Development of an Arterial Carbon Dioxide Estimation Model Using End-tidal Carbon Dioxide Levels during Surgery in the Pediatric Population.

Background

Intraoperative arterial carbon dioxide partial pressure monitoring is essential for pediatric ventilatory management but requires invasive arterial sampling. While end-tidal carbon dioxide offers a noninvasive alternative, its reliability is limited by individual physiologic gradients. This study developed a machine learning model to estimate arterial carbon dioxide partial pressure using end-tidal carbon dioxide and intraoperative clinical features in children.

Methods

The authors retrospectively analyzed 8,853 paired end-tidal carbon dioxide and arterial carbon dioxide partial pressure measurements from 3,586 pediatric patients in the vital sign registry (VitalDB) database. Clinical and ventilatory features were used to train four machine learning algorithms, with missing data managed via age group-based imputation. Model performance was verified through internal temporal validation using a recent dataset of 2,138 pairs and external validation using an independent dataset of 92 pairs from a distinct hospital. Feature importance was assessed using Shapley additive explanations values.

Results

The gradient boosting model performed best, yielding a mean absolute error of 2.73 mmHg and root mean squared error of 4.13 mmHg. External validation confirmed generalizability with a mean absolute error of 3.65 mmHg (institutional) and 3.67 mmHg (temporal). End-tidal carbon dioxide, body temperature, fraction of inspired oxygen, and preoperative hemoglobin emerged as the most impactful features.

Conclusions

The authors' machine learning model accurately estimates intraoperative arterial carbon dioxide partial pressure using noninvasive parameters. The model demonstrates stable performance across temporal and external cohorts.