Evidence map›Paper›PMID 29169912›Full record

ArticleResuscitation2018

Comparison of the Between the Flags calling criteria to the MEWS, NEWS and the electronic Cardiac Arrest Risk Triage (eCART) score for the identification of deteriorating ward patients.

Malcolm Green, Harvey Lander, Ashley Snyder, Paul Hudson, Matthew Churpek, Dana Edelson

Abstract readComparative StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Resuscitation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed, 5 pooled it
–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

66 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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6 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Malcolm GreenClinical Excellence Commission, Level 17 McKell Building, 2-24 Rawson Place, Sydney 2000, New South Wales, Australia. Electronic address: malcolm.green1@health.nsw.gov.au.
Harvey LanderClinical Excellence Commission, Level 17 McKell Building, 2-24 Rawson Place, Sydney 2000, New South Wales, Australia.
Ashley SnyderDepartment of Medicine, University of Chicago, 5841 South Maryland Avenue, MC 6076, Chicago, 60637, IL, United States.
Paul HudsonClinical Excellence Commission, Level 17 McKell Building, 2-24 Rawson Place, Sydney 2000, New South Wales, Australia.
Matthew ChurpekDepartment of Medicine, University of Chicago, 5841 South Maryland Avenue, MC 6076, Chicago, 60637, IL, United States.
Dana EdelsonDepartment of Medicine, University of Chicago, 5841 South Maryland Avenue, MC 6076, Chicago, 60637, IL, United States.

Funding

Re-Engineering Translational Research at the University of ChicagoUL1TR000430 · NCATS · UNIVERSITY OF CHICAGO · PI SOLWAY, JULIAN · 2012 to 2016
$20.2M
Predicting In-hospital Cardiac Arrest Using Electronic Health Record DataK08HL121080 · NHLBI · UNIVERSITY OF CHICAGO · PI CHURPEK, MATTHEW MICHAEL · 2014 to 2018
$751k
NCATS NIH HHS UL1 TR000430NHLBI NIH HHS K08 HL121080
6 · The paper itself

Abstract

introductionTraditionally, paper based observation charts have been used to identify deteriorating patients, with emerging recent electronic medical records allowing electronic algorithms to risk stratify and help direct the response to deterioration. OBJECTIVE(S): We sought to compare the Between the Flags (BTF) calling criteria to the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS) and electronic Cardiac Arrest Risk Triage (eCART) score. DESIGN AND

participantsMulticenter retrospective analysis of electronic health record data from all patients admitted to five US hospitals from November 2008-August 2013.

main outcome measuresCardiac arrest, ICU transfer or death within 24h of a score

resultsOverall accuracy was highest for eCART, with an AUC of 0.801 (95% CI 0.799-0.802), followed by NEWS, MEWS and BTF respectively (0.718 [0.716-0.720]; 0.698 [0.696-0.700]; 0.663 [0.661-0.664]). BTF criteria had a high risk (Red Zone) specificity of 95.0% and a moderate risk (Yellow Zone) specificity of 27.5%, which corresponded to MEWS thresholds of >=4 and >=2, NEWS thresholds of >=5 and >=2, and eCART thresholds of >=12 and >=4, respectively. At those thresholds, eCART caught 22 more adverse events per 10,000 patients than BTF using the moderate risk criteria and 13 more using high risk criteria, while MEWS and NEWS identified the same or fewer. CONCLUSION(S): An electronically generated eCART score was more accurate than commonly used paper based observation tools for predicting the composite outcome of in-hospital cardiac arrest, ICU transfer and death within 24h of observation. The outcomes of this analysis lend weight for a move towards an algorithm based electronic risk identification tool for deteriorating patients to ensure earlier detection and prevent adverse events in the hospital.

Indexed as

Clinical DeteriorationHospital MortalityOrgan Dysfunction ScoresAdultAgedAged, 80 and overAlgorithmsArea Under CurveEarly DiagnosisElectronic Health RecordsHeart ArrestHumansMiddle AgedPatient TransferRetrospective StudiesRisk AssessmentDecision supportDeteriorating patientsEarly warning scoresMEWSNEWSRapid response systems

Identifiers

PMID29169912
PMCPMC6556215

What OpenQuestion holds

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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.