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1. Individualized estimation of arterial carbon dioxide partial pressure using machine learning in children receiving mechanical ventilation

Author

Hye-Ji Han, Bongjin Lee, and June Dong Park

Subjects
Machine learning, Blood gas analysis, Capnography, Mechanical ventilation, Respiratory Dead Space, Pediatrics, RJ1-570
Abstract

Abstract Background Measuring arterial partial pressure of carbon dioxide (PaCO2) is crucial for proper mechanical ventilation, but the current sampling method is invasive. End-tidal carbon dioxide (EtCO2) has been used as a surrogate, which can be measured non-invasively, but its limited accuracy is due to ventilation-perfusion mismatch. This study aimed to develop a non-invasive PaCO2 estimation model using machine learning. Methods This retrospective observational study included pediatric patients (

Published
2024
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