2022
DOI: 10.1007/s00246-022-03084-8
|View full text |Cite
|
Sign up to set email alerts
|

Descriptors of Failed Extubation in Norwood Patients Using Physiologic Data Streaming

Abstract: Objective: To evaluate the utility of high-frequency physiologic data during the extubation process and other clinical variables for describing the physiologic pro le of extubation failure in neonates with hypoplastic left heart syndrome (HLHS) post-Norwood procedure.Methods: Single-center, retrospective analysis. Extubation events were collected from January 2016 until July 2021. Extubation failure was de ned as the need for re-intubation within 48 hours of extubation. The data included streaming heart rate, … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
3
2

Relationship

2
3

Authors

Journals

citations
Cited by 5 publications
(3 citation statements)
references
References 33 publications
0
3
0
Order By: Relevance
“…Increased oxygen extraction has been demonstrated to increased morbidity and mortality, with oxygen extraction of 30 to 40 being demonstrated as a period at which the risk of morbidity such as impaired neurodevelopment, acute kidney injury, hepatic insu ciency, necrotizing enterocolitis, and cardiac arrest increase [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] .…”
Section: Discussionmentioning
confidence: 99%
“…Increased oxygen extraction has been demonstrated to increased morbidity and mortality, with oxygen extraction of 30 to 40 being demonstrated as a period at which the risk of morbidity such as impaired neurodevelopment, acute kidney injury, hepatic insu ciency, necrotizing enterocolitis, and cardiac arrest increase [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] .…”
Section: Discussionmentioning
confidence: 99%
“…Although findings suggest the limited ability of traditional parameters in isolation to predict the likelihood of extubation success, machine learning could provide the clinician a means with which to synthesize the multitude of available variables to guide extubation decision-making. Our group and others have explored machine learning and evaluation of information available through physiologic data streaming as means by which better evaluate candidacy for extubation (21)(22)(23). Future directions may include the extraction of more granular data and computer modeling to create and validate useful models of extubation readiness.…”
Section: At the Bedsidementioning
confidence: 99%
“…This leads to higher morbidity and mortality, as well [2][3][4]. Management of these patients thus requires thorough understanding of the principles that underpin this circulation, how to adequately monitor these patients, as well as the impacts of various clinical interventions in this circulation [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. The role of central venous pressures and what factors are associated with central venous pressure in patients with parallel circulation have not been well described.…”
Section: Introductionmentioning
confidence: 99%