Evidence map›Paper›PMID 39405061›Full record

ArticleJAMA network open2024

Early Warning Scores With and Without Artificial Intelligence.

Dana P Edelson, Matthew M Churpek, Kyle A Carey, Zhenqui Lin, Chenxi Huang, Jonathan M Siner, Jennifer Johnson, Harlan M Krumholz, Deborah J Rhodes

Erratum issuedAbstract read
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 47 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 4 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

47 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Dana P EdelsonSection of Hospital Medicine, University of Chicago, Chicago, Illinois.
Matthew M ChurpekSection of Pulmonary and Critical Care Medicine, University of Wisconsin School of Medicine and Public Health, Madison.
Kyle A CareySection of Hospital Medicine, University of Chicago, Chicago, Illinois.
Zhenqui LinSection of Cardiovascular Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut.
Chenxi HuangSection of Cardiovascular Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut.
Jonathan M SinerSection of Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut.
Jennifer JohnsonCare Signature, Yale New Haven Health, New Haven, Connecticut.
Harlan M KrumholzSection of Cardiovascular Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut.
Deborah J RhodesSection of General Internal Medicine, Yale School of Medicine, New Haven, Connecticut.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
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
NCATS NIH HHS UL1 TR001863NHLBI NIH HHS R01 HL157262
6 · The paper itself

Abstract

Importance: Early warning decision support tools to identify clinical deterioration in the hospital are widely used, but there is little information on their comparative performance. Objective: To compare 3 proprietary artificial intelligence (AI) early warning scores and 3 publicly available simple aggregated weighted scores. Design, Setting, and Participants: This retrospective cohort study was performed at 7 hospitals in the Yale New Haven Health System. All consecutive adult medical-surgical ward hospital encounters between March 9, 2019, and November 9, 2023, were included. Exposures: Simultaneous Epic Deterioration Index (EDI), Rothman Index (RI), eCARTv5 (eCART), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and NEWS2 scores. Main Outcomes and Measures: Clinical deterioration, defined as a transfer from ward to intensive care unit or death within 24 hours of an observation. Results: Of the 362 926 patient encounters (median patient age, 64 [IQR, 47-77] years; 200 642 [55.3%] female), 16 693 (4.6%) experienced a clinical deterioration event. eCART had the highest area under the receiver operating characteristic curve at 0.895 (95% CI, 0.891-0.900), followed by NEWS2 at 0.831 (95% CI, 0.826-0.836), NEWS at 0.829 (95% CI, 0.824-0.835), RI at 0.828 (95% CI, 0.823-0.834), EDI at 0.808 (95% CI, 0.802-0.812), and MEWS at 0.757 (95% CI, 0.750-0.764). After matching scores at the moderate-risk sensitivity level for a NEWS score of 5, overall positive predictive values (PPVs) ranged from a low of 6.3% (95% CI, 6.1%-6.4%) for an EDI score of 41 to a high of 17.3% (95% CI, 16.9%-17.8%) for an eCART score of 94. Matching scores at the high-risk specificity of a NEWS score of 7 yielded overall PPVs ranging from a low of 14.5% (95% CI, 14.0%-15.2%) for an EDI score of 54 to a high of 23.3% (95% CI, 22.7%-24.2%) for an eCART score of 97. The moderate-risk thresholds provided a median of at least 20 hours of lead time for all the scores. Median lead time at the high-risk threshold was 11 (IQR, 0-69) hours for eCART, 8 (IQR, 0-63) hours for NEWS, 6 (IQR, 0-62) hours for NEWS2, 5 (IQR, 0-56) hours for MEWS, 1 (IQR, 0-39) hour for EDI, and 0 (IQR, 0-42) hours for RI. Conclusions and Relevance: In this cohort study of inpatient encounters, eCART outperformed the other AI and non-AI scores, identifying more deteriorating patients with fewer false alarms and sufficient time to intervene. NEWS, a non-AI, publicly available early warning score, significantly outperformed EDI. Given the wide variation in accuracy, additional transparency and oversight of early warning tools may be warranted.

Indexed as

Artificial IntelligenceEarly Warning ScoreAgedClinical DeteriorationFemaleHospital MortalityHumansIntensive Care UnitsMaleMiddle AgedRetrospective StudiesROC Curve

Identifiers

PMID39405061
PMCPMC11544488

What OpenQuestion holds

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Registered trials

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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.