Evidence map›Paper›PMID 40690757›Full record

ArticleJMIR research protocols2025

Improving the Predictive Accuracy of the National Early Warning Score 2: Protocol for Algorithm Refinement.

Chris Plummer, Cen Cong, Madison Milne-Ives, Lynsey Threlfall, Peta Le Roux, Edward Meinert

Abstract read
In one paragraph

Article in JMIR research protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
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

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

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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

1 citing paper in PubMed.

  1. 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

6 authors.

Chris PlummerDepartment of Cardiology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom.ORCID 0000-0003-4135-3008
Cen CongTranslational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.ORCID 0009-0007-8261-6480
Madison Milne-IvesTranslational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.ORCID 0000-0001-7628-882X
Lynsey ThrelfallNIHR Newcastle Biomedical Research Centre, Newcastle University, Newcastle upon Tyne, United Kingdom.ORCID 0009-0004-3331-8776
Peta Le RouxInformation Services, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom.ORCID 0009-0005-4664-2732
Edward MeinertTranslational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.ORCID 0000-0003-2484-3347

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe National Early Warning Score 2 (NEWS2) has been widely adopted for predicting patient deterioration in health care settings using routinely collected physiological observations. The use of NEWS2 has been shown to reduce in-hospital mortality, but it has limited accuracy in the prediction of clinically important outcomes, especially over longer time periods.

objectiveThis project aims to improve the predictive accuracy of the NEWS2 scoring system, particularly its accuracy over more than 24 hours and its predictive value in older patients and children. It will investigate whether using the currently collected data differently and the inclusion of additional data would result in an improved algorithm.

methodsThe study will use historical patient data from the Newcastle upon Tyne Hospitals NHS Foundation Trust, including observational data (eg, vital signs), BMI- related data, and other outcome-related variables (eg, mortality rates) to train and test an algorithm to predict the risk of key clinical outcomes, including mortality, intensive therapy unit admission, sepsis, and cardiac arrest, to demonstrate a proof of concept for a modified scoring system. The algorithm's performance will be assessed based on its accuracy, precision, F

resultsThe study is expected to start in April 2025. The findings are expected to be produced by the end of 2026 and will be disseminated at symposia, conferences, and in journal publications.

conclusionsThe refined NEWS2 algorithm will address limited accuracy in predicting clinical deterioration beyond 24 hours in the original system by incorporating additional variables. Improved accuracy in the early detection of deterioration can lead to timely interventions, potentially reducing mortality and adverse clinical events. The enhanced algorithm also has the potential to be integrated into existing clinical decision support systems to facilitate health care professionals' decision-making. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/70303.

Indexed as

AlgorithmsEarly Warning ScoreChildClinical DeteriorationHospital MortalityHumansPredictive Value of TestsROC Curveclinical deteriorationNational Early Warning Scoreperformanceproof-of-concept

Identifiers

PMID40690757
PMCPMC12322607

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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.