Algorithms to estimate PaCO2 and pH using noninvasive parameters for children with hypoxemic respiratory failure.

BACKGROUND

Ventilator management for children with hypoxemic respiratory failure may benefit from ventilator protocols, which rely on blood gases. Accurate noninvasive estimates for pH or P(aCO2) could allow frequent ventilator changes to optimize lung-protective ventilation strategies. If these models are highly accurate, they can facilitate the development of closed-loop ventilator systems. We sought to develop and test algorithms for estimating pH and P(aCO2) from measures of ventilator support, pulse oximetry, and end-tidal carbon dioxide pressure (P(ETCO2)). We also sought to determine whether surrogates for changes in dead space can improve prediction.

METHODS

Algorithms were developed and tested using 2 data sets from previously published investigations. A baseline model estimated pH and P(aCO2) from P(ETCO2) using the previously observed relationship between P(ETCO2) and P(aCO2) or pH (using the Henderson-Hasselbalch equation). We developed a multivariate gaussian process (MGP) model incorporating other available noninvasive measurements.

RESULTS

The training data set had 2,386 observations from 274 children, and the testing data set had 658 observations from 83 children. The baseline model predicted P(aCO2) within ± 7 mm Hg of the observed P(aCO2) 80% of the time. The MGP model improved this to ± 6 mm Hg. When the MGP model predicted P(aCO2) between 35 and 60 mm Hg, the 80% prediction interval narrowed to ± 5 mm Hg. The baseline model predicted pH within ± 0.07 of the observed pH 80% of the time. The MGP model improved this to ± 0.05.

CONCLUSIONS

We have demonstrated a conceptual first step for predictive models that estimate pH and P(aCO2) to facilitate clinical decision making for children with lung injury. These models may have some applicability when incorporated in ventilator protocols to encourage practitioners to maintain permissive hypercapnia when using high ventilator support. Refinement with additional data may improve model accuracy.