Evidence map›Paper›PMID 42488327›Full record

ArticleCritical care research and practice2026

Investigating the Predictive Power of the National Early Warning Score in Predicting Outcomes Among Intensive Care Unit Patients.

Mahmoud Yousefifard, Madeeh Heydarzadeh Jazi, Maryam Esmaeili, Parvaneh Asgari

Abstract read
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Article in Critical care research and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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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

4 authors.

Mahmoud YousefifardPhysiology Research Center, Iran University of Medical Sciences, Tehran, Iran, iums.ac.ir.ORCID https://orcid.org/0000-0001-5181-4985
Madeeh Heydarzadeh JaziCritical Care Department, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran, tums.ac.ir.ORCID https://orcid.org/0009-0004-0717-7092
Maryam EsmaeiliNursing and Midwifery Care Research Center, Critical Care Department, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran, tums.ac.ir.ORCID https://orcid.org/0000-0002-4798-2270
Parvaneh AsgariNursing and Midwifery Care Research Center, Critical Care Department, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran, tums.ac.ir.ORCID https://orcid.org/0000-0003-0200-9984

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: The aim of this study is to determine the predictive power of the National Early Warning Score in identifying the outcomes of intensive care unit (ICU) patients. Methods: In this study, 300 patients hospitalized in the ICUs (medical and general) of two teaching hospitals affiliated with Tehran University of Medical Sciences were evaluated and followed up for 30 days between June 2023 and April 2024. The samples were selected by convenience sampling based on inclusion criteria. A checklist containing the components of early warning criteria introduced by Braden, SOFA and APACHE II, as well as outcomes of hospitalization in the ward, was used. The early warning criteria were assessed by the researcher at the time of admission, as well as 6 and 24 h after the admission of patients to ICU. The collected data were analysed by SPSS-27 and STATA-14 software using Kruskal-Wallis, Pearson and chi-square tests, as well as the area under the receiver operating characteristic (ROC) curve of early warning system. Results: The mean age of patients was 41 years (31-56), and 63.70% of them were male. Comparison of the area under the ROC curve of early warning tool showed that the early warning score in predicting death at the time of admission, 6 h after admission and 24 h after admission was 0.697, 0.712 and 0.87, respectively. Also, this score in predicting adverse outcomes at the time of admission, 6 h after admission and 24 h after admission was 0.700, 0.784 and 0.936, respectively. In other words, the early warning system had a good predictive power in indicating the occurrence of outcomes at all three times (at admission, 6 hours after admission and 24 h after admission), but the best time to predict death and adverse outcomes was estimated to be 24 h after admission. Conclusion: By evaluating the predictive power of early warning score in identifying the outcomes of patients hospitalized in ICUs, we found that the early warning score at 24 h after admission had a higher predictive power than other times.

Indexed as

deathearly warning systemintensive care unitvital signs

Identifiers

PMID42488327
PMCPMC13389633

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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.