Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data

Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data

Abstract Fluid bolus therapy (FBT) is fundamental to the management of circulatory shock in critical care but balancing the benefits and toxicities of FBT has proven challenging in individual patients. Improved predictors of the hemodynamic response to a fluid bolus, commonly referred to as a fluid...

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Bibliographic Details
Main Authors: Chitrabhanu B. Gupta, Debraj Basu, Timothy K. Williams, Lucas P. Neff, Michael A. Johnson, Nathan T. Patel, Aravindh S. Ganapathy, Magan R. Lane, Fatemeh Radaei, Chen-Nee Chuah, Jason Y. Adams
Format: Article
Language:English
Published: Nature Portfolio 2024-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-50120-5

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https://doi.org/10.1038/s41598-023-50120-5

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