Capnometry for Physiological Closed-Loop Ventilation in Neonates: Estimating PaCO2 from etCO2

Research Square

Abstract

Abstract Background: In mechanically ventilated neonates, the arterial partial pressure of CO2 (PaCO2) is an important indicator for the adequacy of ventilation settings. The automated control of PaCO2 allows for an improved patient care, while relieving the medical staff. An accurate, reliable, and continuous estimation of PaCO2 is required for this automation. We aim to present a non-invasive estimation method for PaCO2 in neonates on the basis of end-tidal CO2 (etCO2). The estimation method should be accurate enough for use in an automated PaCO2 control. Methods: Data from 51 preterm lambs is used, due to its high comparability to preterm human data. We utilize robust linear regression on 863 PaCO2 measurements below or equal to 75 mmHg from the first day of life. etCO2 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. Results: The evaluation is carried out on 12 additional neonatal lambs with 246 PaCO2 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. Conclusions: The proposed estimation method lowered the mean absolute error in the evaluation population by 1.35 mmHg in comparison to unaltered etCO2. The usage for ventilation automation in neonates needs further investigation.

Authors 7

1. Mike Grüne corresponding Aachen Department of Paediatric and Adolescent Medicine Informatik 11 – Embedded Software

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Universitätsklinikum Aachen

   Department of Paediatric and Adolescent Medicine, RWTH Aachen University Hospital, Aachen, Germany

   Informatik 11 -Embedded Software, RWTH Aachen University, Aachen, Germany

2. Lena Olivier Aachen

   Maastricht University Medical Centre · Universitätsklinikum Aachen

   Affiliation as printed

   Universitätsklinikum Aachen

   Maastricht University Medical Centre

3. Valerie Pfannschmidt Aachen Informatik 11 – Embedded Software

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

   Informatik 11 -Embedded Software, RWTH Aachen University, Aachen, Germany

4. Matthias Christian Hütten

   Maastricht University Medical Centre · Maastricht University

   Affiliation as printed

   Maastricht University Medical Centre

   MosaKids Pediatric Hospital, Maastricht University Medical Center, Maastricht, Netherlands

5. Thorsten W Orlikowsky Aachen

   Maastricht University Medical Centre · Universitätsklinikum Aachen

   Affiliation as printed

   Universitätsklinikum Aachen

   Maastricht University Medical Centre

6. André Stollenwerk Aachen Informatik 11 – Embedded Software

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

   Informatik 11 -Embedded Software, RWTH Aachen University, Aachen, Germany

7. Mark Schoberer Aachen

   Maastricht University Medical Centre · Universitätsklinikum Aachen

   Affiliation as printed

   Universitätsklinikum Aachen

   Maastricht University Medical Centre