Evidence map›Paper›PMID 32345932›Full record

ArticlePediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies2020

Prediction of Pediatric Critical Care Resource Utilization for Disaster Triage.

Elizabeth Y Killien, Brianna Mills, Nicole A Errett, Vicki Sakata, Monica S Vavilala, Frederick P Rivara, Niranjan Kissoon, Mary A King

Abstract read
In one paragraph

Article in Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Finding the Right Ethical Framework for PICU Resource Allocation During a Pandemic.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2020
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Elizabeth Y KillienDivision of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington, Seattle, WA.
Brianna MillsHarborview Injury Prevention and Research Center, University of Washington, Seattle, WA.
Nicole A ErrettDepartment of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA.
Vicki SakataNorthwest Healthcare Response Network, Tukwila, WA.
Monica S VavilalaHarborview Injury Prevention and Research Center, University of Washington, Seattle, WA.
Frederick P RivaraHarborview Injury Prevention and Research Center, University of Washington, Seattle, WA.
Niranjan KissoonDepartment of Pediatrics, BC Children's Hospital, Vancouver, BC, Canada.
Mary A KingDivision of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington, Seattle, WA.

Funding

XENOBIOTIC BIOTRANSFORMATION AND DISPOSITIONP30ES007033 · NIEHS · UNIVERSITY OF WASHINGTON · PI ALI SHOJAIE · 1995 to 2026
$42.5M
Pediatric Injury Research Training ProgramT32HD057822 · NICHD · UNIVERSITY OF WASHINGTON · PI CAROLYN A MC CARTY, Ali Rowhani-Rahbar · 2010 to 2026
$4.0M
NICHD NIH HHS T32 HD057822NIEHS NIH HHS P30 ES007033
6 · The paper itself

Abstract

objectivesPediatric protocols to guide allocation of limited resources during a disaster lack data to validate their use. The 2011 Pediatric Emergency Mass Critical Care Task Force recommended that expected duration of critical care be incorporated into resource allocation algorithms. We aimed to determine whether currently available pediatric illness severity scores can predict duration of critical care resource use.

designRetrospective cohort study.

settingSeattle Children's Hospital. PATIENTS: PICU patients admitted 2016-2018 for greater than or equal to 12 hours (n = 3,206).

interventionsNone. MEASUREMENTS AND MAIN

resultsWe developed logistic and linear regression models in two-thirds of the cohort to predict need for and duration of PICU resources based on Pediatric Risk of Mortality-III, Pediatric Index of Mortality-3, and serial Pediatric Logistic Organ Dysfunction-2 scores. We tested the predictive accuracy of the models with the highest area under the receiver operating characteristic curve (need for each resource) and R (duration of use) in a validation cohort of the remaining one of three of the sample and among patients admitted during one-third of the sample and among patients admitted during surges of respiratory illness. Pediatric Logistic Organ Dysfunction score calculated 12 hours postadmission had higher predictive accuracy than either Pediatric Risk of Mortality or Pediatric Index of Mortality scores. Models incorporating 12-hour Pediatric Logistic Organ Dysfunction score, age, Pediatric Overall Performance Category, Pediatric Cerebral Performance Category, chronic mechanical ventilation, and postoperative status had an area under the receiver operating characteristic curve = 0.8831 for need for any PICU resource (positive predictive value 80.2%, negative predictive value 85.9%) and area under the receiver operating characteristic curve = 0.9157 for mechanical ventilation (positive predictive value 85.7%, negative predictive value 89.2%) within 7 days of admission. Models accurately predicted greater than or equal to 24 hours of any resource use for 78.9% of patients and greater than or equal to 24 hours of ventilation for 83.1%. Model fit and accuracy improved for prediction of resource use within 3 days of admission, and was lower for noninvasive positive pressure ventilation, vasoactive infusions, continuous renal replacement therapy, extracorporeal membrane oxygenation, and length of stay.

conclusionsA model incorporating 12-hour Pediatric Logistic Organ Dysfunction score performed well in estimating how long patients may require PICU resources, especially mechanical ventilation. A pediatric disaster triage algorithm that includes both likelihood for survival and for requiring critical care resources could minimize subjectivity in resource allocation decision-making.

Indexed as

Intensive Care Units, PediatricTriageChildCritical CareHospital MortalityHumansInfantRetrospective Studies

Identifiers

PMID32345932
PMCPMC7199765

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.