P67 Detecting Preload Reduction with Machine Learning on Arterial Waveform Parameters

DOI

10.2991/artres.k.191224.098How to use a DOI?

Abstract

Background: Hemodynamic optimization of unstable patients by means of fluid resuscitation improves patient outcome, but choosing the correct amount of fluid can be difficult. Too little fluid may not ensure adequate perfusion whereas too much fluid is associated with increased mortality. Static parameters are not sufficiently sensitive to detect a reduction in preload, and dynamic parameters rely on changes induced by mechanical ventilation. We hypothesized that the arterial wave form contains parameters that can be used as model input to identify patients that benefit from fluid administration.

Methods: Radial artery waveform parameters were extracted in patients after they had undergone a coronary artery bypass graft surgery (n = 20, all male). Three classes were defined: unchanged preload, preload reduction induced by positive end-expiratory breath holds (PEEP), and preload increase following fluid administration. A leave-one-out multinomial logistic regression was performed to train and evaluate the model. Model performance is reported as accuracy, sensitivity and specificity.

Results: In univariate analysis, left ventricular ejection time, augmentation index, dPdtmax and stroke volume showed the largest variation between the classes and were selected as model inputs. Following leave-one-out cross-validation the final model detected decreased preload with an accuracy, sensitivity and specificity of 87.5%, 85% and 90% respectively. Fluid administration did not give enough stimulus for modelling.

Conclusion: Arterial waveform parameters adequately distinguish unchanged from artificially reduced preload; preload increase could not be reliably detected. Since PEEP influences arterial compliance, future studies need to evaluate this effect, and also the applicability of the model in other populations.

Copyright

© 2019 Association for Research into Arterial Structure and Physiology. Publishing services by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

TY  - JOUR
AU  - Björn van der Ster
AU  - Marije Wijnberge
AU  - Marthe Huntelaar
AU  - Job de Haan
AU  - Koen van der Sluijs
AU  - Denise Veelo
AU  - Berend Westerhof
PY  - 2020
DA  - 2020/02/17
TI  - P67 Detecting Preload Reduction with Machine Learning on Arterial Waveform Parameters
JO  - Artery Research
SP  - S111
EP  - S111
VL  - 25
IS  - Supplement 1
SN  - 1876-4401
UR  - https://doi.org/10.2991/artres.k.191224.098
DO  - 10.2991/artres.k.191224.098
ID  - vanderSter2020
ER  -