The Role of Baroreflex Sensitivity in Acute Hypotensive Episodes Prediction in the Intensive Care Unit
Name
AngelottiIEEE2018.pdf
Description
Accepted version
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188.85 KB
Format
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Checksum (MD5)
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Author(s)
Angelotti, G.
Morandini, P.
Mark, Roger G
Barbieri, R.
Lehman, Li-Wei
Date Issued
October 2018
Journal
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Angelloti, G. et al. "The Role of Baroreflex Sensitivity in Acute Hypotensive Episodes Prediction in the Intensive Care Unit." 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 2018, Honolulu, Hawaii, USA, Institute of Electrical and Electronics Engineers (IEEE), October 2018 © 2018 IEEE
Version
Author's final manuscript
Abstract
A life threatening condition in Intensive Care Unit (ICU) is the Acute Hypotensive Episode (AHE). Patients experiencing an AHE may suffer from irreversible organ damage associated with increased mortality. Predicting the onset of AHE could be of pivotal importance to establish appropriate and timely interventions. We propose a method that, using waveforms widely acquired in ICU, like Arterial Blood Pressure (ABP) and Electrocardiogram (ECG), will extract features relative to the cardiac system to predict whether or not a patient will experience a hypotensive episode. Specifically, we want to assess if there are hidden patterns in the dynamics of baroreflex able to improve the prediction of AHEs. We will investigate the predictive power of features related to the baroreflex by performing classifications with and without them. Results are obtained using 17 classifiers belonging to different model families: classification trees, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs) replicated with different set of hyper-parameters and logistic regression. On average, the use of baroreflex features in the AHE prediction process increases the Area Under the Curve (AUC) by 10%.
MIT Department
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/embc.2018.8512859
