Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning

Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning

Background: High flow nasal cannula (HFNC) is commonly used as non-invasive respiratory support in critically ill children. There are limited data to inform consensus on optimal device parameters, determinants of successful patient response, and indications for escalation of support. Clinical scores...

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Bibliographic Details
Main Authors: Joshua A. Krachman, Jessica A. Patricoski, Christopher T. Le, Jina Park, Ruijing Zhang, Kirby D. Gong, Indranuj Gangan, Raimond L. Winslow, Joseph L. Greenstein, James Fackler, Anthony A. Sochet, Jules P. Bergmann
Format: article
Language:EN
Published: Frontiers Media S.A. 2021
Subjects:
high flow nasal cannula
flow rate escalation
pediatric critical care
non-response
machine learning
acute respiratory failure
Pediatrics
RJ1-570
Online Access:https://doaj.org/article/936a3e40619c4397af3d8200c68880d5
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https://doaj.org/article/936a3e40619c4397af3d8200c68880d5

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