Evidence map›Paper›PMID 38188917›Full record

ArticleFrontiers in pediatrics2023

Association between mortality and critical events within 48 hours of transfer to the pediatric intensive care unit.

Huan Liang, Kyle A Carey, Priti Jani, Emily R Gilbert, Majid Afshar, L Nelson Sanchez-Pinto, Matthew M Churpek, Anoop Mayampurath

Abstract read
In one paragraph

Article in Frontiers in pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

4 citing papers in PubMed.

  1. Mortality risk prediction from dense PICU data from patients with suspected infection: data-derived physiological trajectories outperform expert assessments when temporal resolution is high.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2026
    Article
  2. Article
  3. Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2025
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Huan LiangDepartment of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States.
Kyle A CareyDepartment of Medicine, University of Chicago, Chicago, IL, United States.
Priti JaniDepartment of Pediatrics, University of Chicago, Chicago, IL, United States.
Emily R GilbertDepartment of Medicine, Loyola University Medical Center, Maywood, IL, United States.
Majid AfsharDepartment of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States.
L Nelson Sanchez-PintoDepartment of Pediatrics (Critical Care), Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, United States.
Matthew M ChurpekDepartment of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States.
Anoop MayampurathDepartment of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States.

Funding

Pediatric COVID-19 Severity Dashboard During Delta Variant CirculationR01HD105939 · NICHD · UNIVERSITY OF COLORADO DENVER · PI Tellen Bennett, Lazaro Nelson Sanchez-Pinto · 2021 to 2026
$4.6M
Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney InjuryR01DK126933 · NIDDK · UNIVERSITY OF CHICAGO · PI CHURPEK, MATTHEW MICHAEL, KOYNER, JAY L · 2021 to 2025
$3.4M
Using causal machine learning for personalized treatment recommendations in critically ill patientsR01HL157262 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI Matthew Michael Churpek · 2021 to 2026
$3.1M
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)R01GM123193 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI CHURPEK, MATTHEW MICHAEL · 2017 to 2021
$1.9M
Using Machine Learning to Predict Clinical Deterioration in Hospitalized ChildrenK01HL148390 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI MAYAMPURATH, ANOOP · 2019 to 2023
$738k
NHLBI NIH HHS K01 HL148390NHLBI NIH HHS R01 HL157262NICHD NIH HHS R01 HD105939NIDDK NIH HHS R01 DK126933NIGMS NIH HHS R01 GM123193
6 · The paper itself

Abstract

Introduction: Critical deterioration in hospitalized children, defined as ward to pediatric intensive care unit (PICU) transfer followed by mechanical ventilation (MV) or vasoactive infusion (VI) within 12 h, has been used as a primary metric to evaluate the effectiveness of clinical interventions or quality improvement initiatives. We explore the association between critical events (CEs), i.e., MV or VI events, within the first 48 h of PICU transfer from the ward or emergency department (ED) and in-hospital mortality. Methods: We conducted a retrospective study of a cohort of PICU transfers from the ward or the ED at two tertiary-care academic hospitals. We determined the association between mortality and occurrence of CEs within 48 h of PICU transfer after adjusting for age, gender, hospital, and prior comorbidities. Results: Experiencing a CE within 48 h of PICU transfer was associated with an increased risk of mortality [OR 12.40 (95% CI: 8.12-19.23, Discussion: We demonstrate that occurrence of a CE within 48 h of PICU transfer was associated with mortality after adjusting for confounders. Studies focusing on the impact of quality improvement efforts may benefit from using CEs within 48 h of PICU transfer as an additional evaluation metric, provided these events could have been influenced by the initiative.

Indexed as

critical eventselectronic health recordsintensive care unitmortalitypediatrics

Identifiers

PMID38188917
PMCPMC10768058

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.