The prediction of extubation failure after surgery in pediatric patients with congenital heart disease

Master thesis (2021)

Authors

B.S. de Jong Mechanical Engineering

Contributors

Joppe Nijman Wilhelmina Children's Hospital (supervisor 1)
J.H. Krijthe Electrical Engineering, Mathematics and Computer Science (supervisor 1)
Faculty
Mechanical Engineering
Copyright: © 2021 Berend de Jong
Machine learning Extubation failure PICU
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Published Date

27-07-2021

Language

English

Reuse Rights

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Faculty

Mechanical Engineering

Abstract

Background and aims
The timing of extubation is a difficult decision for the medical team on the PICU. With negative impact on patient outcome when extubating too late or too early. The aim of this study was to create machine learning models for extubation failure prediction after surgery in patients with congenital heart disease. The goal was to assess the influence of time variant features on the performance.

Methods
Data from post cardiac surgery patients admitted to the PICU of the University Medical Centre Utrecht, The Netherlands, between 2009 and 2018 was collected. Ventilator and monitor parameters were extracted in 12-hour segments. Different representations of time-variant features were calculated (per hour/ per 12-hour segment), these representations were tested against machine learning trained on only time-invariant features (age, weight diagnosis). Machine learning algorithms tested were: long short-term memory network (LSTM), logistic regression and random forest model. Models were evaluated by comparing the areas under the receiver operator curves

Results
With only time invariant features a performance of 75% [95%CI 81%-90%] using logistic regression. Adding the time-variant features to a LSTM model a performance was reached 77% [95%CI 80%-90%]. Important features from the logistic regression models were age, weight, heart rate and respiratory rate.

Conclusions
Based on the overall results we concluded that the chosen representations of time variant features did not significantly improve the performance of the models. To improve performance and implementation of machine learning models in the future, transparent and externally validated models need to be developed.