infrequently. An accurate, reliable, and continuous estimation of $$ is of high interest for medical staff, giving the possibility of a closer monitoring and faster reactions to changes. We aim to present a non-invasive estimation method for $$ in neonates on the basis of end-tidal $$ ( $$ ) with inclusion of different physiological and ventilation parameters. The estimation method should be more accurate than an estimation by unaltered $$ measurements with regard to the mean absolute error and the standard deviation.

Secondary data from 51 preterm lambs are used, due to its high comparability to preterm human data. We utilize robust linear regression on 863 $$ measurements below or equal to 75 mmHg from the first day of life. $$ along with a set of ventilation settings and measurements as well as vital parameters are included in the regression. Included independent variables are chosen iteratively by highest Pearson correlation to the remaining estimation deviation.

The evaluation is carried out on 12 additional neonatal lambs with 246 $$ measurements below or equal to 75 mmHg from the first two days of life. The estimation method shows a mean absolute error of 3.80 mmHg with a 4.92 mmHg standard deviation of differences and a standard error of 0.31 mmHg in comparison to measured $$ by blood gas analysis.

The estimation of $$ by the proposed equation is less biased than unaltered $$ . The usage of this method in clinical practice or in applications like the automation of ventilation needs further investigation.