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Dublin Core
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Title
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November 2022 List
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November 2022 List
URL Address
<a href="http://doi.org/10.1097/cce.0000000000000764" target="_blank" rel="noreferrer noopener"> http://doi.org/10.1097/cce.0000000000000764</a>
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Predicting Time to Death After Withdrawal of Life-Sustaining Treatment in Children
Publisher
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Critical Care Explorations
Date
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2022
Subject
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Decision Support Techniques; Intensive Care Unit; Machine Learning; Pediatric; Terminal Care; Tissue and Organ Procurement
Creator
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Winter MC; Ledbetter DR
Description
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Accurately predicting time to death after withdrawal of life-sustaining treatment is valuable for family counseling and for identifying candidates for organ donation after cardiac death. This topic has been well studied in adults, but literature is scant in pediatrics. The purpose of this report is to assess the performance and clinical utility of the available tools for predicting time to death after treatment withdrawal in children. DATA SOURCES: Terms related to predicting time to death after treatment withdrawal were searched in PubMed and Embase from 1993 to November 2021. STUDY SELECTION: Studies endeavoring to predict time to death or describe factors related to time to death were included. Articles focusing on perceptions or practices of treatment withdrawal were excluded. DATA EXTRACTION: Titles, abstracts, and full text of articles were screened to determine eligibility. Data extraction was performed manually. Two-by-two tables were reconstructed with available data from each article to compare performance metrics head to head. DATA SYNTHESIS: Three hundred eighteen citations were identified from the initial search, resulting in 22 studies that were retained for full-text review. Among the pediatric studies, predictive models were developed using multiple logistic regression, Cox proportional hazards, and an advanced machine learning algorithm. In each of the original model derivation studies, the models demonstrated a classification accuracy ranging from 75% to 91% and positive predictive value ranging from 0.76 to 0.93. CONCLUSIONS: There are few tools to predict time to death after withdrawal of life-sustaining treatment in children. They are limited by small numbers and incomplete validation. Future work includes utilization of advanced machine learning models.
Identifier
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<a href="http://doi.org/10.1097/cce.0000000000000764" target="_blank" rel="noreferrer noopener">10.1097/cce.0000000000000764</a>
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Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
Terminal Care
2022
Critical Care Explorations
Decision Support Techniques
Intensive Care Unit
Ledbetter DR
machine learning
November 2022 List
Pediatric
Tissue and Organ Procurement
Winter MC
-
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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June 2021 List
Text
A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.
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June 2021 List
URL Address
<a href="http://doi.org/10.1097/PCC.0000000000002612" target="_blank" rel="noreferrer noopener">http://doi.org/10.1097/PCC.0000000000002612</a>
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Machine learning to predict cardiac death within 1 hour after terminal extubation
Publisher
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Pediatric Critical Care Medicine
Date
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2021
Subject
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artificial; data science; intensive care units; machine learning; palliative care; pediatric; respiration; terminal care
Creator
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Winter MC; Day TE; Ledbetter DR; Aczon MD; Newth CJL; Wetzel RC; Ross PA
Description
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Objectives: Accurate prediction of time to death after withdrawal of life-sustaining therapies may improve counseling for families and help identify candidates for organ donation after cardiac death. The study objectives were to: 1) train a long short-term memory model to predict cardiac death within 1 hour after terminal extubation, 2) calculate the positive predictive value of the model and the number needed to alert among potential organ donors, and 3) examine associations between time to cardiac death and the patient's characteristics and physiologic variables using Cox regression. Design(s): Retrospective cohort study. Setting(s): PICU and cardiothoracic ICU in a tertiary-care academic children's hospital. Patient(s): Patients 0-21 years old who died after terminal extubation from 2011 to 2018 (n = 237). Intervention(s): None. Measurements and Main Results: The median time to death for the cohort was 0.3 hours after terminal extubation (interquartile range, 0.16-1.6 hr); 70% of patients died within 1 hour. The long short-term memory model had an area under the receiver operating characteristic curve of 0.85 and a positive predictive value of 0.81 at a sensitivity of 94% when predicting death within 1 hour of terminal extubation. About 39% of patients who died within 1 hour met organ procurement and transplantation network criteria for liver and kidney donors. The long short-term memory identified 93% of potential organ donors with a number needed to alert of 1.08, meaning that 13 of 14 prepared operating rooms would have yielded a viable organ. A Cox proportional hazard model identified independent predictors of shorter time to death including low Glasgow Coma Score, high Pao<inf>2</inf>-to-Fio<inf>2</inf>ratio, low-pulse oximetry, and low serum bicarbonate. Conclusion(s): Our long short-term memory model accurately predicted whether a child will die within 1 hour of terminal extubation and may improve counseling for families. Our model can identify potential candidates for donation after cardiac death while minimizing unnecessarily prepared operating rooms. Copyright © 2021 Lippincott Williams and Wilkins. All rights reserved.
Identifier
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<a href="http://doi.org/10.1097/PCC.0000000000002612" target="_blank" rel="noreferrer noopener">10.1097/PCC.0000000000002612</a>
Rights
Information about rights held in and over the resource
Article information provided for research and reference use only. PedPalASCNET does not hold any rights over the resource listed here. All rights are retained by the journal listed under publisher and/or the creator(s).
2021
Aczon MD
Artificial
data science
Day TE
Intensive Care Units
June 2021 List
Ledbetter DR
machine learning
Newth CJL
Palliative Care
Pediatric
Pediatric Critical Care Medicine
Respiration
Ross PA
Terminal Care
Wetzel RC
Winter MC