Pao2 is the gold standard to assess acute hypoxic respiratory failure, but it is only routinely available by intermittent spot checks, precluding any automatic continuous analysis for bedside tools.
Objective: To validate a continuous and noninvasive method to estimate hypoxemia severity for all Spo2 values.
Derivation cohort: All patients who had an arterial blood gas and simultaneous continuous noninvasive monitoring from 2011 to 2019 at Boston Children's Hospital (Boston, MA) PICU.
Validation cohort: External cohort at Sainte-Justine Hospital PICU (Montreal, QC, Canada) from 2017 to 2020.
Prediction model: We estimated the Pao2 using three kinds of neural networks and an empirically optimized mathematical model derived from known physiologic equations.
Results: We included 52,879 Pao2 (3,252 patients) in the derivation dataset and 12,047 Pao2 (926 patients) in the validation dataset. The mean function on the last minute before the arterial blood gas had the lowest bias (bias -0.1% validation cohort). A difference greater than or equal to 3% between pulse rate and electrical heart rate decreased the intraclass correlation coefficients (0.75 vs 0.44; p < 0.001) implying measurement noise. Our estimated Pao2 equation had the highest intraclass correlation coefficient (0.38; 95% CI, 0.36-0.39; validation cohort) and outperformed neural networks and existing equations. Using the estimated Pao2 to estimate the oxygenation index showed a significantly better hypoxemia classification (kappa) than oxygenation saturation index for both Spo2 less than or equal to 97% (0.79 vs 0.60; p < 0.001) and Spo2 greater than 97% (0.58 vs 0.52; p < 0.001).
Conclusion: The estimated Pao2 using pulse rate and electrical heart rate Spo2 validation allows a continuous and noninvasive estimation of the oxygenation index that is valid for Spo2 less than or equal to 97% and for Spo2 greater than 97%. Display of continuous analysis of estimated Pao2 and estimated oxygenation index may provide decision support to assist with hypoxemia diagnosis and oxygen titration in critically ill patients.
Keywords: automatic data processing; clinical decision support systems; critical care; machine learning; oximetry.
Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.