NDLI: P67 Detecting Preload Reduction with Machine Learning on Arterial Waveform Parameters

Content Provider	Springer Nature : BioMed Central
Author	van der Ster, Björn Wijnberge, Marije Huntelaar, Marthe de Haan, Job van der Sluijs, Koen Veelo, Denise Westerhof, Berend
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.
Related Links	https://arteryresearch.biomedcentral.com/counter/pdf/10.2991/artres.k.191224.098.pdf
Ending Page	S111
Page Count	1
Starting Page	S111
File Format	HTM / HTML
ISSN	18764401
DOI	10.2991/artres.k.191224.098
Journal	Artery Research
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2020-02-17
Access Restriction	Open
Subject Keyword	Medicine Public Health Medicine/Public Health
Content Type	Text
Resource Type	Synopsis
Subject	Physiology (medical) Physiology
Journal Impact Factor	0.9/2023
5-Year Journal Impact Factor	0.7/2023

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